Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems
Open accessAbstract
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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Despite advancements, multimodal learning still confronts challenges such as refining architectures to handle diverse data types, developing comprehensive datasets, and establishing benchmarks for evaluating complex systems.
“Despite the advancements, the field of multimodal learning still confronts ongoing challenges, such as refining the architectures to handle diverse data types more effectively [269], [270], developing comprehensive datasets that accurately represent multifaceted information [269], [271], and establishing benchmarks for evaluating the performance of these complex systems [272], [273].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [269]
Cites
Calibrating Noise to Sensitivity in Private Data Analysis
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272 · see citing works
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302 · see citing works
(2018) · Cited by 3,510 · see citing works
Steps toward an ecology of infrastructure: Design and access for large information spaces
Star, S. L., & Ruhleder, K. (1996) · Information Systems Research · doi:10.1287/isre.7.1.111 · Cited by 3,238 · see citing works
How the machine “thinks”: Understanding opacity in machine learning algorithms
Burrell, J. (2016) · Big Data & Society · doi:10.1177/2053951715622512 · Cited by 3,196 · cited 1× · see citing works
“Tools such as analytic frameworks for operationalizing values [48, 181, 182], tech-”
BackgroundTowards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121 · see citing works
Model Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681 · see citing works
Gillespie, T. (2010) · New Media & Society · doi:10.1177/1461444809342738 · Cited by 2,522 · cited 1× · see citing works
“platforms such as ISPs, social media and forum websites, and hosting providers [106].”
BackgroundGebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM · doi:10.1145/3458723 · Cited by 2,469 · see citing works
The mythical man-month: essays on software engineering
(1996) · Choice Reviews Online · doi:10.5860/choice.33-5142 · Cited by 2,453 · see citing works
Mitchell, M. (2009) · doi:10.1093/oso/9780195124415.001.0001 · Cited by 2,310 · see citing works
Fair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159 · see citing works
A Digital Signature Based on a Conventional Encryption Function
Merkle, R. C. (1988) · Lecture Notes in Computer Science · doi:10.1007/3-540-48184-2_32 · Cited by 1,998 · see citing works
Zerocash: Decentralized Anonymous Payments from Bitcoin
Ben‐Sasson, E., Chiesa, A., Garman, C., Green, M., Miers, I., Tromer, E., & Virza, M. (2014) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2014.36 · Cited by 1,987 · see citing works
Principles alone cannot guarantee ethical AI
Mittelstadt, B. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0114-4 · Cited by 1,886 · cited 1× · see citing works
“research challenge of the values-oriented design movement and research community [94, 168, 172, 173].”
BackgroundFairness and Abstraction in Sociotechnical Systems
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287598 · Cited by 1,631 · see citing works
The Challenger Launch Decision: Risky Technology, Culture, and Deviance at NASA.
Rosa, E. A., & Vaughan, D. (1997) · Social Forces · doi:10.2307/2580695 · Cited by 1,630 · see citing works
‘Accountability’: An Ever‐Expanding Concept?
Mulgan, R. (2000) · Public Administration · doi:10.1111/1467-9299.00218 · Cited by 1,609 · see citing works
Engineering a Safer World: Systems Thinking Applied to Safety
(2012) · Choice Reviews Online · doi:10.5860/choice.49-6305 · Cited by 1,566 · see citing works
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372873 · Cited by 1,372 · cited 1× · see citing works
“tems, especially when those systems are computerized [40, 81, 93, 99, 127, 142, 143, 178, 195, 209, 243].”
BackgroundWhy and Where: A Characterization of Data Provenance
Buneman, P., Khanna, S., & Wang-Chiew, T. (2001) · Lecture Notes in Computer Science · doi:10.1007/3-540-44503-x_20 · Cited by 1,334 · see citing works
The Checklist Manifesto: How to Get Things Right
Gawande, A. A. (2011) · Journal of Nursing Regulation · doi:10.1016/s2155-8256(15)30310-0 · Cited by 1,155 · see citing works
Value Sensitive Design and Information Systems
Friedman, B., Kahn, P. H., & Borning, A. H. (2008) · Wiley eBooks · doi:10.1002/9780470281819.ch4 · Cited by 1,149 · see citing works
Reporting and preventing medical mishaps: lessons from non-medical near miss reporting systems
Barach, P. R. (2000) · BMJ · doi:10.1136/bmj.320.7237.759 · Cited by 1,109 · see citing works
An introduction to Docker for reproducible research
Boettiger, C. (2015) · ACM SIGOPS Operating Systems Review · doi:10.1145/2723872.2723882 · Cited by 1,059 · see citing works
Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., & Srikumar, M. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3518482 · Cited by 998 · cited 1× · see citing works
“research challenge of the values-oriented design movement and research community [94, 168, 172, 173].”
BackgroundGet ready for agile methods, with care
BOEHM, B. W. (2002) · Computer · doi:10.1109/2.976920 · Cited by 912 · see citing works
Classes of recursively enumerable sets and their decision problems
Rice, H. G. (1953) · Transactions of the American Mathematical Society · doi:10.1090/s0002-9947-1953-0053041-6 · Cited by 869 · see citing works
MOOR, J. H. (1985) · Metaphilosophy · doi:10.1111/j.1467-9973.1985.tb00173.x · Cited by 863 · see citing works
Bender, E. M., & Friedman, B. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00041 · Cited by 854 · cited 2× · see citing works
“These include data sheets [101], fact sheets [10], data statements [28], data nutrition labels [123], model cards [171], and other standardized”
BackgroundMitigating bias in algorithmic hiring: evaluating claims and practices
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372828 · Cited by 845 · see citing works
Handbook of Human Factors and Ergonomics
Salvendy, G. (2012) · Wiley eBooks · doi:10.1002/9781118131350 · Cited by 758 · see citing works
Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174156 · Cited by 743 · see citing works
'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions
Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173951 · Cited by 691 · cited 2× · see citing works
“say, perspectives on the performance of a system [30, 238]) would provide useful benchmarks for those charged with the test and eval-”
BackgroundAccounting for Rationality: Double-Entry Bookkeeping and the Rhetoric of Economic Rationality
Carruthers, B. G., & Espeland, W. N. (1991) · American Journal of Sociology · doi:10.1086/229739 · Cited by 638 · see citing works
A Survey of Methods for Explaining Black Box Models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629 · see citing works
Lum, K., & Isaac, W. (2016) · Significance · doi:10.1111/j.1740-9713.2016.00960.x · Cited by 611 · see citing works
SNARKs for C: Verifying Program Executions Succinctly and in Zero Knowledge
Ben-Sasson, E., Chiesa, A., Genkin, D., Tromer, E., & Virza, M. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40084-1_6 · Cited by 598 · see citing works
Devops: A Software Architect's Perspective
Bass, L., Weber, I. M., & Zhu, L. (2015) · CERN Document Server (European Organization for Nuclear Research) · Cited by 561 · see citing works
Intelligible models for classification and regression
Lou, Y., Caruana, R., & Gehrke, J. (2012) · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2339530.2339556 · Cited by 547 · see citing works
Helios: Web-based open-audit voting
Adida, B. (2008) · Cited by 543 · see citing works
Provenance-Aware Storage Systems
Muniswamy‐Reddy, K., Holland, D. A., Braun, U., & Seltzer, M. (2006) · Cited by 527 · see citing works
Edwards, L., & Veale, M. (2017) · doi:10.31228/osf.io/97upg · Cited by 524 · see citing works
Verifying and Validating Software Requirements and Design Specifications
BOEHM, B. W. (1984) · IEEE Software · doi:10.1109/ms.1984.233702 · Cited by 518 · see citing works
Meaningful information and the right to explanation
Selbst, A. D., & Powles, J. (2017) · International Data Privacy Law · doi:10.1093/idpl/ipx022 · Cited by 516 · see citing works
Accountability of AI Under the Law: The Role of Explanation
Doshi-Velez, F., Kortz, M., Budish, R., Bavitz, C., Gershman, S. J., O'Brien, D., Scott, K., Shieber, S., Waldo, J., Weinberger, D., et al. (2017) · doi:10.2139/ssrn.3064761 · Cited by 457 · see citing works
FactSheets: Increasing trust in AI services through supplier's declarations of conformity
Arnold, M., Bellamy, R. K. E., Hind, M., Houde, S., Mehta, S., Mojsilović, A., Nair, R., Ramamurthy, K. N., Olteanu, A., Piorkowski, D., et al. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2942288 · Cited by 434 · see citing works
Consultative Committee For Space Data Systems
Pilgram, M. (2010) · Brill | Nijhoff eBooks · doi:10.1163/ej.9789004163300.i-1081.440 · Cited by 432 · see citing works
Succinct non-interactive zero-knowledge for a von Neumann architecture
Ben‐Sasson, E., Chiesa, A., Tromer, E., & Virza, M. (2014) · Cited by 426 · see citing works
Star, S. L., & Ruhleder, K. (1994) · Proceedings of the 1994 ACM conference on Computer supported cooperative work - CSCW '94 · doi:10.1145/192844.193021 · Cited by 412 · see citing works
Weitzner, D. J., Abelson, H., Berners‐Lee, T., Joan Feigenbaum, A., Hendler, J., & Sussman, G. J. (2008) · Communications of the ACM · doi:10.1145/1349026.1349043 · Cited by 411 · see citing works
Measuring personalization of web search
Hannak, A., Sapiezynski, P., Molavi Kakhki, A., Krishnamurthy, B., Lazer, D., Mislove, A., & Wilson, C. (2013) · Proceedings of the 22nd international conference on World Wide Web · doi:10.1145/2488388.2488435 · Cited by 396 · see citing works
Establishing the rules for building trustworthy AI
Floridi, L. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0055-y · Cited by 394 · see citing works
The Intuitive Appeal of Explainable Machines
Selbst, A. D., & Barocas, S. (2018) · doi:10.2139/ssrn.3126971 · Cited by 392 · see citing works
Explanations as Mechanisms for Supporting Algorithmic Transparency
Rader, E., Cotter, K., & Cho, J. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173677 · Cited by 388 · see citing works
Values as lived experience: evolving value sensitive design in support of value discovery
Dantec, C. A. L., Poole, E., & Wyche, S. (2009) · International Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1518875 · Cited by 386 · see citing works
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Kearns, M., Neel, S., Roth, A., & Wu, Z. S. (2017) · arXiv · doi:10.48550/arxiv.1711.05144 · Cited by 381 · see citing works
Holding Power to Account: Accountability in Modern Democracies
Mulgan, R. G. (2003) · Medical Entomology and Zoology · Cited by 379 · see citing works
Algorithms in practice: Comparing web journalism and criminal justice
Christin, A. (2017) · Big Data & Society · doi:10.1177/2053951717718855 · Cited by 379 · see citing works
Laurie, B., Langley, A., & Kasper, E. (2013) · doi:10.17487/rfc6962 · Cited by 378 · see citing works
Building an Encrypted and Searchable Audit Log.
Waters, B. R., Balfanz, D., Durfee, G., & Smetters, D. K. (2004) · Cited by 372 · see citing works
An introduction to Docker for reproducible research, with examples from the R environmentPreprint
Boettiger, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.0846 · Cited by 372 · see citing works
Measuring price discrimination and steering on e-commerce web sites
Hannak, A., Soeller, G., Lazer, D., Mislove, A., & Wilson, C. (2014) · Proceedings of the 2014 Conference on Internet Measurement Conference · doi:10.1145/2663716.2663744 · Cited by 368 · see citing works
Jacobs, A. Z., & Wallach, H. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445901 · Cited by 362 · see citing works
Accountability in a computerized society
Nissenbaum, H. (1996) · Science and Engineering Ethics · doi:10.1007/bf02639315 · Cited by 346 · see citing works
Iterative enhancement: A practical technique for software development
Basil, V. R., & Turner, A. J. (1975) · IEEE Transactions on Software Engineering · doi:10.1109/tse.1975.6312870 · Cited by 343 · see citing works
Haeberlen, A., Kouznetsov, P., & Druschel, P. (2007) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/1294261.1294279 · Cited by 343 · see citing works
Consumer Financial Protection Bureau
Camacho, L. (2014) · Choice Reviews Online · doi:10.5860/choice.51-3000 · Cited by 333 · see citing works
Moral crumple zones: cautionary tales in human–robot interaction
Elish, M. C. (2019) · Engaging Science, Technology, and Society · doi:10.17351/ests2019.260 · Cited by 321 · see citing works
Wieringa, M. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372833 · Cited by 321 · see citing works
(1993) · Cited by 314 · see citing works
Implementing Reproducible Research
Stodden, V. (2014) · doi:10.1201/b16868 · Cited by 314 · see citing works
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., Khlaaf, H., Yang, J., Toner, H., Fong, R., et al. (2020) · arXiv · doi:10.48550/arxiv.2004.07213 · Cited by 306 · cited 2× · see citing works
“these tools can be extended to methods for trustworthy system development [45].”
BackgroundA survey on provenance: What for? What form? What from?
Herschel, M., Diestelkämper, R., & Ben Lahmar, H. (2017) · The VLDB Journal · doi:10.1007/s00778-017-0486-1 · Cited by 300 · see citing works
Problem Formulation and Fairness
Passi, S., & Barocas, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287567 · Cited by 283 · see citing works
Breaux, T. D., Vail, M. W., & Antón, A. I. (2006) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re.2006.68 · Cited by 280 · see citing works
Owning Ethics: Corporate Logics, Silicon Valley, and the Institutionalization of Ethics
Metcalf, J., Moss, E., & boyd, D. (2019) · Social research · doi:10.1353/sor.2019.0022 · Cited by 280 · see citing works
(2007) · Digital Commons at University of Maryland Carey Law (University of Maryland Francis King Carey School of Law) · Cited by 277 · see citing works
Quantification and the Accounting Ideal in Science
Porter, T. M. (1992) · Social Studies of Science · doi:10.1177/030631292022004004 · Cited by 256 · see citing works
Causes and Explanations: A Structural-Model Approach. Part II: Explanations
Halpern, J. Y., & Pearl, J. (2005) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/axi148 · Cited by 242 · see citing works
Procedural Justice in Algorithmic Fairness
Lee, M. K., Jain, A., Jin, H., Ojha, S., & Kusbit, D. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359284 · Cited by 236 · see citing works
Digital Preservation: A Time Bomb for Digital Libraries
Hedstrom, M. L. (1997) · Computers and the Humanities · doi:10.1023/a:1000676723815 · Cited by 235 · see citing works
Gürses, S. F., Troncoso, C., & Díaz, C. (2011) · Lirias · Cited by 235 · see citing works
How computer systems embody values
Nissenbaum, H. (2001) · Computer · doi:10.1109/2.910905 · Cited by 232 · see citing works
(2018) · Cited by 230 · see citing works
The Waterfall Model in Large-Scale Development
Petersen, K., Wohlin, C., & Baca, D. (2009) · International Conference on Product Focused Software Process Improvement · doi:10.1007/978-3-642-02152-7_29 · Cited by 225 · see citing works
Passi, S., & Jackson, S. J. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274405 · Cited by 225 · see citing works
The provenance of electronic data
Moreau, L., Groth, P., Miles, S., Vázquez-Salceda, J., Ibbotson, J., Jiang, S., Munroe, S., RANA, O. F., Schreiber, A., Tan, V., et al. (2008) · Communications of the ACM · doi:10.1145/1330311.1330323 · Cited by 224 · see citing works
Framework for Improving Critical Infrastructure Cybersecurity | NIST
Stine, K., Quill, K., & Witte, G. A. (2014) · ITL Bulletin - · Cited by 222 · see citing works
Value-Sensitive Algorithm Design: Method, Case Study, and Lessons
Zhu, H., Yu, B., Halfaker, A., & Terveen, L. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274463 · Cited by 220 · see citing works
Assumptions Underlying Agile Software-Development Processes
Turk, D., Robert, F., & Rumpe⋆, B. (2005) · Journal of Database Management · doi:10.4018/jdm.2005100104 · Cited by 218 · see citing works
Lee, M. K., Kusbit, D., Kahng, A., Kim, J. T., Yuan, X., Chan, A., See, D., Noothigattu, R., Lee, S., Psomas, A., et al. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359283 · Cited by 217 · see citing works
Don’t ask if artificial intelligence is good or fair, ask how it shifts power
Kalluri, P. (2020) · Nature · doi:10.1038/d41586-020-02003-2 · Cited by 213 · see citing works
Rosner, D. K., & Ames, M. G. (2014) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2531602.2531692 · Cited by 212 · see citing works
(2015) · doi:10.1145/2815400.2815415 · Cited by 202 · see citing works
The growth of software testing
Gelperin, D., & Hetzel, B. (1988) · Communications of the ACM · doi:10.1145/62959.62965 · Cited by 202 · see citing works
"The human body is a black box": supporting clinical decision-making with deep learning
Sendak, M., Elish, M. C., Gao, M., Futoma, J., Ratliff, W., Nichols, M., Bedoya, A., Balu, S., & O'Brien, C. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372827 · Cited by 196 · see citing works
Accountability as a Way Forward for Privacy Protection in the Cloud
Pearson, S., & Charlesworth, A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10665-1_12 · Cited by 191 · see citing works
Auditing and Inference Control in Statistical Databases
Chin, F. Y. L., & Özsoyoğlu, G. (1982) · IEEE Transactions on Software Engineering · doi:10.1109/tse.1982.236161 · Cited by 189 · see citing works
“Won’t Somebody Think of the Children?” Examining COPPA Compliance at Scale
Reyes, I. O., Wijesekera, P., Reardon, J., On, A. E. B., Razaghpanah, A., Vallina-Rodríguez, N., & Egelman, S. (2018) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2018-0021 · Cited by 189 · see citing works
Disparate Impact in Big Data PolicingPreprint
Selbst, A. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2819182 · Cited by 189 · see citing works
Values and Ethics in Human-Computer Interaction
Shilton, K. (2018) · Found. Trends Hum. Comput. Interact. · doi:10.1561/1100000073 · Cited by 187 · see citing works
Version Control with Git: Powerful Tools and Techniques for Collaborative Software Development
Loeliger, J., & McCullough, M. (2009) · O'Reilly Media, Inc. eBooks · Cited by 185 · see citing works
Lee, M. K., & Baykal, S. (2017) · Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing · doi:10.1145/2998181.2998230 · Cited by 182 · see citing works
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020) · arXiv · doi:10.48550/arxiv.2001.00973 · Cited by 179 · see citing works
Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2019) · arXiv · Cited by 176 · see citing works
The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards
Holland, S., Hosny, A., Newman, S., Joseph, J., & Chmielinski, K. (2018) · arXiv · doi:10.48550/arxiv.1805.03677 · Cited by 175 · see citing works
Avoiding Technological Quicksand: Finding a Viable Technical Foundation for Digital Preservation
Rothenberg, J. (1999) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.7754009 · Cited by 174 · see citing works
A case for the accountable cloud
Haeberlen, A. (2010) · ACM SIGOPS Operating Systems Review · doi:10.1145/1773912.1773926 · Cited by 169 · see citing works
Verifying computations with state
Braun, B., Feldman, A. J., Ren, Z., Setty, S., Blumberg, A. J., & Walfish, M. (2013) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/2517349.2522733 · Cited by 168 · see citing works
Stodden, V., & Miguez, S. (2014) · Journal of Open Research Software · doi:10.5334/jors.ay · Cited by 163 · see citing works
Values as Hypotheses: Design, Inquiry, and the Service of Values
JafariNaimi, N., Nathan, L. P., & Hargraves, I. G. (2015) · Design Issues · doi:10.1162/desi_a_00354 · Cited by 160 · see citing works
Appel, A. W. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19718-5_1 · Cited by 160 · see citing works
Ewusi-Mensah, K. (2003) · The MIT Press eBooks · doi:10.7551/mitpress/6322.001.0001 · Cited by 159 · see citing works
Küsters, R., Truderung, T., & Vogt, A. (2010) · Proceedings of the ACM Conference on Computer and Communications Security · doi:10.1145/1866307.1866366 · Cited by 159 · see citing works
Knobel, C. P., & Bowker, G. C. (2011) · Communications of the ACM · doi:10.1145/1965724.1965735 · Cited by 158 · see citing works
Investigating the Impact of Gender on Rank in Resume Search Engines
Chen, L., Ma, R., Hannák, A., & Wilson, C. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174225 · Cited by 155 · see citing works
95/02020 Hostages of each other: The transformation of nuclear safety since three mile island
Rees, J. V. (1995) · Fuel and Energy Abstracts · doi:10.1016/0140-6701(95)93685-8 · Cited by 150 · see citing works
Flanagan, M., Howe, D. C., & Nissenbaum, H. F. (2005) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1054972.1055076 · Cited by 149 · see citing works
Privacy is an essentially contested concept: a multi-dimensional analytic for mapping privacy
Mulligan, D. K., Koopman, C., & Doty, N. (2016) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2016.0118 · Cited by 149 · cited 2× · see citing works
“even an essentially contested point [181].”
BackgroundThe policy knot: re-integrating policy, practice and design in cscw studies of social computing
Jackson, S. J., Gillespie, T., & Payette, S. (2014) · Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing · doi:10.1145/2531602.2531674 · Cited by 143 · see citing works
Muniswamy‐Reddy, K., Macko, P., & Seltzer, M. (2010) · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · doi:10.5555/1855511.1855526 · Cited by 140 · see citing works
A Hybrid Architecture for Interactive Verifiable Computation
Vu, V., Setty, S., Blumberg, A. J., & Walfish, M. (2013) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2013.48 · Cited by 140 · see citing works
Efficient data structures for tamper-evident logging
Crosby, S. A., & Wallach, D. S. (2009) · Cited by 139 · see citing works
Kenthapadi, K., Mishra, N., & Nissim, K. (2005) · ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/1065167.1065183 · Cited by 138 · see citing works
Secure Sampling of Public Parameters for Succinct Zero Knowledge Proofs
Ben‐Sasson, E., Chiesa, A., Green, M., Tromer, E., & Virza, M. (2015) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2015.25 · Cited by 133 · see citing works
Kroll, J. A. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2018.0084 · Cited by 132 · see citing works
Self-citationPrivacy and Utility in Business Processes
Barth, A., Mitchell, J. C., Datta, A., & Sundaram, S. (2007) · IEEE Computer Security Foundations Workshop (CSFW) · doi:10.1109/csf.2007.26 · Cited by 126 · see citing works
Does mitigating ML's impact disparity require treatment disparity?Preprint
Lipton, Z. C., Chouldechova, A., & McAuley, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.07076 · Cited by 125 · see citing works
Statistical foundations of audit trail analysis for the detection of computer misuse
Helman, P., & Liepins, G. E. (1993) · IEEE Transactions on Software Engineering · doi:10.1109/32.241771 · Cited by 122 · see citing works
It's Not Privacy, and It's Not Fair
Dwork, C., & Mulligan, D. K. (2013) · bepress Legal Repository · Cited by 115 · see citing works
This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology
Mulligan, D. K., Kroll, J. A., Kohli, N., & Wong, R. Y. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359221 · Cited by 111 · see citing works
Self-citationPutting Fairness Principles into Practice: Challenges, Metrics, and Improvements
Beutel, A., Chen, J., Doshi, T., Qian, H., Woodruff, A., Luu, C., Kreitmann, P., Bischof, J., & Chi, E. H. (2019) · Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3306618.3314234 · Cited by 111 · see citing works
Analyzing Software Specifications for Mode Confusion Potential
Leveson, N. G., Pinnel, L. D., Sandys, S. D., Koga, S., & Reese, J. D. (1997) · Cited by 104 · see citing works
Haeberlen, A., Aditya, P., Rodrigues, R., & Druschel, P. (2010) · ScholarlyCommons (University of Pennsylvania) · doi:10.5555/1924943.1924952 · Cited by 104 · see citing works
Privacy by Design: A Counterfactual Analysis of Google and Facebook Privacy IncidentsPreprint
Rubinstein, I., & Good, N. A. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2128146 · Cited by 104 · see citing works
McPhillips, T., Song, T., Kolisnik, T., Aulenbach, S. M., Belhajjame, K., Bocinsky, R. K., Cao, Y., Cheney, J., Chirigati, F., Dey, S., et al. (2015) · International Journal of Digital Curation · doi:10.2218/ijdc.v10i1.370 · Cited by 103 · see citing works
Fair Information Practices: A Basic HistoryPreprint
Gellman, R. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2415020 · Cited by 101 · see citing works
Public service accountability: a comparative perspective
(1989) · Choice Reviews Online · doi:10.5860/choice.27-1743 · Cited by 101 · see citing works
Privacy on the Ground: Driving Corporate Behavior in the United States and Europe
Bamberger, K. A., & Mulligan, D. K. (2015) · Cited by 101 · see citing works
Haeberlen, A., Kouznetsov, P., & Druschel, P. (2007) · ACM SIGOPS Operating Systems Review · doi:10.1145/1323293.1294279 · Cited by 101 · see citing works
Bringing Design to the Privacy Table
Wong, R. Y., & Mulligan, D. K. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300492 · Cited by 100 · see citing works
Towards a Theory of Accountability and Audit
Jagadeesan, R., Jeffrey, A., Pitcher, C., & Riely, J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04444-1_10 · Cited by 98 · see citing works
Accountability, Quantification, and Law
Espeland, W., & Vannebo, B. I. (2007) · Annual Review of Law and Social Science · doi:10.1146/annurev.lawsocsci.2.081805.105908 · Cited by 98 · see citing works
Ellison, C. M. (2007) · Cited by 93 · see citing works
ASAX: Software architecture and rule-based language for universal audit trail analysis
Habra, N., Charlier, B. L., Mounji, A., & Mathieu, I. (1992) · Lecture notes in computer science · doi:10.1007/bfb0013912 · Cited by 91 · see citing works
Allspaw, J., & Cook, R. I. (2010) · Cited by 90 · see citing works
On the Brittleness of Software and the Infeasibility of Security Metrics
Bellovin, S. (2006) · IEEE Security & Privacy · doi:10.1109/msp.2006.101 · Cited by 89 · see citing works
Towards robustness in query auditing
Nabar, S. U., Marthi, B., Kenthapadi, K., Mishra, N., & Motwani, R. (2006) · doi:10.5555/1182635.1164142 · Cited by 87 · see citing works
Bamberger, K. A., & Mulligan, D. K. (2015) · The MIT Press eBooks · doi:10.7551/mitpress/9905.001.0001 · Cited by 85 · see citing works
A not quite random walk: Experimenting with the ethnomethods of the algorithm
Ziewitz, M. (2017) · Big Data & Society · doi:10.1177/2053951717738105 · Cited by 85 · see citing works
(2017) · SSRN Electronic Journal · Cited by 85 · see citing works
Self-citationAI Ethics – Too Principled to Fail?
Mittelstadt, B. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3391293 · Cited by 82 · see citing works
Auditing Algorithms for Discrimination
Kim, P. T. (2017) · Open Scholarship Institutional Repository (Washington University in St. Louis) · Cited by 77 · see citing works
Nikitin, K., Kokoris-Kogias, E., Jovanovic, P., Gailly, N., Gasser, L., Khoffi, I., Cappos, J., & Ford, B. (2017) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 71 · see citing works
Young, M., Magassa, L., & Friedman, B. (2019) · Ethics and Information Technology · doi:10.1007/s10676-019-09497-z · Cited by 71 · see citing works
How to see values in social computing
Shilton, K., Koepfler, J. A., & Fleischmann, K. R. (2014) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2531602.2531625 · Cited by 68 · see citing works
Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603203 · Cited by 67 · see citing works
Developing Bug-Free Machine Learning Systems With Formal MathematicsPreprint
Selsam, D., Liang, P., & Dill, D. (2017) · International Conference on Machine Learning · doi:10.48550/arxiv.1706.08605 · Cited by 66 · see citing works
A systematic review of provenance systems
Pérez, B., Rubio, J., & Sáenz-Adán, C. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-018-1164-3 · Cited by 66 · see citing works
Gillespie, T. (2006) · Social Studies of Science · doi:10.1177/0306312706056047 · Cited by 66 · see citing works
From critical design to critical infrastructure
Irani, L. C., & Silberman, M. ‘. (2014) · interactions · doi:10.1145/2627392 · Cited by 60 · see citing works
Values and Ethics in Human-Computer Interaction
Shilton, K. (2018) · doi:10.1561/9781680834673 · Cited by 58 · see citing works
A distributed requirements management framework for legal compliance and accountability
Breaux, T. D., Antón, A. I., & Spafford, E. H. (2008) · Computers & Security · doi:10.1016/j.cose.2008.08.001 · Cited by 53 · see citing works
Enslaving the Algorithm: From a 'Right to an Explanation' to a 'Right to Better Decisions'?
Edwards, L., & Veale, M. (2018) · Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) · Cited by 53 · see citing works
Albarghouthi, A., & Vinitsky, S. (2019) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3287560.3287588 · Cited by 52 · see citing works
Human-Centered Approaches to Fair and Responsible AI
Lee, M. K., Grgić-Hlača, N., Tschantz, M. C., Binns, R., Weller, A., Carney, M., & Inkpen, K. M. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3375158 · Cited by 49 · see citing works
Privacy by Design: A Counterfactual Analysis of Google and Facebook Privacy Incidents
Rubenstein, I. S. (2013) · bepress Legal Repository · doi:10.15779/z38g11n · Cited by 49 · see citing works
Disparate Impact in Big Data Policing
Selbst, A. D. (2018) · Digital Commons (University of Georgia School of Law) · Cited by 44 · see citing works
Evaluating and Mitigating Software Supply Chain Security Risks
Ellison, R. J., Goodenough, J., Weinstock, C. B., & Woody, C. (2010) · KiltHub Repository · doi:10.1184/r1/6573497 · Cited by 42 · see citing works
Young, M., Rodriguez, L., Keller, E., Sun, F., Sa, B., Whittington, J., & Howe, B. (2019) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3287560.3287577 · Cited by 41 · see citing works
Accountability in Computer Systems
Kroll, J. A. (2020) · The Oxford Handbook of Ethics of AI · doi:10.1093/oxfordhb/9780190067397.013.10 · Cited by 41 · see citing works
Self-citationBishop, M. (1995) · University of North Texas Digital Library (University of North Texas) · Cited by 40 · see citing works
Data science on the ground: Hype, criticism, and everyday work
Carter, D., & Sholler, D. (2015) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23563 · Cited by 32 · see citing works
Breaking Down While Building Up
Steinhardt, S. B. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858420 · Cited by 32 · see citing works
The Changing Contours of "Participation" in Data-driven, Algorithmic Ecosystems
Wolf, C. T., Zhu, H., Bullard, J., Lee, M. K., & Brubaker, J. R. (2018) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3272973.3273005 · Cited by 32 · see citing works
Mulligan, D. K., Kluttz, D., & Kohli, N. (2019) · doi:10.2139/ssrn.3311894 · Cited by 32 · see citing works
Putting Fairness Principles into Practice: Challenges, Metrics, and ImprovementsPreprint
Beutel, A., Chen, J., Doshi, T. S., Qian, H., Woodruff, A., Luu, C., Kreitmann, P., Bischof, J., & Ed, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.04562 · Cited by 29 · see citing works
International Practices of Criminal Justice
(2017) · Routledge eBooks · doi:10.4324/9781315144788 · Cited by 29 · see citing works
CSAR : A Practical and Probable Technique to Make Randomized Systems Accountable
Michael Backes, A., Druschel, P., Haeberlen, A., & Unruh, D. (2009) · Max Planck Digital Library · Cited by 28 · see citing works
Elish, M. C., & Hwang, T. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2720477 · Cited by 28 · see citing works
A Survey of Query Auditing Techniques for Data Privacy
Nabar, S. U., Kenthapadi, K., Mishra, N., & Motwani, R. (2008) · The Kluwer international series on advances in database systems · doi:10.1007/978-0-387-70992-5_17 · Cited by 27 · see citing works
Building reliable voting machine software
Wagner, D., Hearst, M. A., & Yee, K.-P. (2007) · Cited by 21 · see citing works
Enforceability vs. Accountability in Electronic Policies
Breaux, T. D., Antón, A. I., Karat, C.-M., & Karat, J. (2006) · IEEE International Workshop on Policies for Distributed Systems and Networks · doi:10.1109/policy.2006.18 · Cited by 20 · see citing works
Assessing regulatory change through legal requirements coverage modeling
Gordon, D. G., & Breaux, T. D. (2013) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re.2013.6636714 · Cited by 20 · see citing works
On the Concrete-Efficiency Threshold of Probabilistically-Checkable ProofsPreprint
Ben‐Sasson, E., Chiesa, A., Genkin, D., & Tromer, E. (2012) · Electronic colloquium on computational complexity · Cited by 20 · see citing works
Automated Verification of Accountability in Security Protocols
Künnemann, R., Esiyok, I., & Backes, M. (2019) · Proceedings - Computer Security Foundations Workshop/Proceedings · doi:10.1109/csf.2019.00034 · Cited by 20 · see citing works
How Computer Systems Embody Values
NissenbaumHelen (2001) · Computer · doi:10.5555/619061.621683 · Cited by 20 · see citing works
Aviation safety through the rule of law : ICAO's mechanisms and practices
Huang, J. (2009) · Medical Entomology and Zoology · Cited by 19 · see citing works
Friedman, B., Thomas, J. C., Grudin, J. T., Nass, C., Nissenbaum, H. F., Schlager, M. S., & Schneiderman, B. (1999) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/632716.632766 · Cited by 18 · see citing works
Observing algorithmic marketplaces in-the-wild
Chen, L., & Wilson, C. (2017) · ACM SIGecom Exchanges · doi:10.1145/3055589.3055594 · Cited by 18 · see citing works
Bashir, M. A., Arshad, S., & Wilson, C. (2016) · Internet Measurement Conference · doi:10.1145/2987443.2987469 · Cited by 15 · see citing works
The Algorithm Says You Did It: The Use of Black Box Algorithms to Analyze Complex DNA Evidence
Kwong, K. (2017) · Harvard journal of law & technology · Cited by 15 · see citing works
Privacy through Accountability: A Computer Science Perspective
Datta, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-04483-5_5 · Cited by 14 · see citing works
This Thing Called Fairness: Disciplinary Confusion Realizing a Value in TechnologyPreprint
Mulligan, D. K., Kroll, J. A., Kohli, N., & Wong, R. Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11869 · Cited by 14 · see citing works
Self-citationAn Algorithmic Equity Toolkit for Technology Audits by Community Advocates and ActivistsPreprint
Katell, M. A., Young, M., Herman, B., Dailey, D., Tam, A., Guetler, V., Binz, C., Raz, D., & Krafft, P. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.02943 · Cited by 14 · see citing works
Martin, D., Prabhakaran, V., Kuhlberg, J., Smart, A., & Isaac, W. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.09663 · Cited by 14 · see citing works
(2016) · Springer Proceedings in Business and Economics · doi:10.1007/978-3-319-40226-0 · Cited by 14 · see citing works
Audit GamesPreprint
Blocki, J., Christin, N., Datta, A., Procaccia, A. D., & Sinha, A. (2013) · arXiv (Cornell University) · Cited by 13 · see citing works
Flagged first, then by citations
9 of the 218 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Towards a standard for identifying and managing bias in artificial intelligence
Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022) · doi:10.6028/nist.sp.1270 · Cited by 548 · see citing works
“And without proper governance, excising context and flattening the categories into numerical constructs makes traceability more difficult [124].”
BackgroundMcIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
“Furthermore, AI Ethics involves ensuring traceability, auditability, and transparency throughout the model development lifecycle, employing practices such as algorithmic auditing, establishing ethics boards, and adhering to documentation standards and model cards [237], [236].”
MethodFedele, A., Punzi, C., & Tramacere, S. (2024) · Computer law & security review · doi:10.1016/j.clsr.2024.105986 · Cited by 53 · see citing works
Lewicki, K., Lee, M. S. A., Cobbe, J., & Singh, J. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581463 · Cited by 30 · see citing works
Assessing Ethical and Legal Implications for a Trustworthy Ai Development in EducationPreprint
Punzi, C., Fedele, A., & Tramacere, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4494528 · Cited by 1 · see citing works
- 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
- OpenAlex
- W3122480037
- Cited by
- 2
- References
- 218
- Citations by year
- 2024: 1 · 2023: 1
- FWCI
- 0.45
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Topics
- Social Sciences › Safety Research › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
