This work seeks to center validity considerations in deliberations around whether and how to build data-driven algorithms in high-stakes domains.
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.
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.
This stylized setup offers the distinct capability of testing fairness interventions beyond observational data and against an unbiased benchmark.
AI-based decision support tools (ADS) are increasingly used to augment human decision-making in high-stakes, social contexts.
Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS).
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.
On Wikipedia, sophisticated algorithmic tools are used to assess the quality of edits and take corrective actions.