# Scientific mobility, prestige and skill alignment in academic institutions

Márcia R. Ferreira  R. Costas  V.D.P. Servedio  Complexity Science Hub Vienna,
Josefstädter Strasse 39, A-1080 Vienna, Austria  Stefan Thurner

###### Abstract

Scientific institutions play a crucial role in driving intellectual, social,
and technological progress. Their capacity to innovate depends mainly on their
ability to attract, retain, and nurture scientific talent and ultimately make
it available to other organizations, industries, or the economy. As
researchers change institutions during their careers, their skills are also
transferred. The extent and mechanisms by which academic institutions manage
their internal portfolio of scientific skills by attracting and sending
researchers are far from being understood. We examine 25 million publication
histories of 9.2 million scientists extracted from a large-scale bibliographic
database covering thousands of research institutions worldwide to understand
how the skills of mobile scientists align with those present in-house. We find
a clear association between top-ranked institutions and greater skill
alignment, i.e., the degree to which skills of incoming academics match those
of their colleagues at the institution. We uncover similar high-alignment for
scientists leaving top-ranked institutions. This type of academic alignment is
more pronounced in engineering and life, health, earth, and physical sciences
than in mathematics, computer science, social sciences, and the humanities. We
show that over the past two decades, institutions generally have become more
closely aligned in their overall skill profiles. We interpret these results in
terms of levels of proactive management of the composition of the scientific
workforce, diversity, and internal collaboration strategies at the
institutional level.

## Introduction

Scientific discovery requires the capacity to seek, nurture, and combine
internal and external sources of knowledge. Universities, in particular, serve
as vital “containers” for the advancement and integration of this knowledge.
However, because of the “tacit” nature of knowledge [1], knowledge synergies
do not emerge automatically. They rely on transfer mechanisms such as
collaboration, networks, and labor mobility [2, 3, 1, 4]. Academic mobility is
a particularly important mechanism for knowledge to flow effectively across
people, organizations, locations, and time [2, 5, 6].

Scientists are moving between different institutions with increasing frequency
[7]. According to some estimates, in 1990, about 2% of scientists worked
outside their country of origin [5]. By 2000, this proportion increased to 14%
[5], and by 2015, it was estimated that about one-third of scientists were
working outside their country of origin [8]. A similar trend has been observed
in Europe, where it has been reported that 7% of hired researchers were from
abroad [9]. However, the presence of mobile researchers varies considerably by
region and institution [10, 9, 7]. At Cambridge University, for example, it
has been reported that more than 40% of the faculty were foreign-born [9].

Figure 1:  A graphical representation of the skill and workforce structures of
a scientific institution. We study three types of scientists (A, B, and C):
institutional newcomers $\nu$ (A), institutional natives $\sigma$ (B), and
institutional outgoers $\omega$ (C). Every individual has a skill vector;
every component in it, $S_{k}$, represents a particular skill $k$. We
aggregate the individual skill vectors for each population (A), (B) and (C) of
researchers and compute the cosine similarities between them, as shown in the
grey boxes in panels G and H. We denote these similarities by $\theta$ (G) and
$\eta$ (H). For illustration purposes, we took angles larger than $90\deg$.
For definitions of the measures of skill alignment, see Materials and Methods.

The attraction of mobile individuals to institutions has been studied for many
decades [11, 5, 6], and several analyses have shown that external talent is
essential for innovation [2, 5, 12, 7, 6]. Attracting individuals trained in
various research contexts is critical for frontier research [6, 5, 7, 13, 14,
15, 16], as it enables institutions to explore new areas of knowledge.
However, it is also a challenge faced by most research institutions worldwide
[13]. The exchange of talent is increasingly concentrated in a handful of
universities [12]. In the United States, for example, the most prestigious
institutions attracted and trained most of the available faculty before
sending them to other mid- and upper-level research institutions [17, 18].
Education systems also differ dramatically, with more prominent, well-funded
universities offering more facilities, funding opportunities, and research
diversity than smaller, specialized universities [13, 19, 20, 21]. This
unequal access to knowledge has significant implications for knowledge sharing
across academic institutions and, more importantly, within the organization
[22, 23].

It has been argued that most academic institutions pursue the overarching goal
of profile continuity and that the long-term sustainability of institutions
can only be achieved through various forms of alignment [24, 25]. Knowledge
institutions typically invest and strengthen knowledge in their established
research areas over time [26], as leveraging on existing competencies can
create alignments that improve performance, learning, and knowledge transfer
[27, 2]. When scientists within the institution have a common knowledge base,
they can better learn from each other [2], leading to improved productivity
and reduced barriers to collaboration. Yet, top institutions with large
endowments are those that can experiment more in new emerging scientific
fields.

An important driver of attracting talent that matches the internal profiles of
institutions is the current reward system of science [28]. This system often
and increasingly discourages the pursuit of novel research areas because the
returns from new ideas and topics are seen as uncertain, distant, and often
risky [24]. In contrast, the benefits of refining and expanding existing
expertise and technologies are positive, immediate, and predictable [25].
Other studies have suggested that too much similarity in knowledge can also
limit innovation [29], which at the institutional level means that as academic
organizations exceed optimal levels of alignment, they potentially ‘lock’ into
dominant thematic profiles.

The mobility of academic talent, collaboration, and the alignment of skill
profiles between institutions and incoming and outgoing researchers can play a
critical role in shaping research dynamics within and across institutions.
However, on a quantitative basis, there is limited understanding of the
processes behind the alignment of knowledge and skills within institutions and
the alignment of the skills of institutions and new hires. To gain a better
understanding of these alignment strategies, we study academic institutions
from the perspective of the composition of their workforce and the internal
skill profiles they generate as the composition of their workforce changes
over time.

We use the Dimensions database (see Materials and Methods) to compare the
internal skill profiles of millions of mobile individuals with the skill
structures of the institutions they move to or leave. We quantify the academic
skills of individuals by using their publications mapped into a high-
resolution classification scheme of scientific topics across all disciplines
[30]. This classification is the basis for defining the skill vector, $S^{j}$
for every individual, $j$. Every component of that binary vector represents a
skill of the researcher, if the k-th component is $S^{j}_{k}=1$, researcher
$j$ has competency $k$, if $S^{j}_{k}=0$, $j$ has no skill in $k$. If an
author publishes in many different research areas, they has many ‘skills’, if
they publish on only one specific topic, the author has only a single non-zero
component in the skill vector; see Materials and Methods. The skills of an
institution are defined as the superposition (sum of all vectors) of all the
members of the existing workforce. These aggregated vectors are indicated as
$S_{\Sigma}$, with a subscript $\Sigma$. The Dimensions database allows not
only to quantify skills but also to observe the flows of researchers around
the globe.

However, measuring scientific skill profiles by bibliographic means is not an
easy task. This partly depends on the level of resolution we use to determine
researchers’ skills. Data limitations have also been an obstacle to the study
of scientists’ knowledge pathways [31, 7, 10], leading to a prevalence of
findings from self-reported information, small-scale studies, or studies
limited to researchers from specific fields or countries [32, 33, 6, 5, 34,
35]. The situation is particularly problematic at the institutional level, as
it relies on clear institutional identification and robust author-name
disambiguation algorithms [36, 10]. This situation has changed recently as
more databases improve author and affiliation metadata [31, 7, 10]. In what
follows, we focus on harmonized research-intensive institutions data [37, 38]
for which extensive metadata on author-affiliation transitions exists [10].

In Fig. 1, we present a schematic view of how we approach the problem of skill
assignment. We define three types of researchers: Newcomers (A), Natives (B),
and Outgoers (C). The natives represent the non-mobile workforce at a given
institution, $i$. In the figure, we represent them as two scientists,
$\sigma_{1}$ and $\sigma_{2}$, (green), both of which have different skills
that are given by a skill vector, $S$, that has $n=4,163$ components that mark
the different individual categories in the science classification scheme.
Native scientist $\sigma_{1}$ has three skills $S_{1}$, $S_{2}$, and $S_{4}$,
hence $S^{\sigma_{1}}_{1}=S^{\sigma_{1}}_{4}=1$, whereas $\sigma_{2}$ has only
two, $S_{2}$ and $S_{3}$, $S^{\sigma_{2}}_{2}=S^{\sigma_{2}}_{3}=1$, all other
components being zero. Their combined skills are given by the sum of their
skill vectors indicated by the small arrows. The skills present at the
institution are seen in panel E. In this example, there are $r=42$ native
researchers present; their skills are collected in the table. The sum of all
their skills is called $S^{\sigma}_{\Sigma}$ and represents the current skill
vector of the institution, $i$. We now assume that in the next time period, a
set of researchers will join the institution (newcomers, $\nu$) (A), and some
will leave (the outgoers, $\omega$) (C). The collective skill vectors of these
groups are called $S^{\nu}_{\Sigma}$ and $S^{\omega}_{\Sigma}$, respectively.
In this example, we have 25 newcomers and 20 outgoers, with their skills
captured in the tables in D and F. Data shows that the native population and
newcomers make up the largest fraction in most institutions, while the
outgoing population makes up the smallest fraction.

With this notion, we can now quantify the _newcomer skill alignment_ between
the natives (plus outgoers) and the incoming workforce as the cosine of the
angle, $\theta$, between the incoming skill vector, $S^{\nu}_{\Sigma}$ and the
sum of natives and outgoers, $S^{\sigma}_{\Sigma}+S^{\omega}_{\Sigma}$; see
panel (G). With this measure, we can analyze whether internally trained
authors (natives and outgoers) and external authors (newcomers) generate
aligned or divergent skill profiles at the institutional level. Similarly, we
define the _outgoer skill alignment_ by calculating the cosine of the angle,
$\eta$, between the skill vector of outgoers, $S^{\omega}_{\Sigma}$ and the
combined skill vectors of natives and newcomers,
$S^{\sigma}_{\Sigma}+S^{\nu}_{\Sigma}$; see panel (H).

## Results

#### Skill Alignment in Research Institutions

Figure 2: Alignments of the skill profile of the native faculty and newcomers
to- and outgoers from all academic institutions. Panels A and B show the
distributions for newcomers, $\cos{\theta}$, and outgoers, $\cos{\eta}$,
respectively. The purple (panel A) and blue (panel B) distributions show the
alignment of skills between those newcomers and outgoers that were not
collaborating with their peers at the institution before they joined or during
their stay, respectively. The transparent lines represent a reference
distribution of skill alignments obtained by shuffling the target (source)
affiliations of newcomers (outgoers) ($n=10,000$ random assignments). Clearly,
skill similarity is absent in the shuffled data. Panels C, D, E, and F show
the influence of the institution’s reputation. The alignment is shown for the
quartiles within the top 1% most impactful institutions, PPtop1%. The more
impact, the more alignment, regardless of existing collaborations (compare C E
and D E). Panel G captures the influence of institution type. It gives the
median alignment scores by institution type. The shaded bars represent the
percentage and number of institutions by organization type in our sample.

To what extent does the skills profile of externally trained incoming
scientists match that of institutional natives? Do their skills align, or are
they different? Figures 2A and B show the cosine similarity between the skills
profile of the institutions and its newcomer and outgoing workforce. We find a
substantial similarity with a median of $0.84$ and $0.86$ for the newcomers
and outgoers, respectively. The fact that the skill alignment is slightly
lower for the newcomers than for the outgoers suggests that the outgoers have
become more similar in their skills while they stayed at the institutions.
Panels A (purple) and B (light blue) also show the similarity between the
existing workforce skills and the skills profile generated by those newcomers
and outgoers who did not interact with the rest of the institution’s workforce
during their stay. For these cases, we find much less similarity (median
$0.54$ and $0.61$). This indicates that internal collaboration is a potential
driver of intra-institutional skill alignment. The regression analysis shown
in SI text 2 confirms that collaboration within institutions is an important
predictor of intra-institutional skill alignment.

To illustrate that the observed alignments are a significant and genuine
effect that does not simply emerge as a statistical consequence of the
definition of the cosine-similarity measure, we devise a simple “null model”.
We preserve the skill profiles of the in- and outgoers but remove the
correlations with the profiles of the institutions. We do this by randomly
assigning newcomer and outgoer skill profiles to institutions. The
distributions are shown as transparent lines in Figures 2A and B. The skill
alignments practically vanish as a result.

There is a clear relation between institutional prestige, as captured by the
PPtop1% indicator (for definition, see Materials and Methods), and skill
alignment. Figures 2C and E show that institutions that have substantially
more than one percent of their publications in the top 1% most cited papers
worldwide tend to have similar skill profiles across the different types of
workforce. Newcomers who move to an institution with top-cited publications
tend to have more similar skill profiles than newcomers who move to a less
prestigious institution, see C. The situation is similar for departing
scientists, see E. In other words, talent flowing to and from organizations
with high institutional prestige is associated with greater skill alignment. A
greater skill dispersion is also observed at universities with lower prestige.
If we compare C with D and E with F, we see that the prestige effect is
independent of whether there are collaboration ties (i.e., co-authorship)
between newcomers and outgoers with local researchers.

Figure 3: Scatter plot of the alignments of newcomers, $\cos{\theta}$, and
outgoers, $\cos{\eta}$ (A). The line represents the diagonal (same in- and
outgoer alignment). The grey quadrant lines are positioned at the median
values of $\cos{\theta}$ ($\tilde{x}=0.84$) and $\cos{\eta}$
($\tilde{x}=0.86$). Every circle represents an institution. Color indicates
the scientific impact of the institutions (${PP}_{\mathrm{top1\%}}$
indicator). Grey institutions are below the global average
(${PP}_{\mathrm{top1\%}}\leq{0.01}$) of institutions with the same skills
profile and years of production as explained in Materials and Methods).
Orange, blue, and yellow circles represent institutions with medium
($0.01\leq{PP}_{\mathrm{top1\%}}\leq{0.05}$), high
($0.06\leq{PP}_{\mathrm{top1\%}}\leq{0.09}$), and very high impact
(${PP}_{\mathrm{top1\%}}\geq{0.10}$), respectively. Top institutions tend to
have generally high alignments and a slightly higher out-alignment. The
scatter plots in panels B and C show the relation between the number of skills
present at an institution (as a % of all skills in the sample) and the skill
alignments $\cos{\theta}$ (B) and $\cos{\eta}$ (C) for Education, Healthcare,
and Facility research institutions, respectively. Healthcare and Facilities
tend to have high in- and out-alignments; see also SI Figure 6.

Figure 2G shows the median alignment between newcomers and outgoers (colors
correspond to those in panels A and B) and institutional natives by
organization type. More generalist educational institutions (e.g.,
universities) tend to have lower median levels of similarity than more
thematically focused institutions such as Facilities, Archives, Companies,
Non-profits, or Governmental institutions. Interestingly, Healthcare research
institutions also show comparatively low median scores of alignment, which may
indicate that while they are considered specialized, their skill sets are
broad enough to encompass a greater diversity of skill profiles between in and
outgoing researchers and natives.

Figure 3A shows the alignment of newcomers (x-axis) versus the alignment of
outgoers (y-axis). The color indicates the degree of the citation’s impact of
institutions. The solid line marks the regression result. We segment the plot
into four quadrants (gray lines at the median alignment values) associated
with strategic patterns of talent attraction and training. Institutions (41%)
in the first quadrant (top right) attract and send the same skills at a rate
above the median. Most notably, the U.S. Ivy Leagues, top European
universities, and prominent molecular biology and cancer research institutes
are in the first quadrant. There we also find several university hospitals and
medical centers. This indicates that the institutions’ strategy in the first
quadrant is thematic continuity [24, 25] and homogeneity in their recruitment
and training practices.

In the third quadrant (bottom left), we observe the opposite trend for about
45% of institutions. Here, the profiles of newcomer hiring and outgoing
researchers within an institution diverge and fall below the overall median
scores. An example of this pattern is the Institute for Advanced Study (IAS)
at Princeton (blue circle). This institute has a remarkably low alignment
between outgoers and the rest of the institution, as well as between newcomers
and the rest of the institution. Historically, the IAS has been a place where
scientists retreat for sabbaticals and exchange ideas, encouraging unexpected
discoveries and interdisciplinary thinking. This suggests that it is not
always necessary to have a high-skill alignment of newcomers and outgoers to
conduct high-impact research. Our method captures their (non) alignment
strategy. However, IAS is an exception since this strategy seems prevalent at
most institutions whose citation performance is below or close to the global
average (grey).

The second quadrant (upper left) shows institutions (8%) that attract
researchers working in potentially complementary research areas. These
institutions send researchers who produce skill-aligned profiles above the
median of the other institutions to the institution’s facilities and attract
those who produce differentiated work below the median to the rest of the
institution. In the fourth quadrant (bottom right), we find the opposite
situation; about 6% of institutions bring more of the same skills and send
more researchers who exhibit different skills.

Figure 4: Skill alignment of newcomers (A) and outgoers (B) in five main
scientific fields as a function of institutional prestige as captured by
impact quartiles of PPtop1% (same figure setup as in Fig 2 C and E). Again,
top-cited institutions have higher newcomer and outgoer alignment than average
organizations (two-sample $t$ test, $P$ value <0.001). Disciplines are
arranged alphabetically from top to bottom. Social sciences, humanities,
mathematics, and computer science show lower levels of alignment than
biomedical and health sciences, life and earth sciences, physical sciences and
engineering. For the case controlled for collaborations, see SI Figure 7.

In Figures 3B and C show the number of skills present at an institution (in %
of all possible 4,163 skills in our classification, see see Materials and
Methods), versus the skill alignment at institutions, B newcomers, C outgoers.
We find considerable heterogeneity. Colors highlight three types of
organizations: Education, Healthcare, and Facility, which account for 93% of
the institutions in our sample. The education category includes general and
specialized universities, while the healthcare category includes university
hospitals and medical research centers. Facilities, typically established by
the government or academic stakeholders, often specialize in one particular
field, such as agriculture, high-energy physics, specific technologies, and
others. We see that research facilities tend to be more specialized (small
percentage of skills) and have higher skill alignments of both their incoming
(B) and outgoing (C) workforce. Educational institutions tend to have a larger
number of skills and show a large spread in both number of skills and
alignment. Healthcare institutions fall between the two regarding skill
diversity and show relatively high alignment values.

In SI text 2, we conduct a multivariate regression analysis that examines the
relationship between alignment and scientific impact measures and various
controls while considering the different sizes of institutions. Our findings
indicate that the level of internal collaboration within an institution and
citation impact are important factors in determining skill alignment within
academic institutions.

#### Skill Alignment in Different Science Fields

Various degrees of skill alignment are found in the five major areas of
science. Figure 4 shows a breakdown of the distribution of alignment scores
for newcomers (A) and outgoers (B). We find that the profiles for academics in
the social sciences, humanities, mathematics, and computer science show lower
levels of alignment. This is especially true for lower impact institutions, as
captured by the quartiles of the proportion of papers in the top one percent
(PPtop1% indicator). Finally, in the fields of biomedical and health sciences,
life and earth sciences, and physical sciences and engineering, there is a
comparatively slightly higher degree of similarity between the skills of
newcomers with their institution as well as between outgoers and the rest of
their institution.

#### Skill Alignment Between Academic Institutions Over Time

Finally, we present the situation of the alignment of skills between all the
institutions in the sample. We analyze the inter-institutional skill
alignment, i.e., the similarity of skill profiles of the entire workforce of
institutions between all institutions in the sample. The inter-institutional
skill alignment is defined as the skill profile similarity denoted by
$\cos{\phi}$ (see Materials and Methods for the definition). We compute it for
all pairs of institutions.

Figure 5A shows average inter-institutional skill alignment during four time
periods from 2000 to 2019. The red error bars mark standard errors of the
mean. We see that the average skill alignment is generally low, however, it
doubled in the past twenty years, i.e., across the globe, skill profiles of
institutions have become more similar.

Figure 5: Inter-institutional skill alignment over time. Panel A shows the
average of pairwise cosine similarity for all pairs of institutions for four
non-overlapping time periods: P1:2000-2004, P2:2005-2009, P3:2010-2014, and
P4:2015-2019. The red error bars represent standard errors. Panel B shows the
differences between the overall skill alignment of pairs of institutions,
$\Delta\cos{\phi}$, over time. $\Delta<0$ means that institutions become
increasingly similar in the composition of their overall skill profiles. The
sub-panels capture the changes between the time periods. The tendency of
becoming more similar is visible (over time, the peak is moving to the left).

Figure 5B shows the distribution of the differences in skill alignment between
the pairs of institutions over time. A value of $\Delta\cos{\phi}$ below zero
means that institutions have become more similar; when $\Delta\cos{\phi}>0$,
institutions become more dissimilar. The dashed red line indicates the zero
line of the x-axis. The three sub-panels capture the changes between the time
periods, P1-P2, P2-P3, and P3-P4. It is visible that the peak of the
distribution is moving toward the leftover time, indicating an acceleration
toward becoming more similar in more recent years.

## Discussion

By quantifying the alignment of skills present at $3,965$ institutions, which
includes the publication records of $9,299,250$ disambiguated authors
affiliated with $108$ countries, with the $4,163$ skill types of the incoming
and outgoing workforce, we can show strong quantitative signatures of academic
skill alignment – the degree to which mobile scholars (newcomers or outgoers)
publish on topics that are in line with those of their colleagues already at
the new institution. In particular, newcomers tend to publish on topics that
align with those of their colleagues already at the new institution.
Alignment, as measured by skills profile similarity, is more pronounced at the
most prestigious (i.e., top-cited) institutions than at average institutions.
Even within the top 1% of highly cited institutions, there is a correlation
between skill alignment and institutional citation performance. Research
institutions with moderate levels of citation impact tend to have
significantly less aligned skill profiles between natives and in- and
outgoers. The greater alignment of skill profiles at top institutions is not
surprising, as it indicates a strategic, specific, and targeted hiring policy
that may not be present at more moderate institutions.

Highly aligned skill profiles potentially realize synergies between newcomers
and existing faculty [27, 2] and can reinforce already strong research
portfolios. However, this also may lead to selection pressures for those hired
and the hiring institutions themselves and eventually lead to the under-
representation of relevant research expertise, and important topics [39].

Two likely mechanisms could explain the origin of the observed similarities.
One is that the newly hired scholars adapt their publication behavior (and
scholarly interests) to the existing academic interests of the new
institution. In this work, we see evidence that this may be the case, the
outgoing researchers are more similar to the natives than the incoming
researchers. This is reflected in a shift toward higher alignment and a
narrower alignment distribution among outgoers. The other mechanism is the
preference of institutions to hire scholars with similar skills to their
current knowledge base or the preference of scholars to move to universities
that are established in their fields. Also, a preferential dynamics of
researchers going to places where lots of expertise exists, as, e.g. described
in [40], might explain part of the observed effects.

We also assessed the role of collaboration within the institution. We found a
significant difference in the skill alignments of newcomers who have
collaborative relationships (co-authorship on publications) with colleagues
(natives or outgoers) at hiring institutions. Our results suggest that
collaboration is the most natural approach for newcomers and natives to align,
combine, and complement their skills at the institution. Both newcomers and
outgoers who did not engage in collaborations are virtually unmatched by the
local workforce, i.e., the cosine $\sim 0.5$. Note that skill differences
between natives and newcomers are small when the degree of cooperation between
them is high. The role of collaboration in skill alignment may also explain
the disciplinary differences observed in our study, where Social Sciences,
Humanities, Mathematics, and Computer Science show systematically lower levels
of skill alignment. These disciplines traditionally have lower collaboration
rates than the Natural Sciences or Engineering [41].

A shortcoming of the present work is that individual preferences and
motivations for mobility cannot be assessed. It would be interesting to
supplement these results in future work with appropriately designed surveys
and controlled experiments to uncover the relative importance of the
individual-level mechanisms that lead to the observed profile alignments at
the institutional level.

The presented results on the extent of skill alignments in scientific hiring
can be considered steps toward a better understanding of talent flows in
science. Future research would be important to determine how the alignment of
institutions’ skills profile interacts with different dimensions of workforce
diversity, such as gender and seniority. This could provide policymakers with
analytical tools to uncover the latent capabilities of different kinds of
newcomers and assess their ability to influence (or not) the skill profile of
their institutions.

Quantitative measures, such as those presented here, can inform and evaluate
university (and unit) policy regarding their mobility, recruitment, and talent
acquisition strategies, particularly about their existing competency profiles
and those desired in the future. University leadership, funding agencies, and
science policymakers in general– may benefit from a quantitative assessment of
the degree of alignment within their respective areas or organizations and can
use it to develop interventions aimed at reaching desired alignment levels
(e.g., by promoting internal collaboration networks).

## Materials and Methods

Investigating the alignment between the skills profile of mobile scientists
(newcomers or outgoers) and that of resident faculty at the institutional
level requires data that describes the skills of individuals and captures the
temporal information of the affiliations of every scientist. Such information
is typically unavailable in surveys on a country’s labor force. Even if
available, the categories of highly skilled workers are often too ambiguous to
identify specific groups of scientists [5]. Therefore, we use data from the
Dimensions111Dimensions is produced by Digital Science and was launched in
January 2018. For more references, see Dimensions.ai website database, which
we accessed through the Centre for Science and Technology Studies (CWTS) at
Leiden University. Dimensions covers local journals more comprehensively than
other large-scale bibliographic databases such as Web of Science or Scopus.
Its broader scope allows our analysis to be more inclusive of organizations
with a more local focus, and thus, we also reduce mainstream effects [10, 37].

We examine the publication patterns of disambiguated authors from 108
countries between 2000 and 2020. Our analysis relies on three major
improvements to the data: algorithmic disambiguation of author-names, improved
consistency of organizations’ metadata [37], and a highly detailed field
classification system [30], the latter also provided by the CWTS. We focus on
disambiguated publications by authors with harmonized affiliation links. High-
precision author disambiguation and institutional harmonization allow us to
track the publication history of individual scientists across research
institutions [10]. This provides us with 9,299,250 million disambiguated
author names.

Individual authors were disambiguated using the author-name disambiguation
algorithm developed by Dimensions [37], which uses the public ORCID222For more
information, see ORCID documentation as the basis for validating each author
and their publication history. The disambiguation of organization names is
based on the GRID333For more information, see GRID website system [38]. For
these authors, we retrieve their affiliations and publication history. We only
consider publication-intensive institutions with at least 2,000 indexed
publications, resulting in 3,965 institutions. Associated with these authors
are 25,310,742 distinct documents indexed in the Dimensions database. The
disambiguation of author names and the harmonization of research organization
procedures allow us to produce a consistent overview of changes in
researchers’ affiliations with different institutions [10] and topics in
large-scale bibliometric analysis.

### Measures of skill alignment for scientific institutions

The skill sets of an institution’s workforce are defined using the
publication-level classification system of science developed by Waltman and
van Eck [42]. The classification is done using the Leiden algorithm [30] that
clusters publications based on direct citation relations. With this method, we
obtain a detailed classification system for scientific literature that covers
all scientific fields. It provides several features. First, it identifies the
relatedness between pairs of 38.4 million publications indexed in Dimensions
that are directly linked to 513 million citation relations. This step
processes publications such as articles, reviews, book chapters, and
proceedings from 2006 to 2020. In the second step, the publications are
clustered into research areas using a clustering procedure and the areas are
organized in a hierarchical structure [30]. Finally, the methodology results
in a hierarchical clustering system: i) a top level with 22 broad disciplines,
ii) the second level with 824 areas, and iii) the third level with 4,163
micro-clusters. For more details on this approach, we refer to [42, 30].

In this paper, we consider the third classification level of 4,163 micro-
clusters to define the skills profile vectors of institutions, as shown in
Figure 1. In the figure, quantities with a subscript $\Sigma$ refer to the
aggregate quantities of institutions. Formally, for an institution, $i$ (for
which we have omitted the index $i$ in the figure for simplicity),

$S^{\nu}_{\Sigma,i}=\sum_{r=1}^{N_{i}}S^{\nu_{r}}_{r,i}\leavevmode\nobreak\
\leavevmode\nobreak\ ,\leavevmode\nobreak\ \leavevmode\nobreak\
\leavevmode\nobreak\
S^{\sigma}_{\Sigma,i}=\sum_{r=1}^{S_{i}}S^{\sigma_{r}}_{r,i}\leavevmode\nobreak\
\leavevmode\nobreak\ ,\leavevmode\nobreak\ \leavevmode\nobreak\
S^{\omega}_{\Sigma,i}=\sum_{r=1}^{O_{i}}S^{\omega_{r}}_{r,i}$

where $S^{\nu}_{r,i},\ S^{\sigma}_{r,i},\ S^{\omega}_{r,i}$ represent the
$n$-component skill cluster vector of the newcomer, native, and outgoer
scientists, $r$, respectively. The institution where these scientists are
hosted is labeled by the index, $i$. The values $N_{i},\ S_{i},\ O_{i}$
represent the total number of newcomers, natives, and outgoers, respectively,
in institution, $i$. Components in the skill vectors are always binary, $1$ if
the skill is present, $0$ if it is not present in an individual or at the
institutional level.

The angles $\theta_{i}$ and $\eta_{i}$ are defined according to the cosine
similarity expressions with the Euclidean dot product:

$\cos\theta_{i}=\frac{S^{\nu}_{\Sigma,i}\cdot(S^{\omega}_{\Sigma,i}+S^{\sigma}_{\Sigma,i})}{|S^{\nu}_{\Sigma,i}|\,|S^{\omega}_{\Sigma,i}+S^{\sigma}_{\Sigma,i}|}.$
(1)
$\cos\eta_{i}=\frac{S^{\omega}_{\Sigma,i}\cdot(S^{\nu}_{\Sigma,i}+S^{\sigma}_{\Sigma,i})}{|S^{\omega}_{\Sigma,i}|\,|S^{\nu}_{\Sigma,i}+S^{\sigma}_{\Sigma,i}|}.$
(2)

These definitions quantify the skill profile alignment of newcomers,
$\cos\theta_{i}$, and the skills profile alignment of outgoers,
$\cos\eta_{i}$, relative to the remaining researchers at academic
institutions. A value of $\cos\theta_{i}=1$ (or $\cos\eta_{i}=1$) indicates
that two vectors of skill clusters are identical for a given institution,
reflecting the fact that the institution attracts new scientists and retains
native scientists or promotes outgoing scientists and retains native
scientists with the same micro-clusters or ‘skills’, but also that there are
scientists producing publication outputs that express these skills with the
same weight. Conversely, a value of $0$ means that these different types of
scientists do not have the same skills profile.

We introduce a reference or “null” model for these two measures that retains
the size of the institutions in terms of their total number of competencies
and the number of authors. This is done to remove correlations between
newcomers (outgoers) and the profile of the natives. This way, we recalculate
$\cos\theta$ and $\cos\eta$ by randomly assigning newcomers and outgoers to
institutions. This procedure allows us to disentangle actual ”local” matching
between scientists from statistical effects due to collaborative activities
and the institutional size; see Figure 2A and B.

Using the same workforce components shown in Figure 1, we additionally
calculate a measure of inter-institutional skill alignment between institution
$i$ and $j$, $\cos\phi_{ij}(t)$, to track whether institutions’ profiles are
aligning or diverging over time. Here, $\phi_{ij}(t)$ is the angle between
total (native, incoming, and outgoing) skill vectors of institutions $i$ and
$j$ at time period, $t$ (i.e., 2000-2004, 2005-2009, 2010-2014, and
2015-2019). This indicator estimates and accounts for an institution’s entire
workforce (i.e., no distinction is made between newcomers, natives, and
outgoers) and reflects overall profile alignment across all pairs of
institutions over four non-overlapping time periods, $t$ .

We define inter-institutional alignment, $\cos\phi_{ij}(t)$, as

$\cos\phi_{ij}(t)=\frac{T_{i}\cdot T_{j}}{|T_{i}|\,|T_{j}|}\,.$ (3)

where $T_{i}=S^{\nu}_{\Sigma,i}+S^{\omega}_{\Sigma,i}+S^{\sigma}_{\Sigma,i}$
is the combined total skill vector of all scientists at institution, $i$.
Finally, we capture the change in pairwise institutional alignment, which we
refer to as inter-institutional alignment, as

$\Delta\cos\phi_{ij}(t)=\cos\phi_{ij}(t+1)-\cos\phi_{ij}(t)\,.$ (4)

Clustering relatively homogeneous publication sets into high-resolution
clusters allows us to compare the aggregate capabilities of scientists within
and across institutions. As we explain in the following section, this allows
us to compute the citation impact of organizations in a similar research
context [42, 43].

### Citation impact indicators and normalization

In recent years, in scientometrics, several changes took place that continue
to influence the formal analyses of scientific dynamics. A growing awareness
of the need to account for differences across and within disciplines when
assessing the impact of research has increased research toward innovative
indicators. In particular, field-normalized indicators based on bibliometric
analyzes have become increasingly important for evaluating citation impact.
For example, the average number of citations per publication varies
significantly across scientific fields, institutions, and countries. The
average number of citations per publication also varies by the age of the
publication [44]. Older publications are cited more frequently than more
recent ones [45]. Because of this uneven distribution of citations across
different fields or years, citation counts or averages cannot be compared
across research units [44]. This is also important for the life and earth
sciences, biomedical and health sciences, physical sciences and engineering,
mathematics and computer science, and social sciences and humanities because
these fields encompass different sub-disciplines and the sub-disciplines vary
widely.

Taking these issues into account, we use the same high-resolution micro-
clusters of topics used to define institutional competency vectors in Measures
of skill alignment for scientific institutions \- or the third level of
classification and denoted by $S$ in 1 \- to calculate the normalized citation
indicators for each institution in our sample. These micro-clusters contain
publications from multiple years (2000-2020), and each publication is assigned
to a cluster based only on its citation relationships with other publications
[30]. We use a full-counting approach at the institutional level to calculate
citation impact. That is, if two institutions contribute co-authors to a
publication, the publication is counted as a full publication for both
institutions. Using a full-counting approach, we give more weight to
collaborative publications than non-collaborative ones [42]. We use a
publication window from 2006 to 2020 and a fixed citation window of four years
to count citations to these papers through 2020. The authors’ self-citations
are not included in the calculation of impact indicators.

Before proceeding with the formal definition of citation-based indicators, we
first consider a set of $n$ publications denoted by $1,\cdots,n$. Let $c_{i}$
denote the number of citations of a publication $i$, and $e_{i}$ denote the
expected number of citations of publication $i$ given micro-cluster $S$ and
year $t$ in which publication $i$ was published. In other words, $e_{i}$ is
the average number of citations of all publications published in the same
micro-cluster and year as publication $i$. We define two indicators of
citation impact for each research institution: the total normalized citation
score, TNCS, and the proportion of publications in the top $n^{th}\%$,
PP${}_{\mathrm{top\,nth\%}}$. The TNCS indicator captures an institution’s
total normalized citation rate of the produced publication volume. It is
similar to what [46] calls the total field normalized citation score indicator
and is defined as

$\mathrm{TNCS}=\sum\nolimits_{i=1}^{n}\frac{c_{i}}{e_{i}}\,.$ (5)

The PP${}_{\mathrm{top\,nth\%}}$ uses percentile rank classes instead of mean-
based indicators to normalize the citation impact of publications [47, 42,
48]. It measures the proportion of articles among the top $n^{th}$% most cited
papers in the same skill or micro-cluster, of the same age and document type.
We first assign a percentile based on its position in the citation
distribution of articles in the same micro-cluster. We use the approach
described in [48] to calculate the percentile rank of each publication. In our
analysis, we compute three variations of this metric. Specifically, we use the
$99^{\mathrm{th}}$, the $95^{th}$ and the $90^{\mathrm{th}}$ percentile ranks,
which assign papers with a percentile equal to or greater than the
$99^{\mathrm{{th}}}$, $95^{\mathrm{{th}}}$, and $90^{\mathrm{{th}}}$
percentile to the top 1%, 5%, and 10% of frequently cited papers,
respectively. The percentile rank measures, PPtop 1%, PPtop 5%, and the PPtop
10% of each publication was calculated using

$\mathrm{PP}_{\mathrm{top\,x\%}}=\frac{\sum_{i=0}^{\infty}n_{i}c_{i}}{\sum_{i=0}^{\infty}\
n_{i}}\,,$ (6)

where $n_{i}$ denotes the number of publications from a scientific institution
with $i$ citations. The score of a publication with $i$ citations is indicated
by $c_{i}$. According to this definition, the PP${}_{\mathrm{top\,nth\%}}$ is
simply the average score of the publications of the unit of analysis. For
simplicity, the definition assumes that all publications of a given unit
belong to the same cluster. For more details, see [48, 49, 44].

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## Additional information

Competing interests
All authors declare that they have no competing interests.

Funding
We acknowledge support from the Austrian Research Promotion Agency FFG under
grants 857136 and 882184, as well as the South African DST-NRF Centre of
Excellence in Scientometrics and Science, Technology and Innovation Policy
(SciSTIP).

We also thank Giordano de Marzo, Frank Neffke and Niklas Reisz for fruitful
discussions and the Centre for Science and Technology Studies (CWTS) at Leiden
University for access to the Dimensions database.

## Supplementary information

Figure 6: Scatter plot of the correlation between newcomer $\cos{\theta}$ (W/o
collab)- and outgoer $\cos{\eta}$ (W/o collab) alignment measures.Grey
quadrant lines of median $\cos{\theta}$ (W/o collab) ($\tilde{x}=0.54$) and
$\cos{\eta}$ (W/o collab) ($\tilde{x}=0.61$) are shown. Each circle in A
represents an institution. The color of the circles represents the scientific
impact of the institutions as captured by the PPtop 1% indicator. Grey-colored
institutions have a PPtop 1% that is close to or below the global average
(PP${}_{\mathrm{top\,1\%}}\leq{0.01}$) of institutions with the same skill
profile and years of production. The orange, blue, and yellow circles
represent institutions with medium
($0.01\leq\mathrm{PP}_{\mathrm{top\,1\%}}\leq{0.05}$), high
($0.06\leq\mathrm{PP}_{\mathrm{top\,1\%}}\leq{0.09}$), and very high impact
(PP${}_{\mathrm{top\,1\%}}\geq{0.10}$). 35% of institutions are in quadrant 1,
29% in quadrant 2, 36% in quadrant 3, and 1% in quadrant 4. The scatter plots
in panels B and C show the relationship between an institution’s percentage of
its total skills and $\cos{\theta}$ (W/o collab) and $\cos{\eta}$ (W/o
collab), respectively, for education, health, and research facilities when
articles in collaboration between the different types of the workforce are
excluded. The panels have the same setting as the Figure 3, but this time for
the measures $\cos\theta$ (W/o collab) and $\cos\eta$ (W/o collab), i.e. for
the case of non-collaborative hires and leavers. We find much greater
variability than in the main measures of skill alignment in the main text.
Similar to Figure 3, the skill profile of newcomers and outgoers is
concentrated in the first and third quadrants, followed by the second and
fourth quadrants. Interestingly, “Ivy League” universities and molecular
biology and cancer research institutes remain concentrated in the first
quadrant. In contrast, university hospitals and medical research centres are
mainly found in the second and first quadrants. The most frequently cited
institutions are mostly concentrated around high scores for non-collaborative
alignment in quadrant one, with some exceptions spreading across the other
three quadrants. Panels B and C show a very similar trend compared to figures
3B and C.  Figure 7:  Boxplots of newcomer skill alignment, $\cos{\theta}$(W/o
collab), and outgoer skill alignment, $\cos{\eta}$(W/o collab), by major
fields of science and quartiles of PPtop1%. We provide an overview of
disciplinary trends in the alignment of mobile researchers’ skills that occurs
when scholars within an institution do not collaborate. Top-cited institutions
have higher alignment than average organizations (two-sample $t$ test, $P$
value <0.001) even when researchers do not collaborate internally. This
finding can be generalized across large scientific fields and suggests that
the alignment of capabilities that occurs from non-collaborative work
positively affects the citation visibility of institutions. Here the quartiles
from low impact (i.e., quartile 4) to high impact (i.e., quartile 1) and the
disciplines are arranged alphabetically from top to bottom. The panels have
the same setting as the Figure 4, but this time for the $\cos\theta$ (W/o
collab) and $\cos\eta$ (W/o collab) measures, i.e., for the case of non-
collaborative hires and leavers. We find that the alignment of their skills is
significantly lower across impact quartiles and disciplines. Nevertheless,
alignment scores are higher for the most cited institutions (i.e., quartile 1)
than for the least cited institutions (i.e., quartile 4). This tells us that
internal collaboration is also an important factor in integrating knowledge
and aligning skills in institutions and across the disciplines in which the
institutions operate. Moreover, the most frequently cited institutions still
attract and nurture similarly skilled researchers, even if they do not
collaborate on scientific publications within the institution.

### SI Text 1: Classification of disambiguated authors into Newcomers,
Natives, and Outgoers

Researchers affiliated with each organization in the data are split into three
categories based on where they began publishing in relation to the
organization being analyzed: a) Natives – are those who started publishing on
the same organization and have not (yet) changed their affiliation, b)
Newcomers – are those who started publishing on a different organization, and
c) Outgoers – are those who left the first organization where they published
at some point under the period of analysis (2000-2020).

### SI Text 2: Multivariate Linear Regression Analysis

In this section, we delve into the details of our regression analysis. We
first define several indicators to better understand the connection between
skill alignment, intra-institutional collaboration, scientific impact, and
productivity measures. To ensure the validity of our findings, we also conduct
robustness checks to test whether our main findings still hold when
alternative measures of scientific influence are used (see sections SI Text
2.1, and SI Text 2.2).

#### SI Text 2.1: Regression of newcomer skill alignment

Our main results are based on a multivariate linear ordinary least squares
regression model as below:

$\begin{array}[]{lcl}{\cos\theta}_{i}&=&\beta_{0}\\\
&&+\beta_{1,2}{\mathrm{X}}_{i}\\\ &&+\beta_{3}{\mathrm{r}}_{i}\\\
&&+\beta_{4}{\mathrm{PP}_{\nu}}_{i}\\\
&&+\beta_{5}{\mathrm{PPP}\,(\mathrm{collab}_{\nu})_{i}}\\\
&&+\beta_{6}{\mathrm{PPS}_{i}:\mathrm{Diversified}}\\\ &&+\epsilon_{i}\,.\\\
\end{array}$ (7)

Dependent Variable. The dependent variable is a measure of how closely the
skills of newcomers and the existing workforce at an institution, $i$, match.
This measure is represented by $\cos{\theta}_{i}$ ,which is defined in Eq. 1.
Additionally, we also use another measure, $\cos{\eta}_{i}$, for outgoers who
have changed their institutional affiliations, which is defined in Eq. 2.

Predictors of Interest.The predictors of interest in this study are three
percentile-based citation impact metrics (PPtop,1%i, PP$\mathrm{top,5\%i}$,
and PP$\mathrm{top,10\%_{i}}$) and a continuous variable of normalized
citation counts (TNCSi). These measures will be used to explain the skill
alignment of newcomers and existing workforce in an institution by using a
regression model. For the term Xi in Eq. 7 we first use three percentile-based
independent variables, PP${}_{\mathrm{top\,1\%_{i}}}$,
PP${}_{\mathrm{top\,5\%_{i}}}$, and PP${}_{\mathrm{top\,10\%_{i}}}$, which
indicate the fractions of upper-tail papers being the top 1%, 5%, and 10%,
respectively, as measured by citation (see definition in Eq. 6). These
measures are included separately in different regression models, with
coefficient $\beta_{1}$. Finally, with coefficient $\beta_{2}$, we use an
alternative measure of scientific impact in Xi, the continuous variable TNCSi,
defined in Eq. 5, which measures the total normalised citation count of a
scientific institution.

Control Variables. In this study, several other explanatory variables are
included as independent variables to control for other possible predictors of
workforce skill alignment. These variables include size and internal
collaboration indicators at the institutional level. Descriptive statistics on
the variables used in the regression analysis are presented in Table S1. The
following control variables will be used in the regression analysis of
newcomers’ skill alignment presented in this section:

  * •

ri indicates the total number of researchers at a research institution

  * •

PP${}_{\nu_{i}}$ indicates the proportion of newcomers of a research
institution

  * •

PPP (collab${}_{\nu})_{i}$ indicates the proportion of publications in
collaboration between newcomers and the rest of the institution

  * •

PPSi is a dichotomous variable that indicates whether an institution has an
overall diversified or specialized skills’ portfolio. It indicates the absence
or presence of diversification or specialization that may be expected to shift
the outcome of skill alignment produced by the total workforce of a research
institution. PPSi = 1 if the institution has produced an above median
proportion of skills and PPSi = 0 otherwise

These control variables are used to account for other factors that may
influence the skill alignment of newcomers and existing workforce in an
institution. Furthermore, we included PPSi, and ri in all regression models.
All independent variables included in the regression models had a variance
inflation score (VIF) well below four, below the tolerance threshold of 0.25.

Stratification by Size of Institutions. The total publication output, $P_{i}$,
is used as an indicator of the size of an institution. Institutions’ size has
been shown to affect the ability of institutions to attract researchers [10,
17, 50]. It is therefore assumed that size also modifies the proportion of
publications in the top $1\%$, $5\%$, and $10\%$ of an institution’s
publications on skill alignment. To control for this confounding effect, we
stratified institutions into three subgroups based on their publication
production, creating subgroups in which an institution’s publication
production varies less than all institutions in the sample combined. This
stratification strategy will be applied in all regression models.

The main results of the stratified regression analysis are presented in Table
S2, Table S3, and Table S4. The coefficients of the tables are standardized.
Standardizing the coefficients allows us to compare the strength of the effect
of each individual independent variable on the dependent variable. This is
important because size-dependent associations can produce substantially higher
correlation coefficients, especially when they exhibit substantial variance
[51]. This can potentially alter the relative associations between alignment
and normalized count-based citation measures.

The results of models (1), (2), (3), and (4) in the three tables suggest that
there is a significant relationship between an institution’s skill alignment
and the proportion of publications in the top 1%, 5%, 10%, and Total
Normalized Citation Score (TNCS) after controlling for several other
explanatory variables. These variables include the proportion of incoming
researchers, the proportion of internal collaboration between the newcomers
and the rest of the institution’s workforce, the total workforce, and the
degree of diversification of skills. This relationship is consistent across
all subgroups of institutional size. The findings also indicate that skill
alignment is, on average, lower in diversified institutions than in
specialized institutions, as indicated by the binary variable PPSi:
Diversification/Specialization, which was negative in all models and tables.
In specialized institutions, which are more thematically focused, the
expertise of mobile researchers better matches the expertise of their
institutions, while in diversified institutions there are more opportunities
for topical diversification. The proportion of incoming researchers, while
significant, does not appear to be a particularly strong predictor of skill
alignment.

Collaboration activity within the institution is one of the strongest
predictors of skill alignment, with a standardized coefficient between 0.455
and 0.442 across different subgroups of institutional size. This suggests that
institutions that encourage internal collaboration among researchers may be
more successful in aligning the expertise of newcomers with that of the
institution as a whole. The proportion of papers in the top 1%, 5% and 10% of
highly cited publications are also found to be significantly associated with
skill alignment, with standardized coefficients of 0.168(0.026), 0.214(0.028)
and 0.203(0.028) respectively in, for example, Table S2. For medium and high
output institutions, total workforce capacity (ri) is also found to be
considerably associated with higher levels of alignment, with the coefficients
ranging from 0.224(0.023) to 0.213(0.024) in Table S3 and slightly higher for
high output institutions, ranging from 0.307(0.048) to 0.288(0.022) in Table
S4.

Overall, these results suggest that internal collaboration and citation impact
likely explain the alignment at institutions, particularly in institutions
with lower publication output, while medium and large publication output
institutions also achieve greater alignment by attracting most of the
available scientific labour [50]. The citation impact variables for medium and
large institutions remain significantly associated with alignment, except for
the total normalized citation score (TNCSi) in model (4) in Table S4 for high
output institutions. This suggests that while publications at the high end of
the citation percentile scale may play a role in aligning an institutions
expertise, citation ‘quantity’ does not have a direct effect on the alignment
of skills among newcomers and the rest of the workforce in larger scientific
institutions.

Table S1. Descriptive statistics of the main indicators and control variables
for all academic institutions in the sample (mean, median, standard deviation,
skewness, and kurtosis). Descriptive Statistics
---
N | N | Mean | Median | Std. Deviation | Skewness | Kurtosis
PP${}_{top1\%_{i}}$ | 3,965 | 0.02 | 0.01 | 0.01 | 2.25 | 11.58
PP${}_{top5\%_{i}}$ | 3,965 | 0.07 | 0.06 | 0.03 | 1.35 | 3.77
PP${}_{top10\%_{i}}$ | 3,965 | 0.13 | 0.12 | 0.05 | 0.97 | 1.69
TNCSi | 3,965 | 9,809.58 | 3,813.59 | 18,324.12 | 5.36 | 41.00
Pi | 3,965 | 7,196.51 | 3,610.00 | 11,238.25 | 4.27 | 24.13
ri | 3,965 | 3,834.33 | 1,894.00 | 5,899.69 | 4.99 | 40.13
PP${}_{\nu_{i}}$ | 3,965 | 0.41 | 0.39 | 0.17 | 0.37 | -0.55
PP${}_{\omega_{i}}$ | 3,965 | 0.19 | 0.19 | 0.06 | 0.46 | 0.58
PPP (collab${}_{\nu})_{i}$ | 3,965 | 0.30 | 0.30 | 0.10 | 0.40 | 1.68
PPP (collab${}_{\omega})_{i}$ | 3,965 | 0.27 | 0.27 | 0.11 | 0.44 | 0.60
PPSi | 3,965 | 0.21 | 0.17 | 0.13 | 1.15 | 0.87
cos $\theta_{i}$ | 3,965 | 0.82 | 0.84 | 0.10 | -0.99 | 1.41
cos $\eta_{i}$ | 3,965 | 0.84 | 0.86 | 0.10 | -1.24 | 2.16
cos $\theta$ (W/o collab)i | 3,965 | 0.55 | 0.54 | 0.17 | 0.19 | -0.45
cos $\eta$ (W/o collab)i | 3,965 | 0.60 | 0.61 | 0.17 | -0.16 | -0.52
Table S2. Relationship between low output institutions’ citation impact and newcomer skill alignment. To allow comparison of the coefficients in the table, all variables were centred. Therefore, the beta coefficients in the table have standard deviations as their units. The results in models (1), (2), (3), and (4) show that the proportion of publications in the top 1%, 5%, 10% and the continuous variable TNCSi have a significant association with the alignment between newcomers and the rest of the workforce. The degree of internal collaboration within institution, PPP(collab${}_{\nu})_{i}$, is found to be most strongly associated with alignment, while the proportion of newcomers, PP${}_{\nu_{i}}$, and the total workforce, ri, are least strongly associated with skill alignment. | Dependent variable: $\cos{\theta}_{i}$ |
---|---|---
|  Institutions with Low Publication Output, Pi [387, 2425]
| (1) | (2) | (3) | (4)
Constant |  0.086∗∗∗ (0.025) |  0.082∗∗∗ (0.025) |  0.080∗∗∗ (0.025) |  0.093∗∗∗ (0.025)
ri |  0.094∗∗∗ (0.023) |  0.096∗∗∗ (0.023) |  0.098∗∗∗ (0.023) |  0.088∗∗∗ (0.023)
PP${}_{\nu_{i}}$ |  0.137∗∗∗ (0.025) |  0.106∗∗∗ (0.027) |  0.094∗∗∗ (0.028) |  0.143∗∗∗ (0.025)
PPP (collab${}_{\nu})_{i}$ |  0.455∗∗∗ (0.022) |  0.446∗∗∗ (0.022) |  0.442∗∗∗ (0.022) |  0.443∗∗∗ (0.023)
PP${}_{{top\,1\%}_{i}}$ |  0.168∗∗∗ (0.026) |  |  |
PP${}_{{top\,5\%}_{i}}$ |  |  0.203∗∗∗ (0.028) |  |
PP${}_{{top\,10\%}_{i}}$ |  |  |  0.214∗∗∗ (0.028) |
TNCSi |  |  |  |  0.164∗∗∗ (0.025)
PPSi: Diversification/Specialization dummy? | Yes | Yes | Yes | Yes
Observations | 1,322 | 1,322 | 1,322 | 1,322
Adjusted R2 | 0.386 | 0.391 | 0.392 | 0.385
Residual Std. Error (df = 1316) | 0.784 | 0.780 | 0.780 | 0.784
F Statistic (df = 5; 1316) |  166.946∗∗∗ |  170.617∗∗∗ |  171.361∗∗∗ |  166.655∗∗∗
Standard errors in parentheses. |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Table S3. Relationship between medium output institutions’ citation impact and newcomer skill alignment. To allow comparison of the coefficients in the table, all variables were centred. Therefore, the beta coefficients in the table have standard deviations as their units. The results in models (1), (2), (3), and (4) show that the proportion of publications in the top 1%, 5%, 10% and the continuous variable TNCSi have a significant association with the alignment between newcomers and the rest of the workforce. The degree of internal collaboration within institution, PPP(collab${}_{\nu})_{i}$, is most strongly associated with alignment within institutions, followed by the total workforce, ri, of the institution while the proportion of newcomers, PP${}_{\nu_{i}}$ is least strongly associated with skill alignment. | Dependent variable: $\cos{\theta}_{i}$
---|---
|  Institutions with Medium Publication Output, Pi [2425, 4864]
| (1) | (2) | (3) | (4)
Constant |  0.244∗∗∗ (0.031) |  0.248∗∗∗ (0.031) |  0.249∗∗∗ (0.031) |  0.257∗∗∗ (0.031)
ri |  0.224∗∗∗ (0.023) |  0.222∗∗∗ (0.023) |  0.222∗∗∗ (0.023) |  0.213∗∗∗ (0.024)
PP${}_{\nu_{i}}$ |  0.073∗∗∗ (0.024) |  0.062∗∗ (0.024) |  0.059∗∗ (0.024) |  0.096∗∗∗ (0.024)
PPP (collab${}_{\nu})_{i}$ |  0.431∗∗∗ (0.022) |  0.426∗∗∗ (0.022) |  0.423∗∗∗ (0.022) |  0.429∗∗∗ (0.022)
PP${}_{{top\,1\%}_{i}}$ |  0.182∗∗∗ (0.024) |  |  |
PP${}_{{top\,5\%}_{i}}$ |  |  0.202∗∗∗ (0.024) |  |
PP${}_{{top\,10\%}_{i}}$ |  |  |  0.208∗∗∗ (0.024) |
TNCSi |  |  |  |  0.140∗∗∗ (0.024)
PPSi: Diversification/Specialization dummy? | Yes | Yes | Yes | Yes
Observations | 1,321 | 1,321 | 1,321 | 1,321
Adjusted R2 | 0.406 | 0.412 | 0.414 | 0.396
Residual Std. Error (df = 1315) | 0.770 | 0.767 | 0.766 | 0.777
F Statistic (df = 5; 1315) |  181.735∗∗∗ |  186.102∗∗∗ |  187.440∗∗∗ |  174.110∗∗∗
Standard errors in parentheses. |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Table S4. Relationship between high output institutions’ citation impact and newcomer skill alignment. To allow comparison of the coefficients in the table, all variables were centred. Therefore, the beta coefficients in the table have standard deviations as their units. The results in models (1), (2), (3), and (4) show that the proportion of publications in the top 1%, 5%, 10% have a significant association with the alignment between newcomers and the rest of the workforce. The degree of internal collaboration within institution, PPP(collab${}_{\nu})_{i}$, is most strongly associated with alignment within institutions, followed by the total workforce, ri, of the institution while the proportion of newcomers, PP${}_{\nu_{i}}$ is least strongly associated with skill alignment. | Dependent variable: $\cos{\theta}_{i}$
---|---
|  Institutions with High Publication Output, Pi [4864, 121750]
| (1) | (2) | (3) | (4)
Constant |  0.588∗∗∗ (0.086) |  0.584∗∗∗ (0.086) |  0.588∗∗∗ (0.086) |  0.693∗∗∗ (0.085)
ri |  0.295∗∗∗ (0.022) |  0.288∗∗∗ (0.022) |  0.288∗∗∗ (0.022) |  0.307∗∗∗ (0.048)
PP${}_{\nu_{i}}$ |  0.070∗∗∗ (0.027) |  0.057∗∗ (0.027) |  0.058∗∗ (0.027) |  0.162∗∗∗ (0.022)
PPP (collab${}_{\nu})_{i}$ |  0.429∗∗∗ (0.021) |  0.425∗∗∗ (0.021) |  0.422∗∗∗ (0.021) |  0.424∗∗∗ (0.022)
PP${}_{{top\,1\%}_{i}}$ |  0.159∗∗∗ (0.028) |  |  |
PP${}_{{top\,5\%}_{i}}$ |  |  0.178∗∗∗ (0.029) |  |
PP${}_{{top\,10\%}_{i}}$ |  |  |  0.175∗∗∗ (0.029) |
TNCSi |  |  |  | 0.021 (0.048)
PPSi: Diversification/Specialization dummy? | Yes | Yes | Yes | Yes
Observations | 1,322 | 1,322 | 1,322 | 1,322
Adjusted R2 | 0.430 | 0.432 | 0.432 | 0.416
Residual Std. Error (df = 1316) | 0.755 | 0.754 | 0.754 | 0.764
F Statistic (df = 5; 1316) |  199.974∗∗∗ |  202.121∗∗∗ |  201.545∗∗∗ |  188.853∗∗∗
Standard errors in parentheses. |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01

#### SI Text 2.2: Regression of outgoer skill alignment

The results in this section are based on a multivariate linear ordinary least
squares regression model as below:

$\begin{array}[]{lcl}{\cos\eta}_{i}&=&\beta_{0}\\\
&&+\beta_{1,2}{\mathrm{X}}_{i}\\\ &&+\beta_{3}{\mathrm{r}}_{i}\\\
&&+\beta_{4}{\mathrm{PP}_{\omega}}_{i}\\\
&&+\beta_{5}{\mathrm{PPP}\,(\mathrm{collab}_{\omega})_{i}}\\\
&&+\beta_{6}{\mathrm{PPS}_{i}:\mathrm{Diversified}}\\\ &&+\epsilon_{i}\,.\\\
\end{array}$ (8)

Dependent Variable. The dependent variable is the skill alignment of the
outgoer and remaining workforce of an institution, $i$, $\cos{\eta}_{i}$
defined in Eq. 2.

Predictors of Interest. Just like in the previous section, we use, for the
term Xi, three percentile-based independent variables, ${PP}_{top1\%}$,
${PP}_{top5\%}$, and ${PP}_{top10\%}$, measures to indicate the fractions of
upper-tail papers being the top 1%, 5%, and 10%, respectively, gauged by
citation (See definition in the Eq. 6). These measures are included separately
in different regression models with coefficient $\beta_{1}$. The total
normalized citation counts of a scientific institution TNCSi, defined in Eq. 5
is also tested in Xi with coefficient $\beta_{2}$.

Control Variables. In order to account for other potential factors that may
impact outgoer skill alignment, we have included a number of explanatory
variables in our analysis. These variables include measures of institutional
size and internal collaboration, and the descriptive statistics for these
variables can be found in Table S1. These variables will be used as control
variables in our regression analysis.

  * •

ri indicates the total number of researchers at a research institution

  * •

PP$\omega_{i}$ indicates the proportion of outgoers of a research institution

  * •

PPP (collab${}_{\omega})_{i}$ indicates the proportion of publications in
collaboration between outgoers and natives of the institution

  * •

PPSi is a dichotomous variable that indicates whether an institution has an
overall diversified or specialized skills’ portfolio. It indicates the absence
or presence of diversification or specialization that may be expected to shift
the outcome of skill alignment produced by the total workforce of a research
institution. PPS = 1 if the institution has produced an above median
proportion of skills and PPS = 0 otherwise

These control variables are used to account for other factors that may
influence the skill alignment of outgoers and existing workforce in an
institution. Furthermore, we included PPSi, and ri in all regression models.
All independent variables included in the regression models had a variance
inflation score (VIF) well under four, below the tolerance threshold of 0.25.

Stratification by Size of Institutions. The total publication output, $P_{i}$,
is used as an indicator of the size of an institution. Institutions’ size has
been shown to affect the ability of institutions to attract researchers [10,
17]. It is therefore assumed that size also modifies the proportion of
publications in the top $1\%$, $5\%$, and $10\%$ of an institution’s
publications on outgoer skill alignment. Stratification allows us to control
for this confounding effect by creating three subgroups in which an
institution’s publication production varies less than all institutions in our
sample combined.

Table S5. Relationship between low output institutions’ citation impact and outgoer skill alignment. To allow comparison of the coefficients in the table, all variables were centred. Therefore, the beta coefficients in the table have standard deviations as their units. The results in models (1), (2), (3), and (4) show that the proportion of publications in the top 1%, 5%, 10% and the continuous variable TNCSi have a significant association with the alignment between outgoers and the rest of the workforce. The degree of internal collaboration within institution, PPP(collab${}_{\omega})_{i}$, is most strongly associated with alignment within institutions, followed by the total workforce, ri of the institution while the proportion of newcomers, PP${}_{\omega_{i}}$ and the total workforce, ri, are least strongly associated with skill alignment. | Dependent variable: $\cos{\eta}_{i}$
---|---
|  Institutions with Low Publication Output, Pi [387, 2425]
| (1) | (2) | (3) | (4)
Constant |  0.084∗∗∗ (0.023) |  0.077∗∗∗ (0.022) |  0.074∗∗∗ (0.022) |  0.094∗∗∗ (0.023)
ri |  0.118∗∗∗ (0.022) |  0.114∗∗∗ (0.021) |  0.115∗∗∗ (0.021) |  0.113∗∗∗ (0.022)
PP${}_{\omega_{i}}$ |  0.166∗∗∗ (0.025) |  0.163∗∗∗ (0.024) |  0.160∗∗∗ (0.024) |  0.161∗∗∗ (0.025)
PPP (collab${}_{\omega})_{i}$ |  0.471∗∗∗ (0.025) |  0.476∗∗∗ (0.025) |  0.478∗∗∗ (0.025) |  0.459∗∗∗ (0.025)
PP${}_{{top\,1\%}_{i}}$ |  0.212∗∗∗ (0.020) |  |  |
PP${}_{{top\,5\%}_{i}}$ |  |  0.236∗∗∗ (0.020) |  |
PP${}_{{top\,10\%}_{i}}$ |  |  |  0.243∗∗∗ (0.020) |
TNCSi |  |  |  |  0.205∗∗∗ (0.020)
PPSi: Diversification/Specialization dummy? | Yes | Yes | Yes | Yes
Observations | 1,322 | 1,322 | 1,322 | 1,322
Adjusted R2 | 0.484 | 0.493 | 0.496 | 0.482
Residual Std. Error (df = 1316) | 0.718 | 0.712 | 0.710 | 0.720
F Statistic (df = 5; 1316) |  248.656∗∗∗ |  258.183∗∗∗ |  260.884∗∗∗ |  246.584∗∗∗
Standard errors in parentheses. |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Table S6. Relationship between high output institutions’ citation impact and outgoer skill alignment. To allow comparison of the coefficients in the table, all variables were centred. Therefore, the beta coefficients in the table have standard deviations as their units. The results in models (1), (2), (3), and (4) show that the proportion of publications in the top 1%, 5%, 10% have a significant association with the alignment between outgoers and the rest of the workforce. The degree of internal collaboration within institution, PPP(collab${}_{\omega})_{i}$, is most strongly associated with alignment within institutions, followed by the total workforce, ri, of the institution while the proportion of outgoers, PP${}_{\omega_{i}}$, is least strongly associated with skill alignment. | Dependent variable: $\cos{\eta}_{i}$
---|---
|  Institutions with Medium Publication Output, Pi [2425, 4864]
| (1) | (2) | (3) | (4)
Constant |  0.178∗∗∗ (0.028) |  0.179∗∗∗ (0.028) |  0.180∗∗∗ (0.028) |  0.197∗∗∗ (0.028)
ri |  0.211∗∗∗ (0.022) |  0.206∗∗∗ (0.022) |  0.206∗∗∗ (0.022) |  0.199∗∗∗ (0.022)
PP${}_{\omega_{i}}$ |  0.121∗∗∗ (0.023) |  0.116∗∗∗ (0.023) |  0.116∗∗∗ (0.023) |  0.128∗∗∗ (0.024)
PPP (collab${}_{\omega})_{i}$ |  0.502∗∗∗ (0.024) |  0.505∗∗∗ (0.024) |  0.503∗∗∗ (0.024) |  0.486∗∗∗ (0.024)
PP${}_{{top\,1\%}_{i}}$ |  0.209∗∗∗ (0.020) |  |  |
PP${}_{{top\,5\%}_{i}}$ |  |  0.227∗∗∗ (0.020) |  |
PP${}_{{top\,10\%}_{i}}$ |  |  |  0.228∗∗∗ (0.020) |
TNCSi |  |  |  |  0.182∗∗∗ (0.020)
PPSi: Diversification/Specialization dummy? | Yes | Yes | Yes | Yes
Observations | 1,321 | 1,321 | 1,321 | 1,321
Adjusted R2 | 0.502 | 0.510 | 0.510 | 0.492
Residual Std. Error (df = 1315) | 0.706 | 0.700 | 0.700 | 0.713
F Statistic (df = 5; 1315) |  267.285∗∗∗ |  275.460∗∗∗ |  275.929∗∗∗ |  256.755∗∗∗
Standard errors in parentheses. |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01
Table S7. Relationship between high output institutions’ citation impact and outgoer skill alignment. To allow comparison of the coefficients in the table, all variables were centred. Therefore, the beta coefficients in the table have standard deviations as their units. The results in models (1), (2), (3), and (4) show that the proportion of publications in the top 1%, 5%, 10% have a significant association with the alignment between outgoers and the rest of the workforce. The degree of internal collaboration within institution, PPP(collab${}_{\omega})_{i}$, is most strongly associated with alignment within institutions, followed by the total workforce, ri, of the institution. Contrary to the regressions for low and medium output institutions, the proportion of outgoers, PP${}_{\omega_{i}}$, in an institution’s total workforce is not significantly associated with the outgoers’ skill alignment measure. | Dependent variable: $\cos{\eta}_{i}$
---|---
|  Institutions with High Publication Output, Pi [4864, 121750]
| (1) | (2) | (3) | (4)
Constant |  0.584∗∗∗ (0.089) |  0.566∗∗∗ (0.089) |  0.567∗∗∗ (0.088) |  0.840∗∗∗ (0.089)
ri |  0.311∗∗∗ (0.023) |  0.297∗∗∗ (0.023) |  0.295∗∗∗ (0.023) |  0.148∗∗∗ (0.052)
PP${}_{\omega_{i}}$ | 0.009 (0.024) | 0.009 (0.023) | 0.006 (0.023) |  $-$0.018 (0.025)
PPP (collab${}_{\omega})_{i}$ |  0.414∗∗∗ (0.026) |  0.420∗∗∗ (0.025) |  0.420∗∗∗ (0.025) |  0.366∗∗∗ (0.027)
PP${}_{{top\,1\%}_{i}}$ |  0.276∗∗∗ (0.024) |  |  |
PP${}_{{top\,5\%}_{i}}$ |  |  0.297∗∗∗ (0.024) |  |
PP${}_{{top\,10\%}_{i}}$ |  |  |  0.299∗∗∗ (0.024) |
TNCSi |  |  |  |  0.270∗∗∗ (0.051)
PPSi: Diversification/Specialization dummy? | Yes | Yes | Yes | Yes
Observations | 1,322 | 1,322 | 1,322 | 1,322
Adjusted R2 | 0.410 | 0.419 | 0.420 | 0.363
Residual Std. Error (df = 1316) | 0.768 | 0.762 | 0.761 | 0.798
F Statistic (df = 5; 1316) |  184.615∗∗∗ |  191.455∗∗∗ |  192.543∗∗∗ |  151.621∗∗∗
Standard errors in parentheses. |  ∗p$<$0.1; ∗∗p$<$0.05; ∗∗∗p$<$0.01

When analyzing the factors that explain the alignment between outgoers and the
rest of the institution, a similar pattern is observed. The results of the
analysis are presented in Table S5, Table S6, and Table S7. First, the results
in models (1), (2), (3), (4) of the three tables confirm that there is a
considerable relationship between an institution’s outgoer skill alignment and
the proportion of publications in the top 1%, 5%, and 10%, and the total
normalized citation score, even after controlling for several other
explanatory variables, including the proportion of outgoing researchers, the
proportion of internal collaboration between the outgoers and the rest of the
institution’s workforce, the total workforce, and the degree of
diversification of skills. This result is consistent across institutional size
subgroups. Interestingly, all tables shows a slightly stronger association
between all citation impact measures and ‘outgoer’ skill alignment than for
‘newcomer’ skill alignment for all types of institutions size subgroups.

Moreover, institutions where researchers collaborate internally are more
likely to have converging profiles of outgoing researchers with the rest of
the institution. While the proportion of outgoing researchers is related to
skill alignment for low and medium output institutions, it is not found to be
a significant predictor in high output institutions. In summary, the results
suggest that internal collaboration, citation impact, and total workforce
capacity are crucial predictors of outgoer skill alignment across different
institutional sizes and levels of publication output. These findings suggest
that internal collaboration and learning opportunities within institutions may
play a major role in aligning the skills of outgoing researchers with the rest
of the institution.


# Indexing AI Risks with Incidents, Issues, and Variants

Sean McGregor
Responsible AI Collab.
California
<EMAIL_ADDRESS>Kevin Paeth
MIT CSAIL
Massachusetts
<EMAIL_ADDRESS>Khoa Lam
Responsible AI Collab.
Vietnam
<EMAIL_ADDRESS>
###### Abstract

Two years after publicly launching the AI Incident Database (AIID) as a
collection of harms or near harms produced by AI in the world, a backlog of
“issues” that do not meet its incident ingestion criteria have accumulated in
its review queue. Despite not passing the database’s current criteria for
incidents, these issues advance human understanding of where AI presents the
potential for harm. Similar to databases in aviation and computer security,
the AIID proposes to adopt a two-tiered system for indexing AI incidents
(i.e., a harm or near harm event) and issues (i.e., a risk of a harm event).
Further, as some machine learning-based systems will sometimes produce a large
number of incidents, the notion of an incident “variant” is introduced. These
proposed changes mark the transition of the AIID to a new version in response
to lessons learned from editing 2,000+ incident reports and additional reports
that fall under the new category of “issue.”

In November of 2020, the AIID publicly launched as a collection of harms and
near harms realized in the real world involving intelligent systems [4]. Now
two years later, the database has expanded to more than 300 incidents
characterized by more than 2,000 incident reports. These indexing activities
have engendered considerable discussion around incident reporting as a
potential mechanism to bring about transparency, accountability, and
responsible governance of AI systems [8, 6, 2]. As the AIID organizes various
policy, corporate, and researcher stakeholder groups to collect and share AI
incidents, concept definitions play an imperative role in setting standards
and common understanding. As AI incident reporting is a fast developing
practice, definitions must simultaneously be implementable in future mandatory
and voluntary reporting frameworks [8], and accommodate uncertain and
incomplete reporting about incidents. To meet these requirements, we propose a
shift from a single tier (i.e., only index incidents), to a database
containing “incidents” and “issues” – where issues are a more permissively
defined category. Our proposed short definitions are stated as,

######  Definition 1 (AI Incident).

An alleged harm or near harm event where an AI system is implicated.

######  Definition 2 (AI Issue).

An alleged harm by an AI system that has yet to occur or be detected.

These two definitions draw inspiration from the Common Vulnerabilities and
Exposures (CVE) system [9], whereby an event (e.g., exposure of a web server
hosting private data) is treated separately from a risk (a known vulnerability
of the web server library). Exposures serve to motivate other parties to
respond to vulnerabilities, but interested parties should not require the
example of an exposure to take appropriate mitigating steps. Similarly, AI
issues serve as notices that an AI incident is likely to occur if the
community of AI system deployers does not take mitigating steps.

Tiered systems also present flexibility for different reporting requirements.
Aviation safety databases [7] separate a higher tier of “accidents” from a
lower tier of “incidents” based on whether there was injury, death, or
sufficient damages. In the United States, aviation incident reporting is
voluntary unless compelled by an interested authority, whereas accidents must
always be reported [1]. As regulatory bodies move to require AI incident
disclosure within their jurisdictions, a similar dichotomy is likely to
develop with different jurisdictions shifting the boundary between incidents
and issues. Further, in the event countries do enact penalties for failures to
disclose AI incidents, the existence of a reporting tier that does not presume
incident status provides incentives (e.g., waiver of penalties) for disclosure
first, and sorting out the classification later.

Without a second tier, settling the boundary between “incident” and database
rejection is controversial. The AIID received at least nine reports of facial
recognition systems deployed by Israeli security forces at Palestinian
checkpoints. These reports are yet to be ingested as incidents, pending a
known misidentification of a Palestinian by facial recognition. At that time
the accumulated reports can objectively be added to a new incident.
Alternatively, the AIID could treat the system’s existence as impacting the
Palestinian people’s rights to privacy and non-discrimination. When taking
this framing, Palestinians may also argue that the removal of the facial
recognition system from checkpoints may still not remove the harms imposed by
the human operators of the checkpoint – this candidate incident may fail the
requirement that the incident not occur in the absence of an AI system.
Categorizing these reports as AI issues would prevent these salient potential
harms from being totally excluded from the database.

Assume another challenging checkpoint scenario: in the event that all
misclassifications by the facial recognition model are reported, should all
misidentification events be considered different incidents? Indexing each
incident takes editor time and triggers downstream investigation and
classification effort. Human capacities to index incident data will quickly be
overwhelmed if manual processes are required for every discrete event. Herein
lies a tension between indexing incidents at depth with added insights and
indexing incidents at breadth to cover all occurrences of substantially
similar incidents. We address this tension with the introduction of incident
“variants” defined as,

######  Definition 3 (AI Incident Variant).

An incident that shares the same causative factors, produces similar harms,
and involves the same intelligent systems as a known AI incident.

The variant concept accommodates the multiplicity of events that are likely to
repeat, reducing the human labor required for subsequent event processing. In
the checkpoint example, each misidentification of a Palestinan by these
systems would result in an incident variant because it results from the same
system, goals, technical failings, harms, and harmed population.

Variants also extend to model types that produce thousands of unreported
incidents. Text-to-text (e.g., GPT-3 [3]) and text-to-image (e.g., Stable
Diffusion [5]) models produce incident variations with the same causative
factors and similar harms. These variants can be collected under the first
incident sharing similar factors and inform subsequent investigation.

While solving the multiplicity problem, variants introduce an identification
problem. Incidents currently in the AIID can be cited via their permanent URLs
(e.g., incidentdatabase.ai/cite/1), but the concept of “variant” makes it
difficult to cite specific events. Here we chose to expand the incident
identifier,

######  Definition 4 (Incident Identifier).

A universally unique identifier for the incident and its variants
(NAMESPACE:INCIDENT#.VARIANT#). The “namespace” refers to the numbering
authority,111Currently only “AIID”, but likely others (e.g., country codes) in
the future the “incident” refers to the AI incident number, and the “variant”
refers to the incident variant.

This identifier is backwards compatible since single number identifiers are
assumed to be the originally reported incident. Further, it presumes a future
where incident databases are federated and built to be interoperable via the
introduction of the namespacing. Already a variety of jurisdictions are
considering developing country-specific incident databases (e.g., follow on
efforts from [8]). Efforts must be made to ensure the hard won lessons within
each country are broadly shared. Starting with a standard by which incidents
are namespaced ensures incidents can continue to be referenced and indexed
without ambiguity.

These proposed changes result both from the experience indexing incidents and
operational necessities stemming from a changing policy environment. While
most incident reports are presently derived from popular press journalism of
AI misbehavior, we believe a world of high-volume incident reporting requires
terminology and processes to capture misbehavior comprehensively,
collaboratively, and practically. It is by advancing our sense of history that
we may build and govern a human-centered future with AI.

Acknowledgements

Kate Perkins gave valuable feedback on the contents of this work in addition
to her duties as an AIID incident editor. The definitions and discussions
presented within the paper are also greatly influenced by ongoing efforts by
the Organisation for Economic Co-operation and Development (OECD) to adopt a
shared definition of AI incident across all 38 member states. Finally, the
AIID is an effort by many people and organizations organized under the banner
of the Responsible AI Collaborative, including the Center for Security and
Emerging Technology (CSET), whose Zachary Arnold contributed to the first
incident criteria and definition. It is through the Collab’s collective
efforts that the ontological perspectives presented above have meaning and
real world importance.

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