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.
2022 ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization
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.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
This academic paper examines how federal privacy laws restrict data collection needed for assessing racial disparities, creating a tradeoff between protecting individual privacy and enabling algorithmic fairness in government programs.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
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.
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.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
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.
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.
This study examines public attitudes toward balancing equity and efficiency in algorithmic resource allocation, using online advertising for SNAP enrollment as a case study.
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.
Automated decision systems (ADS) are increasingly used in government decision-making but lack clear definitions, oversight, and accountability mechanisms.
This essay explains why the Center on Privacy & Technology has chosen to stop using terms like "artificial intelligence," "AI," and "machine learning," arguing that such language obscures human accountability and overstates the capabilities of these technologies.
This academic paper examines predictive optimization, a category of decision-making algorithms that use machine learning (ML) to predict future outcomes of interest about individuals. Through this examination, the authors explore how predictive optimization can raise concerns that make its use illegitimate and challenge claims about predictive optimization's accuracy, efficiency, and fairness.
