Produced By: Academic
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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 
Evaluating Facial Recognition Technology: A Protocol for Performance Assessment in New Domains
In May 2020, Stanford's HAI hosted a workshop to discuss the performance of facial recognition technologies that included leading computer scientists, legal scholars, and representatives from industry, government, and civil society. The white paper this workshop produced seeks to answer key questions in improving understandings of this rapidly changing space.
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Automation + AI 
Domain Shift and Emerging Questions in Facial Recognition Technology
This policy brief offers recommendations to policymakers relating to the computational and human sides of facial recognition technologies based on a May 2020 workshop with leading computer scientists, legal scholars, and representatives from industry, government, and civil society
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Automation + AI 
Algorithmic Impact Assessments and Accountability: The Co-construction of Impacts
Algorithmic impact assessments (AIAs) are an emergent form of accountability for organizations that build and deploy automated decision-support systems. This academic paper explores how to co-construct impacts that closely reflects harms, and emphasizes the need for input of various types of expertise and affected communities.
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Automation + AI
Who Audits the Auditors? Recommendations from a Field Scan of the Algorithmic Auditing Ecosystem
Through a field scan, this paper identifies emerging best practices as well as methods and tools that are becoming commonplace, and enumerates common barriers to leveraging algorithmic audits as effective accountability mechanisms.
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Automation + AI 
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. This academic study presents an approach to evaluate bias present in automated facial analysis algorithms and datasets.
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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
Surveillance, Discretion and Governance in Automated Welfare
This academic article develops a framework for evaluating whether and how automated decision-making welfare systems introduce new harms and burdens for claimants, focusing on an example case from Germany.
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Automation + AI 
Technology in the public sector and the future of government work
This report explores technologies that have the potential to significantly affect employment and job quality in the public sector, the factors that drive choices about which technologies are adopted and how they are implemented, how technology will change the experience of public sector work, and what kinds of interventions can protect against potential downsides of technology use in the public sector. The report categories technologies into five overlapping categories including manual task automation, process automation, automated decision-making systems, integrated data systems, and electronic monitoring.
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Automation + AI 
Popular Support for Balancing Equity and Efficiency in Resource Allocation
This article explores how online advertising algorithms bias between Spanish and English speakers for SNAP in California.
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Automation + AI
Government By Algorithm: Artificial Intelligence In Federal Administrative Agencies
Little is known about how agencies are currently using AI systems, and little attention has been devoted to how agencies acquire such tools or oversee their use.
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Automation + AI
Defining and Demystifying Automated Decision Systems
This article suggests that a lack of clear, shared definitions makes it harder for the public and policymakers to evaluate and regulate technical systems that may have significant impacts on communities and individuals by shaping access to benefits, opportunities, and liberty. It presents and evaluates a definition for automated decision systems, developed through workshops with interdisciplinary scholars and practitioners.