Data Set Details

Data Sets / Specialized mix of differing data types

Responsible AI Recommendations (RAIR)

Total Size: 261 KB

This dataset contains recommendations culled from academic and gray literature on the implementation of responsible or ethical AI. Recommendations are tagged with 1 to 5 topical labels, and a label regarding the source type of their parent document.

Contributors

Gastelum, Zoe

Data Files

Transfer data using Globus

Fields

Reference - refers to the original source document

FileName - unique file identifier used internally

UniqueID - unique identification number for each recommendation

Recommendation - recommendation text extracted from the source document

SourceType - the identity of the type of source document of the recommendation, such as university, industry, NGO

Class1 - Class5 - each recommendation contains up to five topical labels. All recommendations have at least one label.

Sample Data Set

Reference
FileName
UniqueID
Recommendation
SourceType
Class1
Class2
Class3
Class4
Class5

UNSECO. 2021. "Recommendation on the Ethics of Artificial Intelligence." November 23. https://unesdoc.unesco.org/ark:/48223/pf0000381137.
UNESCO2021_RecommendationOnEthicsofAI
unsc.35
 Member States and business enterprises should assess the direct and indirect environmental impact throughout the AI system life cycle, including, but not limited to, its carbon footprint, energy consumption and the environmental impact of raw material extraction for supporting the manufacturing of AI technologies, and reduce the environmental impact of AI systems and data infrastructures. Member States should ensure compliance of all AI actors with environmental law, policies and practices.
intergov
sustainability
riskassessment
compliance_policy

UNSECO. 2021. "Recommendation on the Ethics of Artificial Intelligence." November 23. https://unesdoc.unesco.org/ark:/48223/pf0000381137.
UNESCO2021_RecommendationOnEthicsofAI
unsc.31
 Member States should work through international organizations to provide platforms for international cooperation on AI for development, including by contributing expertise, funding, data, domain knowledge, infrastructure, and facilitating multi-stakeholder collaboration to tackle challenging development problems, especially for LMICs, in particular LDCs, LLDCs and SIDS.
intergov
engagement
access

Schneiderman, Ben. 2020. "Bridging the Gap Between Ethics and Practice: Guidelines for Reliable, Safe, and Trustworthy Human-Centered AI Systems." ACM Digital Transactions on Interactive Intelligent Systems (Tiis) 10 (4): 1-31.
Schneiderman2022_BridgingTheGapBetweenEthicsAndPractice
schn.4
 usability testing with typical direct users and indirect stakeholders [is needed].
uni
HCI
engagement

Supplemental Information

Data is an excel file, populated with text. There are 682 recommendations in total.

Software Needed

MS office

Coverage

Spatial Coverage

global

Temporal Coverage

2020-01-02 to 2025-12-31

Top