Research, Publications & Initiatives

Closing Gaps In Responsible AI
Ongoing Project

Closing Gaps In Responsible AI

Operationalizing responsible AI principles is a complex process, and currently the gap between intent and practice is large. To help fill this gap, the Partnership on AI has initiated Closing Gaps in Responsible AI, a multiphase, multi-stakeholder project aimed at surfacing salient challenges and evaluate potential solutions for organizational implementation of responsible AI.

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Explainable Machine Learning in Deployment
Paper

Explainable Machine Learning in Deployment

PAI research reveals a gap between explainability in practice and the goals of transparency.

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AI and Media Integrity Steering Committee
Steering Committee

AI and Media Integrity Steering Committee

The AI and Media Integrity Steering Committee is a formal body of PAI Partner organizations focused on projects to confront the emergent threat of AI-generated mis/disinformation, synthetic media, and AI’s effects on public discourse.

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On the Legal Compatibility of Fairness Definitions
Paper

On the Legal Compatibility of Fairness Definitions

“Fairness” defined in machine learning literature often misuses or misunderstands the legal concepts from which they purport to be inspired by.

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SafeLife 1.0: Exploring Side Effects in Complex Environments
Research Project

SafeLife 1.0: Exploring Side Effects in Complex Environments

This publicly available reinforcement learning environment tests the ability of trained agents to operate safely and minimize side effects.

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Human-AI Collaboration Framework & Case Studies
Case Studies

Human-AI Collaboration Framework & Case Studies

This report includes a framework to help users consider key aspects of human-AI collaboration technologies, and case studies which illustrate real world applications.

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Human-AI Collaboration Trust Literature Review: 
Key Insights and Bibliography
Report

Human-AI Collaboration Trust Literature Review: 
Key Insights and Bibliography

This project highlights key themes and high-level insights from a review of multidisciplinary literature on AI, humans and trust, and includes a thematically tagged bibliography of 78 articles.

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Visa Laws, Policies, and Practices: Recommendations for Accelerating the Mobility of Global AI/ML Talent
Policy paper

Visa Laws, Policies, and Practices: Recommendations for Accelerating the Mobility of Global AI/ML Talent

PAI’s policy paper offers recommendations that will enable multidisciplinary AI/ML experts to benefit from the diverse perspectives offered by the global AI/ML community.

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ABOUT ML - Annotation and Benchmarking on Understanding and Transparency of Machine learning Lifecycles
Ongoing Project

ABOUT ML - Annotation and Benchmarking on Understanding and Transparency of Machine learning Lifecycles

This multi-year, iterative, multistakeholder effort works towards establishing evidence-based ML transparency best practices throughout the ML system lifecycle.

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AI, Labor, and the Economy Case Study Compendium
Case study

AI, Labor, and the Economy Case Study Compendium

These case studies examine the labor implications and productivity impacts of AI implementation across different applications, geographies, and sectors.

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Report on Algorithmic Risk Assessment Tools in the U.S. Criminal Justice System
Report

Report on Algorithmic Risk Assessment Tools in the U.S. Criminal Justice System

This report documents the serious shortcomings of algorithmic risk assessment tools in the U.S. criminal justice system, and includes ten requirements that jurisdictions should weigh heavily prior to the use of these tools.

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About Us

The Partnership on AI to Benefit People and Society was established to study and formulate best practices on AI technologies, to advance the public’s understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society.

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Our Partners

By gathering the leading companies, organizations, and people differently affected by artificial intelligence, PAI establishes a common ground between entities which otherwise may not have cause to work together – and in so doing – serves as a uniting force for good in the AI ecosystem.

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NON PROFIT ORGANIZATIONS

61

INDUSTRY

20

ACADEMIC INSTITUTIONS

19

100 partners in 13 countries

News & Blog

News

Join The Partnership at FAT* 2020

PAI Staff

January 21, 2020

Building Community around Fairness, Accountability, and Transparency in AI The impact of algorithmic systems on fairness, transparency, and accountability (FTA) is at the heart of the ACM FAT* Confere...

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News
PAI Launches Interactive Project To Put Ethical AI Principles into Practice
News
Partnership on AI Welcomes Health Catalyst
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