Human-Centered Approaches to Fair and Responsible AI

By Min Kyung Lee, Nina Grgic-Hlaca, Michael Carl Tschantz, Reuben Binns, Adrian Weller, Michelle Carney, K. Inkpen. 2020

58 citations recorded by the source corpus.

As AI changes the way decisions are made in organizations and governments, it is ever more important to ensure that these systems work according to values that diverse users and groups find important. Researchers have proposed numerous algorithmic techniques to formalize statistical fairness notions, but emerging work suggests that AI systems must account for the real-world contexts in which they will be embedded in order to actually work fairly. These findings call for an expanded research focus beyond statistical fairness to that which includes fundamental understandings of human use and the social impact of AI systems, a theme central to the HCI community. The HCI community can contribute novel understandings, methods, and techniques for incorporating human values and cultural norms into AI systems; address human biases in developing and using AI; and empower individual users and society to audit and control AI systems. Our goal is to bring together academic and industry researchers in the fields of HCI, ML and AI, and the social sciences to devise a cross-disciplinary research agenda for fair and responsible AI systems. This workshop will build on previous algorithmic fairness workshops at AI and ML conferences, map research and design opportunities for future innovations, and disseminate them in each community.

Subfields: Safety Research, Sociology and Political Science, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety Research

1. Vinodkumar Prabhakaran
2. Sunipa Dev

In Sociology and Political Science

1. Brent J. Hecht
2. Vinodkumar Prabhakaran
3. Sunipa Dev

In Safety, Robustness & Interpretability

1. Sunipa Dev
2. Shachi Dave
3. Partha P. Talukdar
4. Shaily Bhatt
5. Vinodkumar Prabhakaran

Citations in the corpus

• Re-contextualizing Fairness in NLP: The Case of India (2022, background): “We believe that participatory approaches (Lee et al., 2020) to create resources for fairness evaluation will be crucial for meaningfully addressing this gap.”
• Behavioral Use Licensing for Responsible AI (2020, background): “centered” [40], as it focuses on the interests of both developers and the general public.”