Systems that augment sensory abilities are increasingly employing AI and machine learning (ML) approaches, with applications ranging from object recognition and scene description tools for blind users to sound awareness tools for d/Deaf users. However, unlike many other AI-enabled technologies these systems provide information that is already available to non-disabled people. In this paper, we discuss unique AI fairness challenges that arise in this context, including accessibility issues with data and models, ethical implications in deciding what sensory information to convey to the user, and privacy concerns both for the primary user and for others.
This content will become publicly available on August 1, 2023
FINS Auditing Framework: Group Fairness for Subset Selection
Subset selection is an integral component of AI systems that is increasingly affecting people’s livelihoods in applications ranging from hiring, healthcare, education, to financial decisions. Subset selections powered by AI-based methods include top- analytics, data summarization, clustering, and multi-winner voting. While group fairness auditing tools have been proposed for classification systems, these state-of-the-art tools are not directly applicable to measuring and conceptualizing fairness in selected subsets. In this work, we introduce the first comprehensive auditing framework, FINS, to support stakeholders in interpretably quantifying group fairness across a diverse range of subset-specific fairness concerns. FINS offers a family of novel measures that provide a flexible means to audit group fairness for fairness goals ranging from item-based, score-based, and a combination thereof. FINS provides one unified easy-to-understand interpretation across these different fairness problems. Further, we develop guidelines through the FINS Fair Subset Chart, that supports auditors in determining which measures are relevant to their problem context and fairness objectives. We provide a comprehensive mapping between each fairness measure and the belief system (i.e., worldview) that is encoded within its measurement of fairness. Lastly, we demonstrate the interpretability and efficacy of FINS in supporting the identification of real bias with case studies using more »
- Award ID(s):
- 2007932
- Publication Date:
- NSF-PAR ID:
- 10338008
- Journal Name:
- AAAI/ACM Conference on AI, Ethics, and Society (AEIS)
- Sponsoring Org:
- National Science Foundation
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