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Many hands make many fingers to point: challenges in creating accountable AI

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Abstract

Given the complexity of teams involved in creating AI-based systems, how can we understand who should be held accountable when they fail? This paper reports findings about accountable AI from 26 interviews conducted with stakeholders in AI drawn from the fields of AI research, law, and policy. Participants described the challenges presented by the distributed nature of how AI systems are designed, developed, deployed, and regulated. This distribution of agency, alongside existing mechanisms of accountability, responsibility, and liability, creates barriers for effective accountable design. As agency is distributed across the socio-technical landscape of an AI system, users without deep knowledge of the operation of these systems become disempowered, unable to challenge or contest when it impacts their lives. In this context, accountability becomes a matter of building systems that can be challenged, interrogated, and, most importantly, adjusted in use to accommodate counter-intuitive results and unpredictable impacts. Thus, accountable system design can work to reconfigure socio-technical landscapes to protect the users of AI and to prevent unjust apportionment of risk.

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Acknowledgements

This research was funded by Cisco Systems under RFP16-02, Legal Implications for IoT, Machine Learning, and Artificial Intelligence. We thank our research participants, including Blake Anderson, Ruzena Bajcsy, Hal Daume III, Stephen Elkins, Enzo Fenoglio, Iria Giuffrida, Dean Harvey, James Hodson, Wei San Hui, Amir Husain, Jeff Kirk, Frederic Lederer, Ted Lehr, Terrell McSweeny, Matt Scherer, Peter Stone, Nicolas Vermeys, and Christopher Yoo as well as eight anonymous participants.

Funding

This research was funded by a grant from Cisco Systems, Inc. RFP-16-02 Legal Implications for IoT, ML, & AI.

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Authors and Affiliations

  1. School of Information, The University of Texas at Austin, Austin, TX, USA

    Stephen C. Slota, Kenneth R. Fleischmann, Nitin Verma & Lan Li

  2. LBJ School of Public Affairs, The University of Texas at Austin, Austin, TX, USA

    Sherri Greenberg & Brenna Cummings

  3. Cisco Systems Inc, Herndon, VA, USA

    Chris Shenefiel

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  1. Stephen C. Slota
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by KF, SG, SS, NV, BC and LL. The first draft of the manuscript was written by SS and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Stephen C. Slota.

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This study was approved by the University of Texas at Austin IRB, #2018-10-0015.

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All participants completed the informed consent process prior to participation in the study. The consent process and research activities were reviewed and approved by the University of Texas at Austin IRB, #2018-10-0015.

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Slota, S.C., Fleischmann, K.R., Greenberg, S. et al. Many hands make many fingers to point: challenges in creating accountable AI. AI & Soc 38, 1287–1299 (2023). https://doi.org/10.1007/s00146-021-01302-0

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