Research, Publications & Initiatives

AI and Shared Prosperity Initiative
Ongoing Project

AI and Shared Prosperity Initiative

A multi-year initiative, the AI and Shared Prosperity Initiative conducts research and gathers multidisciplinary input to develop and disseminate practical frameworks that AI developing and deploying companies should adopt to ensure that AI progress advances broadly shared prosperity and not the economic betterment of a few to the detriment of many. The project strives to equip our Partners with practical approaches for making AI development and deployment inclusive by design. The AI SPI explores ways to proactively guide AI advancement in the direction of expanding the economic prospects of workers, particularly those with limited opportunities for educational advancement.

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The Role of Demographic Data in Addressing Algorithmic Bias
Research Project

The Role of Demographic Data in Addressing Algorithmic Bias

A lack of clarity around the acceptable uses for demographic data has frequently been cited by PAI Partners as a barrier to addressing algorithmic bias in practice. This has led us to ask the question, “When and how should demographic data be collected and used in service of algorithmic bias detection and mitigation?” In response, the Partnership on AI is conducting a research project exploring access to and usage of demographic data as a barrier to detecting bias. We are presently conducting a series of interviews to better understand challenges that may prevent the detection or mitigation of algorithmic bias.

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Publication Norms for Responsible AI
Ongoing Intiative

Publication Norms for Responsible AI

As AI/ML is applied in increasingly high-stakes contexts, and touches increasing parts of our everyday lives, it becomes ever more important to consider the broader social impact of AI/ML research and mitigate the risks of malicious use, unintended consequences, and accidents, so that we can all enjoy the many potential benefits of this transformative technology. The Partnership on AI is undertaking a multistakeholder project that aims to facilitate the exploration and thoughtful development of publication practices for responsible AI.

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Bringing Facial Recognition Systems To Light
Paper

Bringing Facial Recognition Systems To Light

Understanding how facial recognition systems work is essential to being able to examine the technical, social & cultural implications of these systems.

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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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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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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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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
Researching Diversity, Equity, and Inclusion in the Field of AI
News
Multistakeholder Approaches to Explainable Machine Learning
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