Representation in AI evaluations

Conference item

Calls for representation in artificial intelligence (AI) and machine learning (ML) are widespread, with "representation"or "representativeness"generally understood to be both an instrumentally and intrinsically beneficial quality of an AI system, and central to fairness concerns. But what does it mean for an AI system to be "representative"? Each element of the AI lifecycle is geared towards its own goals and effect on the system, therefore requiring its own analyses with regard to what kind of representation is best. In this work we untangle the benefits of representation in AI evaluations to develop a framework to guide an AI practitioner or auditor towards the creation of representative ML evaluations. Representation, however, is not a panacea. We further lay out the limitations and tensions of instrumentally representative datasets, such as the necessity of data existence and access, surveillance vs expectations of privacy, implications for foundation models and power. This work sets the stage for a research agenda on representation in AI, which extends beyond instrumentally valuable representation in evaluations towards refocusing on, and empowering, impacted communities.

Authors: Bergman, AS; Hendricks, LA; Rauh, M; Wu, B; Agnew, W

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Association for Computing Machinery
• Host title: FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency
• Pages: 519-533
• Publication date: 2023-06-12
• Acceptance date: 2022-04-22
• Event title: ACM Conference on Fairness, Accountability, and Transparency (FAccT 2023)
• Event location: Chicago, IL, USA
• Event website: https://facctconference.org/2023/
• Event start date: 2023-06-12
• Event end date: 2023-06-15
• DOI: 10.1145/3593013.3594019
• ISBN: 9798400701924
• Language: English
• Keywords: Networking and Information Technology R&D (NITRD); data science; machine learning and artificial intelligence