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Cited by 5Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)Digitalization, Law, and Regulation

The DOI matches its Crossref record; 208 of the 218 references it cites were found in a registry or an index.

Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems

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Kroll, Joshua A.·2021·Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
DOI 10.1145/3442188.3445937
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Abstract

Accountability is widely understood as a goal for well governed computer systems, and is a sought-after value in many governance contexts. But how can it be achieved? Recent work on standards for governable artificial intelligence systems offers a related principle: traceability. Traceability requires establishing not only how a system worked but how it was created and for what purpose, in a way that explains why a system has particular dynamics or behaviors. It connects records of how the system was constructed and what the system did mechanically to the broader goals of governance, in a way that highlights human understanding of that mechanical operation and the decision processes underlying it. We examine the various ways in which the principle of traceability has been articulated in AI principles and other policy documents from around the world, distill from these a set of requirements on software systems driven by the principle, and systematize the technologies available to meet those requirements. From our map of requirements to supporting tools, techniques, and procedures, we identify gaps and needs separating what traceability requires from the toolbox available for practitioners. This map reframes existing discussions around accountability and transparency, using the principle of traceability to show how, when, and why transparency can be deployed to serve accountability goals and thereby improve the normative fidelity of systems and their development processes.

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Research works printed but not linked: 34

  1. Ifeoma Ajunwa. 2021. The Auditing Imperative for Automated Hiring. Harvard Journal of Law & Technology 34 (2021).
  2. Amit Elazari Bar On. 2018. We Need Bug Bounties for Bad Algorithms. https://www.vice.com/en/article/8xkyj3/we-need-bug-bounties-for-bad-algorithms. Vice News (3 May 2018).
  3. Chris Argyris. 1977. Double loop learning in organizations. Harvard business review 55, 5 (1977), 115--125.
  4. Lisanne Bainbridge. 1983. Ironies of automation. In Analysis, design and evaluation of man-machine systems. Elsevier, 129--135.
  5. Josep Balasch, Alfredo Rial, Carmela Troncoso, Bart Preneel, Ingrid Verbauwhede, and Christophe Geuens. 2010. PrETP: Privacy-Preserving Electronic Toll Pricing.. In USENIX Security Symposium. 63--78.
  6. Kent Beck. 2003. Test-driven development: by example. Addison-Wesley Prof.
  7. Kent Beck, Mike Beedle, Arie Van Bennekum, Alistair Cockburn, Ward Cunningham, et al. [n.d.]. Manifesto for agile software development, 2001. ([n. d.]).
  8. Jeremiah Blocki, Nicolas Christin, Anupam Datta, Ariel D Procaccia, and Arunesh Sinha. 2013. Audit games. In Proceedings of the Twenty-Third international joint conference on Artificial Intelligence. AAAI Press, 41--47.
  9. Paula Bruening and Joshua A. Kroll. 2019. Considering Transparency: AI, Data Science, and the GDPR. Computers, Privacy, and Data Protection (2019).
  10. Jan Camenisch, Susan Hohenberger, and Anna Lysyanskaya. 2006. Balancing accountability and privacy using e-cash. In Security and Cryptography for Networks. Springer, 141--155.
  11. Ann Cavoukian. 2011. Privacy by design in law, policy and practice. A white paper for regulators, decision-makers and policy-makers (2011).
  12. Danielle Keats Citron. 2008. Open code governance. In University of Chicago Legal Forum. 355--387.
  13. Janet L Colbert and Paul Bowen. 1996. A Comparison of Internal Control: COBIT, SAC, COSO and SAS. IS Audit and Control Journal (1996), 26--35.
  14. Computing Community Consortium. 2015. Privacy by Design---Engineering Privacy: Workshop 3 Report. https://cra.org/ccc/wp-content/uploads/sites/2/2015/12/PbD3-Workshop-Report-v2.pdf.
  15. Computing Community Consortium. 2015. Privacy by Design---Privacy Enabling Design: Workshop 2 Report. https://cra.org/ccc/wp-content/uploads/sites/2/2015/05/PbD2-Report-v5.pdf.
  16. Computing Community Consortium. 2015. Privacy by Design-State of Research and Practice: Workshop 1 Report. https://cra.org/ccc/wp-content/uploads/sites/2/2015/02/PbD-Workshop-1-Report-.pdf.
  17. Editorial. 2016. More Accountability for Big Data Algorithms. Nature 537 (2016). Issue 7621.
  18. Joan Feigenbaum, Aaron D Jaggard, Rebecca N Wright, and Hongda Xiao. 2012. Systematizing "Accountability" in Computer Science (Version of Feb. 17, 2012). Technical Report YALEU/DCS/TR-1452. Yale University, New Haven, CT.
  19. Batya Friedman. 1996. Value-sensitive design. interactions 3, 6 (1996), 16--23.
  20. R. Stuart Geiger and Aaron Halfaker. 2017. Operationalizing Conflict and Cooperation between Automated Software Agents in Wikipedia: A Replication and Expansion of "Even Good Bots Fight". Proceedings of the ACM on Human-Computer Interaction 1, CSCW (dec 2017), 1--33.
  21. Government Accountability Office. 2018. Government Auditing Standards, 2018 Revision.
  22. Joseph Y Halpern and Judea Pearl. 2005. Causes and explanations: A structural-model approach. Part I: Causes. The British journal for the philosophy of science 56, 4 (2005), 843--887.
  23. Lauren Kirchner. 2017. Thousands of Criminal Cases in New York Relied on Disputed DNA Testing Techniques. ProPublica (4 Sept. 2017).
  24. Joshua A. Kroll Joanna Huey Solon Barocas Edward W. Felten Joel R. Reidenberg David G. Robinson and Harlan Yu. 2017. Accountable Algorithms. University of Pennsylvania Law Review (to appear) 165 (2017) 633--705. Issue 3.
  25. Min Kyung Lee, Daniel Kusbit, Anson Kahng, Ji Tae Kim, Xinran Yuan, Allissa Chan, Daniel See, Ritesh Noothigattu, Siheon Lee, Alexandros Psomas, et al. 2019. WeBuildAI: Participatory framework for algorithmic governance. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 1--35.
  26. Teresa F Lunt. 1988. Automated audit trail analysis and intrusion detection: A survey. In In Proceedings of the 11th National Computer Security Conference.
  27. Pete McBreen and Kent Foreword By-Beck. 2002. Questioning extreme programming. Addison-Wesley Longman Publishing Co., Inc.
  28. Deirdre K Mulligan and Kenneth A Bamberger. 2018. Saving Governance-by-Design. California Law Review 106, 101 (2018).
  29. Deirdre K Mulligan and Helen Nissenbaum. 2020. The Concept of Handoff as a Model for Ethical Analysis and Design. In The Oxford Handbook of Ethics of AI, Markus Dubber, Frank Pasquale, and Sunit Das (Eds.). Oxford University Press, Oxford, UK.
  30. 10.5555/539883 · DOI
  31. Helen Nissenbaum. 2005. Values in technical design. In Encyclopedia of science, technology, and ethics. Macmillan New York, NY, 66--70.
  32. Dillon Reisman, Jason Schultz, Kate Crawford, and Meredith Whittaker. 2018. Algorithmic Impact Assessements: A Practical Framework for Public Agency Accountability. AI Now Institute Report https://ainowinstitute.org/aiareport2018.pdf.
  33. Christian Sandvig. 2015. Seeing the Sort: The Aesthetic and Industrial Defense of "The Algorithm". Journal of the New Media Caucus (2015). ISSN: 1942-017X, [Online] http://median.newmediacaucus.org/art-infrastructures-information/seeing-the-sort-the-aesthetic-and-industrial-defense-of-the-algorithm/.
  34. Christian Sandvig, Kevin Hamilton, Karrie Karahalios, and Cedric Langbort. 2014. Auditing algorithms: Research methods for detecting discrimination on internet platforms. Data and Discrimination: Converting Critical Concerns into Productive Inquiry (2014).

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Metadata

Authors
Kroll, J. A.
Affiliations
Naval Postgraduate School (US)
Corresponding author
Kroll, Joshua A.
ORCID
Kroll, Joshua A.
Venue
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
Publisher
ACM
Published
2021-03
Pages
758-771
DOI
10.1145/3442188.3445937
OpenAlex
W3122480037
Cited by
2
References
218
Citations by year
2024: 1 · 2023: 1
FWCI
0.45
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Social Sciences › Law › Digitalization, Law, and Regulation
Keywords
traceability, accountability, transparency, AI governance, AI principles, explainable AI, software traceability, requirements mapping, decision processes

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