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A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms
This work seeks to center validity considerations in deliberations around whether and how to build data-driven algorithms in high-stakes domains.
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders
In this work, we conducted a set of seven design workshops with 35 stakeholders who have been impacted by the child welfare system or who work in it to understand their beliefs and concerns around PRMs, and to engage them in imagining new uses of data and technologies in the child welfare system.
Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems.
A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms
This stylized setup offers the distinct capability of testing fairness interventions beyond observational data and against an unbiased benchmark.
Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support
AI-based decision support tools (ADS) are increasingly used to augment human decision-making in high-stakes, social contexts.
"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment
Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS).
Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation
In this paper, we present Value Card, an educational toolkit to inform students and practitioners of the social impacts of different machine learning models via deliberation.
Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems
On Wikipedia, sophisticated algorithmic tools are used to assess the quality of edits and take corrective actions.
