Contestability in Algorithmic Systems

By Kristen Vaccaro, K. G. Karahalios, Deirdre K. Mulligan, Daniel N Kluttz, Tad Hirsch. 2019

51 citations recorded by the source corpus.

As algorithmic (and particularly machine learning) decision making systems become both more widespread and make more important decisions, there are growing concerns about their embedded values and ability to establish legitimacy among decision subjects. We argue that designing for contestability in these systems can assist in surfacing values, aligning system design and use with context, and building legitimacy. However, designing for contestability can be challenging, particularly in systems that are designed to be opaque: systems need to accurately surface embedded values, expose decision making processes in ways that are meaningful for users, support engagement with and allow influence over system performance, and so on. In addition to these technical aspects, designing for contestability may by challenged by the need to protect intellectual property and prevent gaming of the system. In this workshop, we will address goals, audiences, and designs for contestability in algorithmic systems. We hope to develop a taxonomy of contestable systems and understand the value provided by contestability, while bringing together a community to work on this multidisciplinary problem.

Subfields: Artificial Intelligence, Safety Research, AI Ethics, Fairness & Society

How researchers see this paper

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

In Artificial Intelligence

1. Su Lin Blodgett
2. Hal Daumé Iii
3. Hanna M. Wallach
4. Solon Barocas
5. Yejin Choi
6. Noah A. Smith
7. Yulia Tsvetkov
8. Trevor Darrell

In Safety Research

1. Hanna M. Wallach
2. Su Lin Blodgett
3. Hal Daumé Iii
4. Solon Barocas
5. Adrian Weller
6. Alexandra Chouldechova
7. Berk Ustun
8. Aaron Roth

In AI Ethics, Fairness & Society

1. Hanna M. Wallach
2. Hal Daumé Iii
3. Solon Barocas
4. Su Lin Blodgett
5. Adrian Weller
6. Berk Ustun

Citations in the corpus

• Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “Finally, several workshops and events have begun to explore how to empower stakeholders in the development and deployment of technology (Vaccaro et al., 2019; Givens and Morris, 2020; Sassaman et al., 2020)4 and how to help researchers and practitioners consider when not to build systems at all…”