# A Standardized Machine-readable Dataset Documentation Format
for Responsible AI

Nitisha Jain1*, Mubashara Akhtar1*, Joan Giner-Miguelez2*, Rajat Shinde3*,
Joaquin Vanschoren6, Steffen Vogler7, Sujata Goswami11, Yuhan Rao13, Tim
Santos14, Luis Oala8, Michalis Karamousadakis12, Manil Maskey9, Pierre
Marcenac4, Costanza Conforti4, Michael Kuchnik10,
Lora Aroyo4*, Omar Benjelloun4*, Elena Simperl1, 5*

###### Abstract

Data is critical to advancing AI technologies, yet its quality and
documentation remain significant challenges, leading to adverse downstream
effects (e.g., potential biases) in AI applications. This paper addresses
these issues by introducing Croissant-RAI, a machine-readable metadata format
designed to enhance the discoverability, interoperability, and trustworthiness
of AI datasets. Croissant-RAI extends the Croissant metadata format and builds
upon existing responsible AI (RAI) documentation frameworks, offering a
standardized set of attributes and practices to facilitate community-wide
adoption. Leveraging established web-publishing practices, such as Schema.org,
Croissant-RAI enables dataset users to easily find and utilize RAI metadata
regardless of the platform on which the datasets are published. Furthermore,
it is seamlessly integrated into major data search engines, repositories, and
machine learning frameworks, streamlining the reading and writing of
responsible AI metadata within practitioners’ existing workflows. Croissant-
RAI was developed through a community-led effort. It has been designed to be
adaptable to evolving documentation requirements and is supported by a Python
library and a visual editor.

## Introduction

Data is a crucial element in emergent AI technologies and plays a central role
in training and evaluating models. Recent research has emphasized how data can
unexpectedly lead to harmful outcomes in AI applications, highlighting the
real-world consequences of data-related issues. For instance, recent studies
have revealed gender-biased AI classifiers in computer-aided diagnosis as a
direct result of imbalances in training data (Larrazabal et al. 2020), and
issues during data collection that can cause AI applications, which are
designed to detect pneumonia, to fail when deployed to different hospitals
(Zech et al. 2018).

Consequently, the community focused on responsible AI (RAI) has identified
data work as critical to the development of trustworthy AI systems (Smuha
2019; Oala et al. 2024). Seminal works, such as Datasheets for Datasets (Gebru
et al. 2021) and Data Statements (Bender and Friedman 2018) have emphasized
the importance of dataset documentation to assess and increase the
trustworthiness of AI systems. Data needs to be documented, analyzed, and
enriched (e.g., against biases), and maintained.

While the research community has made significant progress in publishing and
sharing datasets, most datasets are still one-time, expensive efforts
(Sambasivan et al. 2021). Formats for documenting AI data can be a rich source
of information, but it is becoming increasingly clear that they need to evolve
(Yang, Liang, and Zou 2024). Current proposals have overlapping formats,
require data documentation written in natural language, lack a standard
structure and are not integrated with widely used tools and ML frameworks,
making them difficult for machines to read and process.

Table 1: Dataset documentation toolkits used as base for the vocabulary design
Toolkit |  Description
---|---
HuggingFace Dataset Cards111https://huggingface.co/docs/hub/en/datasets-cards |  HuggingFace encourage data authors to publish natural text reports following a similar structure as Datasheets for Datasets (Gebru et al. 2021).
Kaggle metadata222https://github.com/Kaggle/kaggle-api/wiki/Dataset-Metadata |  Kaggle platform allows users to upload composition and contextual metadata along datasets.
Data Nutrition Labels (Holland et al. 2018) |  The Data Nutrition Project aims to create a standard label for interrogating datasets, inspired by Datasheets for Datasets.
Data Cards (Pushkarna, Zaldivar, and Kjartansson 2022) |  Data Cards are structured summaries of critical details about various elements of ML datasets that participants need throughout a project’s lifescycle for ethical AI development.
Croissant (Akhtar et al. 2024) |  Croissant is a metadata format for machine learning datasets that combines metadata, resource file descriptions, data structure, and default ML semantics into a single file.
Crowdworksheets (Díaz et al. 2022) |  A framework to facilitate transparent documentation of key decisions points at various stages of the annotation pipeline focusing on crowdworkers.
Fairness Datasets Ontology333https://fairnessdatasets.dei.unipd.it/schema/ |  The Fairness Datasets Ontology clearly outlines the relationships among various fields in the data briefs and explicitly details the connections to external vocabularies.
DescribeML (Giner-Miguelez, Gómez, and Cabot 2023) |  DescribeML is a domain-specific language (DSL) to precisely describe machine learning datasets in terms of their structure, provenance, and social concerns.

This work addresses these issues by introducing Croissant-RAI, a machine-
readable format for capturing and publishing RAI-related data documentation.
It consists of a set of attributes444In this manuscript, the terms attributes
and properties are used interchangeably. Typically, in Computer Science,
attributes refer to additional information, whereas properties describe the
characteristics of the object. However, in English, both have similar
connotations in terms of defining characteristics of a real-world concept.
organized around RAI use cases and related documentation to promote community-
wide adoption. It builds on and complements existing RAI dataset documentation
proposals, making publishing, discovering, and reusing existing RAI
documentation easier. Croissant-RAI is an extension of the Croissant metadata
format (Akhtar et al. 2024). Croissant is built on established web-publishing
practices, such as Schema.org (Guha, Brickley, and Macbeth 2016), and designed
to improve ML datasets’ discoverability, portability, reproducibility, and
interoperability. It makes datasets discoverable by data search engines and
enables loading them directly into ML frameworks and tools.

Croissant-RAI was developed through a multi-step vocabulary engineering
process based on recent RAI data documentation frameworks and inspired by
practical use cases. These use cases cover data design and collection
processes, human and machine data labeling, data participatory processes, AI
safety and fairness assessments, and data regulatory compliance with current
AI regulations. Croissant-RAI is governed by a community-led effort and
intends to be a dynamic asset for addressing new documentation requirements.
Finally, Croissant-RAI is accompanied by a Python library and a visual web
editor to assist users during its usage.

The rest of the paper is organized as follows: First, we discuss background
related to machine-readable metadata, the Croissant format, and state-of-the-
art RAI documentation frameworks. Then, we present the vocabulary engineering
process and the use cases central to Croissant-RAI. We further introduce the
Croissant-RAI vocabulary together with dataset examples demonstrating its use.
Finally, we present the tool support around Croissant-RAI and discuss the
implications of this work.

## Background

Figure 1: The Multi-step Vocabulary Engineering Process for Croissant-RAI

This section discusses related background on machine-readable metadata, the
Croissant metadata format, and responsible AI.

### Machine-readable metadata

As more data becomes available online, the scientific community has long
focused on how to make this data discoverable and easy to use. To accomplish
this, the standard approach is to supplement the published data with the
relevant machine-readable metadata. This metadata may include information
about the data’s repository, authors, or specific tags, expressed in a format
that sticks to consistent structures to facilitate machine computation. For
example, the Data Catalog Vocabulary (DCAT) (Albertoni et al. 2023) is a
metadata format that enables interoperability between web-based data catalogs,
allowing users to automatically aggregate, classify, and filter data.
Institutions like the European Union use this standard to develop open data
portals that automatically integrate data from multiple sources. These portals
ensure data compliance with the FAIR principles (Wilkinson et al. 2016),
demonstrating the benefits and practical applications of machine-readable
metadata.

Other metadata standards aim to improve the content discoverability across the
web. For example, Schema.org (Guha, Brickley, and Macbeth 2016) is a de facto
metadata standard designed to help search engines discover structured content
on the Web. Search engines can find and index published content described with
Schema.org, increasing understandability of content. Furthermore, Schema.org
is a versatile format that can describe a variety of content. For instance,
content described with Schema.org/Dataset555http://schema.org/Dataset
extension is understood and indexed as a dataset by search engines like Google
Dataset Search666https://datasetsearch.research.google.com/.

### Croissant: A metadata format for ML-ready datasets

Machine-readable metadata formats are gaining popularity in the ML community.
Croissant (Akhtar et al. 2024) is a new metadata format for ML-ready datasets,
developed through a community-led effort involving both industry and academia.
Croissant is designed to improve discoverability, portability and
reproducibility of ML datasets. Datasets described with Croissant and
published online can be easily discovered by current data search engines,
regardless of their publication location. Additionally, Croissant prepares
datasets for ML use cases by allowing them to be directly loaded into
frameworks like PyTorch, TensorFlow, or JAX, streamlining data loading across
frameworks.

Croissant extends Schema.org with additional layers that comprehensively
describe datasets’ attributes, resources, structure, and semantics to
facilitate their use and sharing within the ML community. The Croissant format
does not change dataset content representation (e.g., image or text file
formats) but provides a standard way to describe and organize data,
facilitating adoption by a broad community.

### Responsible AI data documentation

Over the past years, data work is increasingly seen as critical to the
development of trustworthy AI systems, considering aspects related to
accountability, fairness, transparency, data privacy and governance,
robustness and safety (Smuha 2019). While the field has seen progress in data
publishing and sharing, more often than not data work is considered as a one-
off effort. To address its potential negative downstream effects, data needs
to be maintained, documented, analysed and enriched (e.g. against biases)
(Sambasivan et al. 2021).

Previous works have emphasized the importance of dataset documenting to
develop fairer and more trustworthy AI applications. Documentation efforts
such as Datasheets for Datasets (Gebru et al. 2021), Data Nutrition Labels
(Holland et al. 2018), and Data Statements (Bender and Friedman 2018) have
encourage data publishers to document data dimensions that may affect dataset
use or the quality of resulting ML models. Seminal works on data documentation
(Bender and Friedman 2018; Gebru et al. 2021) have inspired the development of
various tools for data documentation (see overview in Table 1). For example,
Huggingface Dataset Cards and Data Nutrition Labels have been inspired by
Datasheets for Datasets which documents a dataset’s motivation, creation,
composition, intended uses, distribution, maintenance, and further information
(Gebru et al. 2021).

However, these approaches and tools require data documentation primarily
written in natural language, lack a standard structure and miss integration
with widely used tools and ML frameworks. This makes data documentation
difficult for machines to read and process. Moreover, the proposed dimensions
do not support discoverability by search engines and are challenging to
integrate in frameworks and tools used across the entire life-cycle of an AI
system. This work addresses these issues by proposing a machine-readable
framework for capturing RAI data documentation practices as an extension of
the Croissant format, making RAI-related data documentation more discoverable,
maintainable and interoperable.

## Croissant-RAI Vocabulary Engineering

This section presents the details of the vocabulary design and engineering
process for Croissant-RAI format. The entire process has been led by community
projects involving experts from various backgrounds, referred to as
stakeholders below. The different aspects of Croissant-RAI were meticulously
defined using a comprehensive vocabulary engineering process which was focused
on iterative and participatory co-design activities. In this section, we
explain the process in detail, including the RAI documentation frameworks that
served as the foundation for developing the vocabulary. The subsequent section
will discuss the use cases developed to guide the design of Croissant-RAI
ensuring its usefulness and relevance to machine learning practitioners.

Figure 1 depicts the different steps of the vocabulary engineering process
detailed as follows:

  * •

Use Case Definition. The process began with biweekly sprint sessions, engaging
stakeholders from various domains and levels of expertise. Through several
brainstorming sessions, stakeholders identified and prioritized the attributes
for the Croissant-RAI format. This collaborative approach ensured that the
identified use cases were comprehensive, relevant, and aligned with the
overarching goals of promoting data standardization and responsible AI
practices.

  * •

Comparison with existing dataset documentation vocabularies. In this phase,
existing dataset documentation vocabularies were thoroughly examined to
identify overlaps and gaps in relation to the Croissant vocabulary, i.e. the
attributes. An overview of these vocabularies is presented in Table 1.
Stakeholders were engaged in comparative analyses and discussions to assess
the suitability of different vocabularies for integration with Croissant-RAI.

  * •

Scope Definition. Through participatory co-design activities, stakeholders
formulated competency questions to that defined the scope and requirements of
the Croissant-RAI vocabulary. These questions reflected diverse perspectives
and priorities, acting as guiding principles. They helped identify the key
features and functionalities necessary to meet the objectives of the
Croissant-RAI vocabulary.

  * •

Conceptualization of RAI extension and Implementation. Building on insights
from co-design activities discuss above, the conceptual framework for the
Croissant-RAI vocabulary was defined on top of Croissant. Through
collaborative modeling sessions, concept mapping exercises, and scenario-based
discussions, we ensured that the Croissant-RAI vocabulary aligned with use
cases, stakeholder priorities and did not overlap or contradiction with the
Croissant format. Moreover, in biweekly sprint cycles a conceptual model of
the Croissant-RAI vocabulary as developed. This facilitated an integration of
Croissant-RAI with Croissant and ensured compatibility with existing
functionalities.

  * •

Evaluation through example annotations for each use case. To ensure the
effectiveness and usability of the Croissant-RAI metadata format, stakeholders
engaged in participatory evaluation activities. This included annotating
sample datasets using the implemented features and gathering feedback from
users to identify any usability issues or areas for improvement.

Figure 2: Mapping between the use cases and the properties of the Croissant-
RAI vocabulary, Croissant and Schema.org. Dotted boxes include Schema.org
(sc:) and Croissant (cr:) properties while the other boxes contain Croissant-
RAI properties (rai:).

Throughout each of these steps, participatory co-design activities played a
pivotal role in ensuring that the features of Croissant-RAI were aligned with
the needs and perspectives of diverse stakeholders. By fostering collaboration
and inclusivity, the vocabulary engineering process facilitated the
development of a robust and adaptable framework for promoting standardization
and responsible AI practices within the data documentation ecosystem. Note
that this engineering process is scalable to other domain-specific extensions
that could be developed on top of Croissant in the near future.

## Use Cases

A set of use cases guided the design of the attributes for the RAI croissant
format. During several brainstorming sessions, stakeholders identified and
prioritized the five use cases presented in this section. The collaborative
approach ensured that the identified use cases were comprehensive, relevant,
and aligned with the overall goals of promoting data standardization and
responsible AI practices. The presented use cases are intended to be a
representative sample, but more may be added in the future.

The initial use cases are centered on documenting the data life cycle,
characterizing in-depth data labeling and data participatory processes, and
providing critical information for AI safety and fairness assessments and
regulatory compliance checks. A more detailed explanation is provided below.

#### Use case 1: The data life cycle.

The dataset life cycle involves stages like motivation, composition,
collection, preprocessing, uses, distribution, and maintenance. Documenting
RAI-related properties prompts creators to reflect on the process and aids
user understanding. Information includes: creator, purpose, creation date,
data sources, versioning, composition, processing, intended use, and
maintenance. Documentation of the provenance and lineage of the datasets that
are derived from revision, modification or extension of existing datasets is
also relevant for this use case.

#### Use case 2: Data labeling.

Labels plays a central role in dataset for AI and can be obtained through
human input such as labels from crowdsourcing platforms, or machine
annotations like concept extraction. Details such as the platform used, number
of human labels per record, rater demographics, and annotation tool
characteristics aid in understanding dataset composition and facilitate
efficient sampling. Information on the labeling process enhances comprehension
of data creation, sample characteristics, and aids in assessing, replicating,
and reproducing the process, thereby boosting data reliability.

#### Use case 3: Participatory data.

Some ML datasets are created using fairly well understood, albeit poorly
documented, processes and practices. Others, however, are the result of
community or collaborative work that involve a much wider range of entities
with limited coordination among them. Examples include citizen science
datasets created through participatory sensing; Wikidata, created by 23k
editors; or ML datasets using crowdsourcing platforms. Documenting the
participatory element of the dataset life cycle can help understand biases and
limitations in the dataset, making the process easier to monitor, assess,
repeat, replicate, and reproduce.

#### Use case 4: AI safety and fairness evaluation.

Safety and bias information is crucial for understanding potential risks and
fairness aspects of data usage, aiming to prevent unintended harmful
consequences from models trained on or evaluated with the data. This includes
identifying features for known and intended usage (e.g., adversarial datasets
for safety evaluation, counterfactual annotations for fairness evaluation) and
any usage restrictions. Additionally, accounting for personal and sensitive
information within datasets aids in risk mitigation and responsible usage.
This information is typically gathered at the item level and aggregated at the
dataset level, often presented as a scorecard or nutrition label.

#### Use case 5: Regulatory compliance

Compliance officers and legal teams require data-related information to assess
the dataset’s fit to privacy and the current regulation laws. For instance,
regulations in the field of AI, such as the recently approved European AI Act,
state the need to provide documentation about the data used to train specific
ML applications777https://www.euaiact.com/annex/4, section 2.d. In regards to
this, the Croissant-RAI vocabulary allows users to annotate this information
in a structured way, including but not limited to, sensitive and personal
identifiable information, data governance and security measures and data
sharing agreements. This information can be relevant across various fields,
including research, businesses, and the public sector.

## The Responsible AI Vocabulary

In this section, we present the attributes that comprise the Croissant-RAI
vocabulary. The attributes are presented alongside the corresponding use cases
discussed earlier. As an extension of Croissant, which is based on the
Schema.org/Dataset vocabulary, the Croissant-RAI extension’s properties
incorporate both core Croissant and Schema.org properties. Figure 3 shows the
relationship between these vocabularies and a few representative properties of
each one.

Figure 3: Overview of the Croissant-RAI vocabulary

A concrete mapping of the Croissant-RAI properties to the five use cases
identified during the engineering process is shown in Figure 2. The figure
identifies the different properties related to each use case and their source
vocabulary, i.e., whether the property was derived from Croissant core (marked
with "cr:") or Schema.org (marked with "sc:") vocabularies or if it is defined
as part of the RAI extension (marked with "rai:").888A detailed description of
each property of the vocabulary is provided in the appendix (Table 2).

In the _Data Life Cycle_ scenario, existing information from Schema.org and
the Croissant vocabulary covers aspects like creators, publishers, and
licenses. However, the Croissant-RAI extension requires additional details
about the data collection process, design assumptions including data use cases
and identified limitations, and specific data preprocessing steps.

Conversely, in the _Data Labeling_ use case, the Croissant-RAI vocabulary
offers attributes to ensure label quality. Essential details include the
annotation process and annotator information. Documenting instructions given
to annotators, annotation devices used, and annotator demographics become
crucial for assessing label quality.

For the _Participatory Data_ use case, it is crucial to document information
about the data collectors, including their demographics and the platforms they
use, such as mobile applications. Additionally, if the data represents or is
collected from individuals, it is important to gather demographic details
about the target participants.

In the context of _AI Safety & Fairness_, the Croissant-RAI suggests
documenting data limitations, biases, and sensitive information within the
dataset. These attributes encourage data authors to reason about their data
with a specific schema in mind, akin to structure of proposals such as
Datasheets for Datasets.

Lastly, concerning _Regulatory Compliance_ use case, we propose annotating
information about handling sensitive data for privacy, data maintenance
policies, intended use cases (aligned with the Use cases classification of the
AI Act), and various sharing agreements and manipulation protocols applied to
the data.

In the following section, we showcase a collection of datasets described using
the Croissant-RAI vocabulary, demonstrating its practical application.

Listing 1: Excerpt description of the HLS Burn Scar Scenes dataset

⬇

{

"@type": "schema.org/Dataset",

"name": "HLS Burn Scar Scenes",

"dct:conformsTo": "mlcommons.org/croissant/RAI/1.0",

"rai:dataCollection": "Imagery is collected from V1.4 of Harmonized Landsat
and Sentinel-2 (HLS). A full description and access to HLS may be found at
https://hls.gsfc.nasa.gov/. The labels were from shapefiles maintained by the
Monitoring Trends in Burn Severity (MTBS) group. The masks may be found at:
https://mtbs.gov/",

"rai:dataProcessing": "After co-locating the shapefile and HLS scene, the
512x512 chip was formed by taking a window with the burn scar in the center.
Burn scars near the edges of HLS tiles are offset from the center. Images were
manually filtered for cloud cover and missing data to provide as clean a scene
as possible, and burn scar presence was also manually verified.",

[…]

}

Listing 2: Excerpt description of DICES-350 dataset

⬇

{

"name": "DICES-350",

"dct:conformsTo": "mlcommons.org/croissant/RAI/1.0",

"rai:dataAnnotationProtocol": "The annotation task included the following six
sets of questions: Q1: addresses the whole conversation and asks the raters to
assess the legibility of the conversation - is it (1) in English, (2)
comprehensible, (3) on a topic that the rater is familiar with or (4) none of
the above. Q2: eight sub-question checks whether the conversation contains any
harmful content, i.e., whether it could directly facilitate serious and
immediate harm to individuals, groups …",

"rai:dataAnnotationPlatform" : "Crowdworker annotators with task specific UI",

"rai:dataAnnotationAnalysis": "Initial recruitment of 123 raters for the
DICES-350 dataset, after all annotation tasks were completed, a quality
assessment was performed on the raters and 19 raters were filtered out due to
low quality work …"

"rai:annotatorDemographics": "DICES-350 was annotated by a pool of 104 raters.
The rater breakdown for this pool is: 57 women and 47 men; 27 gen X+, 28
millennial, and 49 gen z; and 21 Asian, 23 Black/African American, 22
Latine/x, 13 multiracial and 25 white. All raters signed a consent form
agreeing for the detailed demographics to be collected for this task."

[…]

}

## Application examples

This section contains three examples of popular and diverse datasets described
in Croissant-RAI format. This goal is to demonstrate the applicability of the
Croissant-RAI properties to datasets from various fields in order to train and
evaluate AI models. We present a geospatial dataset (Phillips et al. 2023)
with relevant RAI details in the data collection and preprocessing, a dataset
for evaluating diversity in conversational AI, documenting the great diversity
within the annotators profiles, and a community-created text dataset used to
train one of the most recent open-source large language models.

Listing 3: Excerpt description of BigScience Roots Corpus dataset

⬇

{

"name": "BigScience Root Corpus",

"dct:conformsTo": "mlcommons.org/croissant/RAI/1.0",

"rai:dataLimitations": [

0:"Crawled content over-represents pornographic text across languages in the
form of spam ads…",

1:"The preprocessing removes some categories of PII but is still far from
exhaustive …",

2:"The reliance on medium to large sources of digitized content still over-
represents privileged voices and language varieties."

],

"rai:dataBiases": "Dataset includes multiple sub-ratings which specify the
type of safety concern, such as type of hate speech and the type of bias or
misinformation, for each conversation …",

"rai:dataManipulationProtocol":[

0:"HTML reconstruction: A 20 fairly simple heuristic ontag types to
reconstruct the structure of the text extracted from an HTML code",

1:"Data filtering: Documents were filtered by a set of criteria such as; Too
high character repetition, too high ratio of spatial characters, to high ratio
of flagged words to avoid pornographical conctect …",

2:"Deduplication: Substring deduplication (Lee et al., 2022) based on Suffix
Array (Manber and Myers, 1993) as a complementary method that clusters
documents sharing a long substring, were applied for documents with more than
6000 characters."

]

[…]

}

Figure 4: UI of the RAI extension of the Croissant editor

### RAI Geospatial AI-ready datasets

Geospatial AI (also GeoAI) refers to the integration of AI techniques with
geospatial data, enabling advanced location-based analysis, mapping, and
decision-making. The GeoAI datasets are curated from data captured by various
sensors on-board spaceborne, airborne, and ground platforms along with the in-
situ sensors. With the ever rising significance of location-based analysis and
the increasing size of the generated data, GeoAI plays an important role in
solving complex and crucial problems like weather forecasting. However,
Geospatial datasets also involve ethical considerations in the acquisition and
utilization of geospatial data, addressing potential biases, environmental
impact, and privacy concerns.

For instance, location accuracy and spatial properties are vital to the
credibility of GeoAI-ready datasets. Changes in local accuracy can have an
impact on AI predictions. For example, in AI-based crop yield prediction
tasks, ground truth for validating the AI model is typically obtained from
agricultural farms. However, due to privacy concerns, these annotations are
often approximated, which can result in inaccurate predictions and estimates
of AI models. In this regard, RAI properties related to data collection, such
as the devices used and details about data preprocessing and manipulation, can
increase support and confidence in developed AI models.

In Listing 1 we can see an excerpt of the HLS Burn Scar Scenes dataset
(Phillips et al. 2023), an AI-ready geospatial dataset described using the
Croissant-RAI format. The listing shows information about the devices used to
collected the satellite images, and the preprocesses applied to center the
detected burn scars. Other RAI relevant attributes for Geospatial AI dataset
such as the sampling strategy or the data validity timeframe (e.g., seasonal
agricultural crop yield estimation) can also be annotated using the Croissant-
RAI format.

### Diversity In Conversational AI Evaluation for Safety

With the increasing rise in development of the LLMs, it is crucial to assess
the LLMs for inclusiveness and FAIRness in an operational scenario (Vidgen et
al. 2024). This also holds true for the conversational AI agents and the
chatbots that automate human-computer interaction based on question-answering,
data captioning and text retrieval tasks. The DICES (Diversity In
Conversational AI Evaluation for Safety) dataset (Aroyo et al. 2024) addresses
the need for nuanced safety evaluation in language modeling, emphasizing
diverse perspectives in model training and assessment.

It introduces several key features, including a focus on rater diversity,
where differences in opinions are viewed in terms of diversity rather than
bias. The dataset ensures balanced demographic representation among raters and
assesses safety across five categories, offering detailed evaluations of harm,
bias, misinformation, politics, and safety policy violations.

Listing 2 contains an excerpt from the Croissant-RAI description of the DICES
dataset showing some dataset key features. The excerpt discusses the data
annotation protocol, the platform, and an analysis of the annotation process.
Furthermore, the property _rai:annotatorDemographics_ provides demographic
information showing the diversity and inclusion effort done by dataset
creators in accommodating language characteristics across gender, race, and
age groups. Such metadata-defined information fosters trust in using the data
to create conversational AI models capable of generating responses to diverse
and FAIR audience groups.

### BigScience Roots Corpus dataset

Due to the popularity of language models, there is an increasing interest in
large, high-quality datasets, particularly in multilingual contexts. Recent
open-source efforts have been focused on creating these kinds of datasets,
providing them to train large-scale monolingual and multilingual AI models and
encouraging further research in the field. The BigScience
workshop999BigScience workshop homepage: https://bigscience.huggingface.co, a
year-long international and interdisciplinary effort with the primary goal of
investigating and training large language models, has led one of these efforts
focusing on ethical considerations, potential harm, and governance issues.

As a result, they generated the BigScience Roots Corpus (Laurençon et al.
2022), a large-scale dataset collected from multiple sources that helped to
train the BigScience Large Open-science Open-access Multilingual (BLOOM)
language model, which has 176 billion parameters. The critical points of this
dataset are its inherent variate composition as they gathered data from
multiple sources, the inherited limitations and biases from these sources, and
the set of different preprocessing steps applied to ensure its quality.

Listing 3 contains an excerpt from the BigScience Roots Corpus dataset
Croissant-RAI description that describes some of the detected data limitations
and biases from the sources, as well as some of the preprocessing steps
applied to the data. Sharing biases and data limitations helps potential users
evaluate the data’s suitability for their use cases, and sharing the
preprocessing steps used on the data improves the reproducibility of dataset
creation by future similar initiatives.

### Tool support

To boost and facilitate the community’s adoption of the Croissant-RAI format,
we have developed an extension of the current Croissant python library and its
web UI editor to implement the Croissant-RAI vocabulary. This editor allows
users to input RAI attributes in JSON-LD form, thus serving as a tool for data
publishers to input dataset-related RAI information. Figure 4 shows the
Croissant Editor’s RAI tab, which currently defines a few RAI metadata
attributes for the dataset publishers. Once the required information is
populated, the data publishers can export the final Croissant metadata as the
output, containing the supported RAI attributes that have been presented in
this work.

## Conclusions

In this work, we present Croissant-RAI, a machine-readable format for
capturing and publishing RAI documentation meta-data for AI datasets. Our
proposal has been built on top of previous data documentation approaches,
making them easy to share, discover, and reuse. The design of the vocabulary
has been derived through relevant RAI use cases and illustrated in real
examples from important domains and datasets. Finally, we expand the current
Croissant toolkit, implementing the Croissant-RAI extension to aid the
community in its adoption.

In future work, we aim to track the community’s uptake of the Croissant-RAI
vocabulary, offering valuable insights into its real-world application.
Moreover, we recognize the socio-technical dimensions of RAI practices and
underscore the importance of collaborating with both public and private
partners. Through active engagement with regulators and corporations, we can
not only promote the widespread adoption of RAI methodologies but also ensure
their responsible implementation. We maintain that institutions should
contemplate integrating RAI benchmarks, such as the one outlined in this
paper, into their frameworks to foster responsible AI practices.

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##  Appendix A Appendix

Table 2 gives an overview of all attributes of Croissant-RAI.

Table 2: Croissant-RAI Property Descriptions
Property |  ExpectedType |  Use Case |  Cardinality |  Description
---|---|---|---|---
rai:dataCollection |  sc:Text |  Data life cycle |  ONE |  Description of the data collection process
rai:dataCollectionType |  sc:Text |  Data life cycle |  MANY |  "Define the data collection type. Recommended values: Surveys, Secondary Data analysis, Physical data collection, Direct measurement, Document analysis, Manual Human Curator, Software Collection, Experiments, Web Scraping, Web API, Focus groups, Self-reporting, Customer feedback data, User-generated content data, Passive Data Collection, Others"
rai:dataCollectionMissingData |  sc:Text |  Data life cycle |  ONE |  Description of missing data in structured/unstructured form
rai:dataCollectionRawData |  sc:Text |  Data life cycle |  ONE |  Description of the raw data i.e. source of the data
rai:dataCollectionTimeframe |  sc:DateTime |  Data life cycle |  MANY |  Timeframe in terms of start and end date of the collection process
rai:dataImputationProtocol |  sc:Text |  Compliance |  ONE |  Description of data imputation process if applicable
rai:dataManipulationProtocol |  sc:Text |  Compliance |  ONE |  Description of data manipulation process if applicable
rai:dataPreprocessingProtocol |  sc:Text |  Data life cycle |  MANY |  Description of the steps that were required to bring collected data to a state that can be processed by an ML model/algorithm, e.g. filtering out incomplete entries etc.
rai:dataAnnotationProtocol |  sc:Text |  Data labeling |  ONE |  Description of annotations (labels, ratings) produced, including how these were created or authored - Annotation Workforce Type, Annotation Characteristic(s), Annotation Description(s), Annotation Task(s), Annotation Distribution(s)
rai:dataAnnotationPlatform |  sc:Text |  Data labeling |  MANY |  Platform, tool, or library used to collect annotations by human annotators
rai:dataAnnotationAnalysis |  sc:Text |  Data labeling |  MANY |  Considerations related to the process of converting the “raw” annotations into the labels that are ultimately packaged in a dataset - Uncertainty or disagreement between annotations on each instance as a signal in the dataset, analysis of systematic disagreements between annotators of different socio demographic group, how the final dataset annotations will relate to individual annotator responses
rai:dataReleaseMaintenancePlan |  sc:Text |  Compliance |  MANY |  Versioning information in terms of the updating timeframe, the maintainers, and the deprecation policies.
rai:personalSensitiveInformation |  sc:Text |  Compliance |  MANY |  Sensitive Human Attribute(s)- Gender, Socio-economic status,Geography, Language, Age,Culture, Experience or Seniority, Others (please specify)
rai:dataSocialImpact |  sc:Text |  AI safety and fairness evaluation |  ONE |  Discussion of social impact, if applicable
rai:dataBiases |  sc:Text |  AI safety and fairness evaluation |  MANY |  Description of biases in dataset, if applicable
rai:dataLimitations |  sc:Text |  AI safety and fairness evaluation |  MANY |  Known limitations - Data generalization limits (e.g related to data distribution, data quality issues, or data sources) and on-recommended uses.
rai:dataUseCases |  sc:Text |  AI safety and fairness evaluation |  MANY |  Dataset Use(s) - Training, Testing, Validation, Development or Production Use, Fine Tuning, Others (please specify), Usage Guidelines. Recommended uses.
rai:annotationsPerItem |  sc:Text |  Data labeling |  ONE |  Number of human labels per dataset item
rai:annotatorDemographics |  sc:Text |  Data labeling |  MANY |  List of demographics specifications about the annotators
rai:machineAnnotationTools |  sc:Text |  Data labeling |  MANY |  List of software used for data annotation ( e.g. concept extraction, NER, and additional characteristics of the tools used for annotation to allow for replication or extension)