AI and Shared Prosperity Initiative
Ongoing ProjectAI 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.
learn moreThe 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.
learn morePublication 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.
learn moreBringing 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.
learn moreClosing 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.
learn moreABOUT 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.
learn moreExplainable Machine Learning in Deployment
Paper
Explainable Machine Learning in Deployment
PAI research reveals a gap between explainability in practice and the goals of transparency.
learn moreAI 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.
learn moreOn 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.
learn moreSafeLife 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.
learn moreHuman-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.
learn moreHuman-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.
learn moreVisa 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.
learn moreAI, 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.
learn moreReport 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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