Topic: Mitigating Harm + Bias
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Automation + AI 
Enabling Principles for AI Governance
A report from the Center for Security and Emerging Technology (CSET) discussing enabling principles for artificial intelligence (AI) governance.
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Automation + AI 
AI Toolkit
Guidance and Resources for Policymakers, Teachers and Parents to Advance AI Readiness in Ohio Schools.
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Automation + AI 
Looking before we leap: Exploring AI and data science ethics review process
This report explores the role that academic and corporate Research Ethics Committees play in evaluating AI and data science research for ethical issues, and also investigates the kinds of common challenges these bodies face.
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Digital Identity 
Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing
This paper explores design considerations and ethical tensions related to auditing of commercial facial processing technology.
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Automation + AI 
Disability, Bias, and AI
This report explores key questions that a focus on disability raises for the project of understanding the social implications of AI, and for ensuring that AI technologies don’t reproduce and extend histories of marginalization.
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Automation + AI 
The Privacy-Bias Trade-Off
Safeguarding privacy and addressing algorithmic bias can pose an under-recognized trade-off. This brief documents tradeoffs by examining the U.S. government’s recent efforts to introduce government-wide equity assessments of federal programs. The authors propose a range of policy solutions that would enable agencies to navigate the privacy-bias trade-off.
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Automation + AI 
A Human Rights-Based Approach to Responsible AI
This paper argues that a human rights framework could help orient the research on artificial intelligence away from machines and the risks of their biases, and towards humans and the risks to their rights, helping to center the conversation around who is harmed, what harms they face, and how those harms may be mitigated.
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Automation + AI 
The Social Life of Algorithmic Harms
This series of essays seeks to expand our vocabulary of algorithmic harms to help protect against them.
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Automation + AI
The Privacy-Bias Tradeoff: Data Minimization and Racial Disparity Assessments in U.S. Government
An emerging concern in algorithmic fairness is the tension with privacy interests. Data minimization can restrict access to protected attributes, such as race and ethnicity, for bias assessment and mitigation. This paper examines how this “privacy-bias tradeoff” has become an important battleground for fairness assessments in the U.S. government and provides rich lessons for resolving these tradeoffs.
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Automation + AI 
Automated Decision-Making Systems and Discrimination
This guidebook offers an introduction to the risks of discrimination when using automated decision-making systems. This report also includes helpful definitions related to automation.
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Automation + AI
Access Denied: Faulty Automated Background Checks Freeze Out Renters
This reporting explores how algorithms used to screen prospective tenants, including those waiting for public housing, can block renters from housing based on faulty information.
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Automation + AI 
POVERTY LAWGORITHMS: A Poverty Lawyer’s Guide to Fighting Automated Decision-Making Harms on Low-Income Communities
This guide, directed at poverty lawyers, explains automated decision-making systems so lawyers and advocates can better identify the source of their clients' problems and advocate on their behalf. Relevant for practitioners, this report covers key questions around automated decision-making systems.