larger models and as a result lead to continued comparable or even higher
energy use, in an instance of Jevon’s paradox.

####  2.6.3 Increasing inequality and negative effects on job quality

_Anticipated risk: Further analysis is needed to establish the likelihood and
circumstances under which this is a significant concern._

##### Problem

Advances in LMs, and the language technologies based on them, could lead to
the automation of tasks that are currently done by paid human workers, such as
responding to customer-service queries, translating documents or writing
computer code, with negative effects on employment.

###### Unemployment and wages

If LM-based applications displace employees from their roles, this could
potentially lead to an increase in unemployment (Acemoglu and Restrepo, 2018;
Webb, 2019), and other longer-term effects.

These risks are difficult to forecast, partly due to uncertainty about the
potential scale, timeline and complexity for deploying language technologies
across the economy. Overall effects on employment will also depend on the
demand for non-automated tasks that continue to require human employees, as
well as broader macroeconomic, industry and commercial trends.

##### Examples

For example, the US Bureau of Labour Statistics projected that the number of
customer service employees in the US will decline by 2029, as a growing number
of roles are automated (of Labor Statistics, 2021). However, despite
increasingly capable translation tools, the Bureau also projected that demand
for translation employees will increase rapidly, due to limitations in
automated translation technologies but also other factors such as increasing
demand for translation services due to demographic trends (of Labor
Statistics, 2021).

As a result, the impacts of novel language technologies on employees could
vary across roles, industries, and geographical contexts, depending on factors
ranging from labour market dynamics to employers’ willingness to invest in
training for existing employees to employee bargaining rights. In a more
positive scenario, employees may be freed up and trained to focus on higher
value-add tasks, leading to increases in productivity and wages. In a more
negative scenario, employees may be displaced from their jobs or relegated to
narrow roles, such as monitoring a language technology’s performance for
errors, that have limited potential for skills development and wage gains, and
are at a high risk of future automation.

##### Additional considerations

###### Exacerbation of income inequality

Evidence from initial AI applications and adjacent fields such as industrial
robotics (Lambert and Cone, 2019; Georgieff and Milanez, 2021), suggests that
while some job displacement from language technologies is likely, the risk of
widespread unemployment in the short- to medium-term is relatively low.

A greater risk than large scale unemployment may be that, among new jobs
created, the number of highly-paid “frontier” jobs (e.g. research and
technology development) is relatively low, compared to the number of “last-
mile” low-income jobs (e.g. monitoring the predictions of an LM application)
(Autor and Salomons, 2019). In this scenario, LMs may exacerbate income
inequality and its associated harms, such as political polarisation, even if
they do not significantly affect overall unemployment rates (Menasce Horowitz
et al., 2020; Ingraham, 2018).

###### Reductions in job quality

LM applications could also create risks for job quality, which in turn could
affect individual wellbeing. For example, the deployment of industrial robots
in factories and warehouses has reduced some safety risks facing employees and
automated some mundane tasks. However, some workers have seen an increase in
the pace of work, more tightly controlled tasks and reductions in autonomy,
human contact and collaboration (Gutelius and Theodore, 2019). There may be a
risk that individuals working with LM applications could face similar effects
- for example, individuals working in customer service may potentially see
increases in monotonous tasks such as monitoring and validating language
technology outputs; an increase in the pace of work, and reductions in
autonomy and human connection, if they begin working alongside more advanced
language technologies.

####  2.6.4 Undermining creative economies

_Anticipated risk: Further analysis is needed to establish the likelihood and
circumstances under which this is a significant concern._

##### Problem

LMs may generate content that is not strictly in violation of copyright but
harms artists by capitalising on their ideas, in ways that would be time-
intensive or costly to do using human labour. Deployed at scale, this may
undermine the profitability of creative or innovative work.

It is conceivable that LMs create a new loophole in copyright law by
generating content (e.g. text or song melodies) that is sufficiently distinct
from an original work not to constitute a copyright violation, but
sufficiently similar to the original to serve as a substitute, analogous to
‘patent-busting’ (Rimmer, 2013). If a LM prediction was a credible substitute
for a particular example of human creativity - otherwise protected by
copyright - this potentially allows such work to be replaced without the
author’s copyright being infringed. Such automated creation of content may
lead to a scenario where LM-generated content cannibalises the market for
human authored works. Whilst this may apply most strongly to creative works
(e.g. literature, news articles, music), it may also apply to scientific
works.

##### Examples

Google’s ’(VersebyVerse, )’ AI is a tool to help ‘you compose poetry inspired
by classic American poets’ (Holt, 2020). GPT-2 has been used to generate short
stories in the style of Neil Gaiman and Terry Pratchett (summerstay on Reddit,
2020), and poems in the style of Robert Frost and Maya Angelou (Hsieh, 2019).
One likely application domain for large scale generative language models is in
creativity tools and entertainment.

Distinctly, concerns of LMs directly reproducing copyrighted material present
in the training data have been raised and it is subject to ongoing legal
discussion whether this constitutes a copyright violation (Vézina and
Hinchcliff Pearson, 2021).

##### Additional considerations

While such ‘copyright-busting’ may create harm, it may also create significant
social benefit, for example, by widening access to educational or creative
material for a broader range of audiences. In patent law, the phenomenon of
‘patent-busting’ has been described to harm some, but create widespread social
benefit to other actors (Rimmer, 2013).161616Patent-busting occurs when an
innovation is made that is sufficiently similar to capture the market of the
original invention, but is sufficiently distinct not to constitute a patent
violation. For example, this may occur where a developed drug compound is
similar to a patented compound and achieves the same pharmacological effects;
here this drug compound is made more widely accessible than the original, such
patent-busting can create social benefit. The distribution of potential harm
and benefit from analogous ‘copyright-busting’ merits further consideration.

####  2.6.5 Disparate access to benefits due to hardware, software, skill
constraints

_Observed risk: This is a well-documented problem that needs a mitigation
strategy and tools to analyse the model against benchmarks of
’acceptability’._

##### Problem

Due to differential internet access, language, skill, or hardware
requirements, the benefits from LMs are unlikely to be equally accessible to
all people and groups who would like to use them. Inaccessibility of the
technology may perpetuate global inequities by disproportionately benefiting
some groups. Language-driven technology may increase accessibility to people
who are illiterate or suffer from learning disabilities. However, these
benefits depend on a more basic form of accessibility based on hardware,
internet connection, and skill to operate the system (Sambasivan and Holbrook,
2018).

The uneven distribution of benefits and risks from novel technologies is a
more general phenomenon that can be observed with almost any breakthrough
technology (Stilgoe, 2020). It is not a unique challenge to LMs. Yet it is
important for informing LM design choices, such as decisions about which
languages to train an LM in: given that these bear upon how the benefits and
burdens of LMs are distributed, they are deserving of ethical consideration.
Normative considerations of justice bear upon the global distribution of
benefit and risk from LMs, something that is discussed in more detail in
(Bender et al., 2021).

##### Examples

###### Access to economic opportunities

LM design choices have a downstream impact on who is most likely to benefit
from the model. For example, product developers may find it easier to develop
LM-based applications for social groups where the LM performs reliably and
makes fewer errors; potentially leaving those groups for whom the LM is less
accurate with fewer good applications (see Lower performance by social group).
Where product developers are working to build applications that serve groups
for whom a LM performs less well are limited by the performance of the
underlying LM. This may create a feedback loop whereby poorer populations are
less able to benefit from technological innovations - reflecting a general
trend whereby the single biggest driver of increasing global income inequality
is technological progress (Jaumotte et al., 2013).

## 3\. Discussion

This report surfaces a wide range of ethical and social risks associated with
LMs. Many of these risks are important and need to be addressed. We believe
that, in each case, there are feasible paths to mitigation. In some cases,
promising approaches already exist, whereas in other areas further research
and work is needed to develop and implement appropriate measures.

In general, the successful mitigation of risks requires:

  1. 1.

> Understanding the point of origin of a risk and its connections and
> similarities to other risks,

  2. 2.

> Identifying appropriate mitigation approaches,

  3. 3.

> The clear allocation of responsibility and implementation of corrective
> measures.

In this section, we discuss each of these aspects in more detail.

###  3.1 Understanding the point of origin of a risk

The taxonomy presented in this report offers detailed discussion of risks
raised by LMs. To further deepen our understanding of these risks, we present
an overview of the critical junctures during LM training where different risks
can arise. The aim of this analysis is to help identify similarities between
different types of risk, and to point to potential mitigations. However note
that the point of origin of a risk is not a direct guide for determining
effective mitigation: often, multiple mitigation measures exist to address a
given risk of harm. Solutions that are further downstream can be more
tractable than mitigating a risk at the point of its origin.

###### Curation and selection of training data

As noted in 2.1 Discrimination, Exclusion and Toxicity and 2.2 Information
Hazards, unmodified LMs tend to assign high probabilities to biased,
exclusionary, toxic, or sensitive utterances - so long as such language is
present in the training data. The formal objective of language modeling is to
accurately represent language from the training corpus (see Definitions). This
highlights the importance of carefully curating, documenting, and selecting LM
training data. Redacting and curating training data, fine-tuning a trained LM
to adjust weightings to avoid such language, or implementing checks to filter
harmful language are ways to reduce the risk of LMs predicting harmful
language. Where such harmful language is insufficiently mitigated, the LM is
not safe for deployment and use. This is discussed in more detail in
Underrepresented groups in the training data and Training datasets elevate
some perspectives over others.

###### Robustness of LM

As noted in 2.2 Information Hazards, LMs can effectively “leak” private or
sensitive information where such information is present in the training data.
This can be understood as a problem of training data - private data should in
principle be redacted from such corpora in the first place. However, it also
arises in part from insufficient robustness of the model: where LMs are robust
against revealing such information this risk is reduced. Work toward such
robustness focuses on algorithmic tools used during the training of the LM,
such as differential privacy methods (Abadi et al., 2016; Ramaswamy et al.,
2020).

###### LM formal structure and training process

As discussed in 2.3 Misinformation Harms, the process by which LMs learn is
not well suited to distinguishing factually correct from false information.
Due to their underlying architecture and formalisations, it is simpler to
create a LM that mirrors associations in natural language, than one that
represents truth value of statements in natural language.

###### Computational cost of training and inference

As noted in 2.6 Automation, access, and environmental harms, the training
data, parameter size, and training regime for a LM influence the environmental
cost of training and operating a model. Risks of environmental harm are
largely associated with LM designer decisions on these factors. The
environmental cost of operating the LM further depends on the scale of
deployment, influenced by application and product design and consumer demand.

###### Intentional use or application of LMs

As noted in 2.4 Malicious Uses and 2.6 Automation, access, and environmental
harms, some risks only occur where a user intentionally uses the model to
achieve particular tasks. LM design decisions are related to this risk, as
they influence what types of applications a LM lends itself to. At the stage
of scoping potential applications, it is worth asking whether a given
technology is anticipated to be net beneficial - or whether it may cause harm
when performing with high accuracy, such as certain kinds of surveillance
tools, in which the application overall should be called into question
(Benjamin, 2020). Responsible publication norms and considerations of
accessibility are also key, as they determine who can develop LM use cases or
applications (Solaiman et al., 2019). Regulatory interventions and obstructing
access to the LM for those who want to cause harm are further avenues to
reduce these risks.

###### Accessibility of downstream applications

As noted in 2.1 Discrimination, Exclusion and Toxicity, especially on Lower
performance by social group and 2.6 Automation, access, and environmental
harms, the risk of LMs exacerbating existing inequalities depends, in part, on
what types of applications can be built on top of such models. This, too,
depends on design decisions. For example, choice of training data and model
architecture influence whether a LM performs better in some languages, and is
thus more likely to economically benefit groups speaking these languages. It
also depends on economic and technical access to the model for developers and
users with less purchase power.

###  3.2 Identifying and implementing mitigation approaches

Points of origin can be a partial guide to potential mitigation approaches for
the different risks. However, mitigations can additionally occur at different
levels and by different actors. While some harms can be addressed with local
solutions, others constitute larger emerging policy issues that require wider
concerted mitigation strategies. For example, the risk of a conversational
agent personifying harmful stereotypes can be addressed locally, by product
designers who ensure that a conversational agent does not perpetuate
stereotypes such as being (“female”, “submissive”) (see Promoting harmful
stereotypes by implying gender or ethnic identity). The risk of misinformation
on the other hand, is entrenched in the societal context where a LM is used
and linked to the wider policy issue of ensuring resilience of public
discourse against widespread misinformation (see 2.3 Misinformation Harms). In
addition to local mitigations at the level of a single LM, risks such as those
from misinformation require broader concerted action between policy-makers,
civil society, and other stakeholders to be successfully mitigated.

Such mitigations include:

  * •

> Social or public policy interventions, e.g. the creation of regulatory
> frameworks and guidelines

  * •

> Participatory projects, e.g. to create better datasets

  * •

> Technical research, e.g. to build more robust LMs

  * •

> AI Ethics and NLP research, e.g. to build better benchmarks and fine-tuning
> datasets

  * •

> Operational solutions, e.g. limited release of a model or funding of
> particular applications

  * •

> Research management, e.g. pivoting toward particular aspects of LM research

  * •

> Product design, e.g. user interface decisions on digital assistants.

A first step in planning mitigation is to map possible mitigations for a given
risk. Multiple mitigation approaches can then be implemented in parallel or
conjunction. Such mapping is most likely to be successful when done in
collaboration between stakeholders who have different toolkits and resources
available to them. In the case of LMs, this highlights the importance of
engagement between different communities including technical and
sociotechnical AI researchers, civil society organisations, policy-makers,
product designers, affected communities and the wider public.

###### Model explainability and interpretability

It is well known that many machine learning models are intrinsically opaque
((Lipton, 2018; Doshi-Velez and Kim, 2017)); this means that it is not easy
for humans, no matter how skilled, to easily understand why and how a specific
algorithmic output is generated. Various scholars have suggested that
explainability and interpretability of AI systems is critical to ensure these
systems are fair, ethical and safe (Gunning et al., 2019; Miller, 2019),
though it remains an open challenge to define what constitutes a good
explanation (Coyle and Weller, 2020; Kasirzadeh, 2021). Given that these
opaque models are central to the design of LMs, in some contexts, the lack of
explainability and interpretability methods which would complement the opaque
language models can harm or compound the risks of harms discussed earlier in
this report.

For example, suppose a person is unfairly discriminated against by a language
technology, as discussed in 2.1 Discrimination, Exclusion and Toxicity. If the
underlying LM of this technology is not appropriately interpretable or
explainable, the victim is unable to obtain an appropriate justification or
reason for the discrimination in order to seek recourse (Vredenburgh, 2021).
Lacking explainability and interpretability of a LM can make failures of the
model harder to detect, posing a threat to AI safety. It can also obscure the
true capabilities of a model, leading users of such models to overestimate
these capabilities, and making it harder for product developers and regulators
to assess inappropriate use cases of such models (see Anthropomorphising
systems can lead to overreliance or unsafe use).

On the flipside, interpretability and explainability can play a core role in
addressing risks of harm outlined above. Tracing a given output or harm to its
origins in the model can be key to addressing and mitigating such harms (see
also the section on Understanding the point of origin of a risk). There is
even some hope that LMs may be useful for improving explainability in other
types of AI systems, for example by helping to generate explanations that are
accessible and somewhat personalised to a person’s level of knowledge (for an
elaboration of such types of explanations see (Miller, 2018)).

A range of tools has been proposed and discussed to make AI systems, and
specifically NLP and language models, more explainable and interpretable (for
reviews see (Belinkov and Glass, 2019; Bommasani et al., 2021; Linardatos et
al., 2021)). This work is crucial for the responsible innovation of LLMs. It
remains a work in progress, as better explainability and interpretability
tools and methods are needed (see also Risk assessment frameworks and tools).

###### Mitigations need to be undertaken in concert

One goal in breaking the risks down into separate items in the presented
taxonomy is to make it more tractable to address individual risks in the
future. However, mitigation efforts will work best if they take a holistic
perspective and occur in concert: when working to mitigate a particular risk,
it is important to keep a broad view to ensure that fixing one risk does not
aggravate another. For example, methods to reduce toxic speech from LMs have
been found to bias model prediction against marginalised groups (Welbl et al.,
2021; Xu et al., 2021). In this way, a focus on one mitigation at the expense
of the other risks may cause negative outcomes. Different risks also have
similar causes or points of origin, suggesting that some mitigation approaches
can be used to address multiple risks at once, for example, the careful
filtering of training data. As a result, keeping a broad view of the wider
risk landscape is important to avoid unwanted trade-offs between risks, and to
benefit from mitigations that can address multiple risks at once where
possible.

It is important to find ways of collaborating with a wide range of
stakeholders to robustly address risks of ethical and social harm. Adjacent
fields demonstrate that mitigating risks is more robust when done in
collaboration of different communities who understand the risks at play
(Stilgoe et al., 2013) and have capacities to implement such mitigations.

###  3.3 Organisational responsibilities

Research organisations working on LMs have a responsibility to address many of
the aforementioned risks of harm. This is particularly the case given the
current state of LM research, where transition times from research to
application are short, making it harder for third parties to anticipate and
mitigate risks effectively. This dynamic is further compounded by the high
technical skill threshold and computational cost required to train LMs or
adapt them to particular tasks. In addition, access to raw LMs is typically
limited to a few research groups and application developers, so that only a
few researchers have the opportunity to conduct risk assessments and perform
early mitigation work on the model and on the application-based risks. Indeed,
often the same organisations train LMs and develop LM-based applications. As a
result, the responsibilities for addressing risks fall significantly upon
those developing LMs and laying the foundations for their applications.

## 4\. Directions for future research

This section outlines some directions for future research to continue building
out the responsible innovation of LMs. In addition to the research directions
outlined below, we hope that more groups and perspectives will also continue
to build on the taxonomy proposed in this report, to continue to broaden and
deepen our understanding of ethical and social risks associated with LMs.

###  4.1 Risk assessment frameworks and tools

Analysing and evaluating a LM regarding the above risks of harm requires
innovation in risk assessment tools, benchmarks and frameworks (Tamkin et al.,
2021; Raji et al., 2020). Many risks identified in this report are not
typically analysed in LMs. Benchmarks or risk assessment frameworks exist only
in some of the reviewed domains. Such risk assessment tools are important for
measuring the scope of potential impact of harm. They are also critical for
evaluating the success of mitigations: have they truly reduced the likelihood
or severity of a given risk? Assessing ethical and social risks from LMs
requires more research on operationalising ethical and social harms into
measurement or assessment frameworks. Developing robust benchmarks is complex
(Welbl et al., 2021) and may work best when complemented by other experimental
or qualitative evaluation tools.

###### Expanding the methodological toolkit for LM analysis and evaluation

Risk assessment requires expanding beyond the methodologies traditionally used
to evaluate LMs, LAs and LTs. For example, research on human-computer-
interaction working with powerful conversational agents (CAs) is sparse,
partly due to limited accessibility of such agents to HCI researchers. As
discussed in 2.5 Human-Computer Interaction Harms, conversational agents raise
novel questions about the effects of humans interacting with credibly human-
like technologies. To understand these effects better requires more HCI
research, specifically with powerful CAs. Similarly, ethnographic research is
not standardly part of the LM evaluation toolkit, but is critical for
surfacing and tracing risks from LTs in particular embedded settings, as
exemplified in an ethnographic study of predictive policing tools in the New
Delhi police force (Marda and Narayan, 2021).

###  4.2 Technical and sociotechnical mitigation research

The risks outlined in this report require mitigation. Great strides have been
made in developing risk mitigation tools, including by (Welbl et al., 2021;
Solaiman and Dennison, 2021; Dinan et al., 2021; Chen et al., 2021a) and
others mentioned in the above taxonomy. However, mitigation work is work in
progress. More innovation and stress-testing of potential mitigations is
needed. For example, more inclusive and scalable pipelines for dataset
curation are needed (see Curation and selection of training data). Similarly,
more work on robustness against leaking private information is needed (see
Risks from leaking or correctly inferring sensitive information). More tools
for fine-tuning LMs to mitigate social or ethical risks are also needed (see
Risk assessment frameworks and tools). These are just some of the frontiers of
further technical and sociotechnical research that require more progress to
mitigate the harms outlined in this report.

###  4.3 Benchmarking: when is a model “fair enough”?

Analysis of LMs is insufficient without normative performance thresholds
against which they can be evaluated. Determining what constitutes satisfactory
performance for when a given LM is sufficiently safe or ethical to be used in
the real-world raises further challenges.

First, setting such performance thresholds in a clear and accountable way
requires participatory input from a broad community of stakeholders, which
must be structured and facilitated. Second, views on what level of performance
is needed are likely to diverge - for example, people hold different views of
what constitutes unacceptable “toxic speech” (Kocoń et al., 2021). This raises
political questions about how best to arbitrate conflicting perspectives
(Gabriel, 2020a), and knock-on questions such as who constitutes the
appropriate reference group in relation to a particular application or
product. Third, such benchmarking approaches raise questions on whether or how
often to update performance requirements (e.g. to avoid the ‘value lock-in’
discussed in the section on Exclusionary norms). Further research is required
to address these questions.

Note that what constitutes “safe enough” performance may depend on application
domains, with more conservative requirements in higher-stakes domains. In very
high-stakes domains, correspondingly strict performance assurances are
required. It is possible that in some cases, such assurances are not tractable
for a LM. Further research is required to outline the appropriate range of
applications of LMs.

###  4.4 Benefits and overall social impact from LMs

This report focuses on risks from LMs. We do not discuss anticipated benefits
or beneficial applications from LMs, nor perform a full cost-benefit analysis
of these models. Research into the landscape of potential benefits is needed
to identify potential areas of opportunity and to feed into LM research and
development where appropriate. Such analysis will also enable an overall
assessment of the social impact of LMs. The authors of this report see
tremendous potential in LMs to spur future research and applications, ranging
from near-term applications (Pilipiszyn, 2021; NLP for Positive Impact, 2021)
to more fundamental contributions to science, for example, as LMs are used to
better understand how humans learn language. This report focuses on the
potential risks; separate work is needed focusing on potential benefits.

## 5\. Conclusion

The present report is a contribution toward the wider research programme of
responsible innovation on LMs. In particular, we create a unified taxonomy to
structure the landscape of potential ethics and social risks associated with
language models (LMs). Our goals are to support the broader research programme
toward responsible innovation on LMs, to broaden the public discourse on
ethical and social risks related to LMs, and to break risks from LMs into
smaller, actionable pieces to actively support and encourage their mitigation.
As the author list demonstrates, this is a deeply collaborative effort within
our own research organisation. More expertise and perspectives will be
required to continue to build out this taxonomy of potential risks from LMs.
Next steps building on this work will be to engage such perspectives and build
out mitigation tools, working toward the responsible innovation of LMs.

### Acknowledgements

The authors thank Phil Blunsom, Shane Legg, Jack Rae, Aliya Ahmad, Richard
Ives, Shelly Bensal and Ben Zevenbergen for comments on earlier drafts of this
report.

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##  Appendix A Appendix

###  A.1 Definitions

####  A.1.1 Language Models

Language Models (LMs) are machine learning models that are trained to
represent a probability distribution $p(w)$ over sequences of utterances $w$
from a pre-specified domain (letters, words, sentences, paragraphs,
documents). LMs aim to capture statistical properties of the sequences of
utterances present in their training corpus and can be used to make
probabilistic predictions regarding sequences of utterances (Bengio, 2008).
Typical training corpora for LMs contain natural language (e.g. collected from
the web), but LMs can also be trained on other types of languages (e.g.
computer programming languages). Moreover, LMs can serve different purposes,
such as generating language (generative language models) or providing semantic
embeddings. Depending on the primary purpose of a LM, slightly different
architectures and training objectives can be used. In this paper, unless we
specify otherwise, we focus on LMs tailored to language generation.

A standard approach to construct generative LMs is to use an autoregressive
decomposition that sequentially proposes a probability distribution for the
next utterance based on past utterances:

$\displaystyle p(w)=p(w_{1})\cdot p(w_{2}|w_{1})\cdots
p(w_{T}|w_{1},\ldots,w_{T-1})\enspace.$

Here $w=w_{1}\ldots w_{T}$ is a sequence of $T=|w|$ utterances. Each of the
terms $p(w_{t}|w_{1},\ldots,w_{t-1})$ with $t=1,\ldots,T$ represents the
probability the model assigns to observing the particular utterance $w_{t}$
given the previous $t-1$ utterances. LMs of this form are trained by updating
the parameters controlling these conditional probabilities to assign high
likelihood to sequences of utterances observed in the training corpus.
Training is the result of an iterative process whereby at each iteration the
model is presented with a batch of utterances and its parameters are updated
to increase the likelihood of that particular set of utterances. Training
large-scale language models can require very high numbers of iterations,
requiring significant computing power.

Recent LMs are primarily distinguished from other LMs due to their parameter
size and training data. Their size allows LMs to retain representations of
extremely large text corpora, resulting in much more general sequence
prediction systems than prior LMs. In this report, we focus on such large-
scale models, connoted as LMs. The emergence of LMs is described in detail in
the section on a brief history of “Large” Language Models.

Note that LMs do not output text directly. Rather, they produce a probability
distribution over different utterances from which samples can be drawn. Greedy
decoding directly from the (conditional) probability distribution provided by
an LM is possible, but often performs poorly in practice. Instead, methods
that focus on the most likely utterances – while introducing a small amount of
variability (e.g. beam search and nucleus sampling (Holtzman et al., 2020)) –
have been found to produce better results in practice (Brown et al., 2020)).
LMs typically aim to mirror language found in the training data. However, they
can also be optimised toward other tasks or objectives. For example, a LM can
be optimised for dialogue, by predicting utterances that are most appropriate
to maintain a conversation.

####  A.1.2 Language Agents

Language agents (LAs) are machine learning systems that are restricted to
providing only natural language text-output (Kenton et al., 2021). LAs may
generate text-output based on LM predictions. LAs that are optimised to engage
a person in direct dialogue are also referred to as “Conversational Agents”
(CAs) (Perez-Marin and Pascual-Nieto, 2011).

####  A.1.3 Language Technologies

LMs can be used in language technologies (LTs) such as voice assistants
including Siri (Apple), Google Assistant (Google), or Alexa (Amazon), text
generation tools such as AutoCorrect or SmartReply, and translation and
summarisation tools. Language technologies can serve different purposes, for
example providing information, entertainment, or productivity aids to the
user.

Powerful large language models (LLMs) may lead to improved versions of
existing language technologies. However, they may also make new types of
language technology possible. For example, they may create conversational
interfaces with human users where the use of this technology is
indistinguishable from interaction with a human counterpart. Such applications
are discussed in more detail in section V. Human-Computer Interaction Harms.

###### Distinguishing “statistical bias” from “social bias”

Concerns regarding “bias” in language models generally revolve around
distributional skews that result in unfavourable impacts for particular social
groups (Sheng et al., 2021). We note that there are different definitions of
“bias” and “discrimination” in classical statistics compared to sociotechnical
studies. In classical statistics, “bias” designates the difference between a
model’s prediction and the ground truth (Dietterich and Kong, 1995); in
machine learning, minimising statistical bias is a component of reducing error
(Dietterich and Kong, 1995). In sociotechnical studies, “bias” refers to skews
that lead to unjust discrimination based on traits such as age, gender,
religion, ability status, whether or not these characteristics are legally
protected (Blodgett et al., 2020). Developing mechanisms to quantify the
latter type of bias is an area of active research, where qualitative and
quantitative measures have been established (Barocas et al., 2019; Hardt et
al., 2016).

###### Distinguishing statistical from sociotechnical notions of
“discrimination”

Similarly, the definition of “discrimination” is multiplicitous. Traditionally
in machine learning, this term refers to making distinctions between possible
categories or target classes (Bowker and Star, 1999). In sociotechnical work,
“discrimination” refers to unjust differential treatment, typically toward
historically marginalised groups. Various steps in training a machine learning
model can result in discrimination in the sociotechnical sense, from labelling
and collection of the training data, to defining the “target variable” and
class labels, to selecting features (Barocas and Selbst, 2016).

###  A.2 References Table

_Table 2. References providing evidence for each risk covered in this report._

Risk |  Evidence in NLP, _Evidence in LMs (GPT-2, GPT-3, T5, Gopher)_
---|---
Discrimination, Exclusion and Toxicity
2.1.2 Social stereotypes and unfair discrimination |  Caliskan et al. (2017); Blodgett et al. (2020); Zhao et al. (2017); Ferrer et al. (2020); Dodge et al. (2021) _Abid et al. (2021); Lucy and Bamman (2021); Nadeem et al. (2020); Nangia et al. (2020); Nozza et al. (2021); Huang et al. (2020)_
2.1.3 Exclusionary norms |  Cao and Daumé III (2020)
2.1.4 Toxic language |  Gorwa et al. (2020); Duggan (2017); Gehman et al. (2020); Luccioni and Viviano (2021) _Wallace et al. (2020); Rae et al. (2021)_
2.1.5 Lower performance by social group |  Joshi et al. (2021); Ruder (2020); Blodgett et al. (2016); Koenecke et al. (2020); Blodgett and O’Connor (2017) _Winata et al. (2021)_
Information Hazards
2.2.2 Compromise privacy by leaking private information |  Kim (2021); Dobberstein (2021) _Carlini et al. (2021)_
2.2.3 Compromise privacy by correctly inferring private information |  Garcia et al. (2018); Park et al. (2015); Makazhanov et al. (2014); Preoţiuc-Pietro et al. (2017); Morgan-Lopez et al. (2017); Nguyen et al. (2013); Golbeck (2018)
2.2.4 Risks from leaking or correctly inferring sensitive information |  _Wallace et al. (2020)_
Misinformation Harms
2.3.2 Disseminating false or misleading information |  Wang et al. (2019b); Allcott et al. (2019); Krittanawong et al. (2020) _Rae et al. (2021); Lin et al. (2021); Zhang et al. (2021b); Branwen (2020); Dale (2021); Lacker (2020)_
2.3.3 Causing material harm by disseminating misinformation e.g. in medicine or law |  _Quach (2020)_
2.3.4 Leading users to perform unethical or illegal actions |  _Hendrycks et al. (2021)_
Malicious Uses
2.4.2 Making disinformation cheaper and more effective |  Zellers et al. (2020); Schneier (2020); Hampton (2021); Mann (2021) _Hao (2020); McGuffie and Newhouse (2020)_
2.4.3 Facilitating fraud and impersonation scams |  Lewis et al. (2017); Van Noorden (2014)
2.4.4 Assisting code generation for cyber attacks, weapons, or malicious use |  _Chen et al. (2021a)_
2.4.5 Illegitimate surveillance and censorship |  Shahbaz and Funk (2019)
Human-Computer Interaction Harms
2.5.2 Anthropomorphising systems can lead to overreliance or unsafe use |  Kim and Sundar (2012)
2.5.3 Create avenues for exploiting user trust to obtain private information |  Van den Broeck et al. (2019); Lewis et al. (2017); Ischen et al. (2019)
2.5.4 Promoting harmful stereotypes by implying gender or ethnic identity |  Cercas Curry et al. (2020); Hwang et al. (2019); Zdenek (2007); Marino (2014)
Automation, access, and environmental harms
2.6.2 Environmental harms from operating LMs |  Strubell et al. (2019) _Bender et al. (2021); Patterson et al. (2021)_
2.6.3 Increasing inequality and negative effects on job quality |
2.6.4 Undermining creative economies |  Hsieh (2019)
2.6.5 Disparate access to benefits due to hardware, software, skill constraints |  _Bender et al. (2021)_