A framework to contest and justify algorithmic decisions
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Henin and Le Métayer's technical framework for contestability is difficult to implement in practice because it requires a complete logical specification of decision norms and an ordering relation over argument strengths.
“While their idea of building upon norms and justifications is conceptually sound (Aler Tubella et al. 2020), their technical framework is difficult to implement in practice: it requires a complete logical specification of decision norms as well as an ordering relation over argument strengths.”
From Explainable AI Isn't Enough! Rethinking Algorithmic Contestability (2026) · cites this paper as Henin and Le Métayer 2021
Broad requirements such as transparency, fairness and accountability are generally ill defined, seldom required by law and difficult to implement.
“Broad requirements such as transparency, fairness and accountability are often presented as ways to limit these risks but they are generally ill defined, seldom required by law and difficult to implement[19, 42].”
From Beyond explainability: justifiability and contestability of algorithmic decision systems (2022) · cites this paper as [42]
Cites
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
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 · cited 2× · see citing works
“) and datasheets for datasets [26] (which can be used to describe the characteristics of a dataset) in order to provide an interactive and easily accessible form of accountability.”
BackgroundMittelstadt, B., Russell, C., & Wachter, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287574 · Cited by 794 · see citing works
Rationalizing Neural Predictions
Lei, T., Barzilay, R., & Jaakkola, T. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1011 · Cited by 777 · see citing works
Accountability in algorithmic decision making
Diakopoulos, N. (2016) · Communications of the ACM · doi:10.1145/2844110 · Cited by 763 · 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
Algorithmic Accountability and Public Reason
Binns, R. (2018) · Philosophy & Technology · doi:10.1007/s13347-017-0263-5 · Cited by 457 · see citing works
*‘Reasons for Action: Justification, Motivation, Explanation’
Alvarez, M. (2016) · Research Portal (King's College London) · Cited by 242 · see citing works
Explanation in Case-Based Reasoning—Perspectives and Goals
Sørmo, F., Cassens, J., & Aamodt, A. (2005) · Artificial Intelligence Review · doi:10.1007/s10462-005-4607-7 · Cited by 239 · see citing works
Engineering and the Problem of Moral Overload
Van den Hoven, J., Lokhorst, G.-J., & Van de Poel, I. (2012) · Science and Engineering Ethics · doi:10.1007/s11948-011-9277-z · Cited by 194 · see citing works
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations
Laugel, T., Lesot, M.-J., Marsala, C., Renard, X., & Detyniecki, M. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/388 · Cited by 194 · see citing works
Lessons learned from AI ethics principles for future actions
Hickok, M. (2021) · AI and Ethics · doi:10.1007/s43681-020-00008-1 · Cited by 168 · cited 1× · see citing works
“We hold that this contribution is a first step to address the call expressed in [12] “for tangible action to move from high-level abstractions and conceptual arguments towards applying ethics in practice and creating accountability mechanisms.””
BackgroundThe four dimensions of contestable AI diagnostics - A patient-centric approach to explainable AI
Ploug, T., & Holm, S. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101901 · Cited by 152 · see citing works
Toward Artificial Argumentation
Atkinson, K., Baroni, P., Giacomin, M., Hunter, A., Prakken, H., Reed, C., Simari, G., Thimm, M., & Villata, S. (2017) · AI Magazine · doi:10.1609/aimag.v38i3.2704 · Cited by 142 · see citing works
Designing Contestability: Interaction Design, Machine Learning, and Mental Health
Hirsch, T., Merced, K., Narayanan, S., Imel, Z. E., & Atkins, D. C. (2017) · Proceedings of the 2017 Conference on Designing Interactive Systems · doi:10.1145/3064663.3064703 · Cited by 110 · see citing works
Explaining and Justifying Expert Consulting Programs
Swartout, W. (1985) · Computers and medicine · doi:10.1007/978-1-4612-5108-8_15 · Cited by 102 · see citing works
Human intervention in automated decision-making: Toward the construction of contestable systems
Almada, M. (2019) · Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law · doi:10.1145/3322640.3326699 · Cited by 95 · see citing works
Human-Centric Justification of Machine Learning Predictions
Biran, O., & McKeown, K. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/202 · Cited by 84 · see citing works
Interactive and interpretable machine learning models for human machine collaboration
Kim, B. (2015) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 83 · see citing works
Irving, G., Christiano, P., & Amodei, D. (2018) · arXiv · doi:10.48550/arxiv.1805.00899 · Cited by 71 · see citing works
A Grounded Interaction Protocol for Explainable Artificial Intelligence
Madumal, P., Miller, T., Sonenberg, L., & Vetere, F. (2019) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/dgmp4001 · Cited by 60 · see citing works
Combining explanation and argumentation in dialogue
Bex, F., & Walton, D. (2016) · Argument & Computation · doi:10.3233/aac-160001 · Cited by 53 · see citing works
The need for user models in generating expert system explanation
Kass, R., & Finin, T. (1988) · Cited by 44 · see citing works
The role of user models in cooperative interactive systems
Kass, R., & Finin, T. (1989) · International Journal of Intelligent Systems · doi:10.1002/int.4550040106 · Cited by 43 · see citing works
Mulligan, D. K., Kluttz, D., & Kohli, N. (2019) · doi:10.2139/ssrn.3311894 · Cited by 32 · see citing works
Explainable, Normative, and Justified Agency
Langley, P. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019775 · Cited by 25 · see citing works
Liao, B., Anderson, M., & Anderson, S. L. (2021) · AI and Ethics · doi:10.1007/s43681-020-00001-8 · Cited by 18 · see citing works
Varieties of Justification in Machine Learning
Corfield, D. (2010) · Minds and Machines · doi:10.1007/s11023-010-9191-1 · Cited by 12 · see citing works
Transparency As Design Publicity: Explaining and Justifying Inscrutable AlgorithmsPreprint
Loi, M., Ferrario, A., & Viganò, E. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3404040 · Cited by 9 · see citing works
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Longo, L., Brčić, M., Cabitza, F., Choi, J., Confalonieri, R., Ser, J. D., Guidotti, R., Hayashi, Y., Herrera, F., Holzinger, A., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102301 · Cited by 648 · see citing works
Beyond explainability: justifiability and contestability of algorithmic decision systems
Henin, C., & Le Métayer, D. (2022) · AI & SOCIETY · doi:10.1007/s00146-021-01251-8 · Cited by 71 · see citing works
Self-citationFMEA-AI: AI fairness impact assessment using failure mode and effects analysis
Li, J. J., & Chignell, M. (2022) · AI and Ethics · doi:10.1007/s43681-022-00145-9 · Cited by 29 · see citing works
Longo, L., Brčić, M., Cabitza, F., Choi, J., Confalonieri, R., Ser, J. D., Guidotti, R., Hayashi, Y., Herrera, F., Holzinger, A., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.19775 · Cited by 14 · see citing works
Calvert, S. C. (2025) · Science and Engineering Ethics · doi:10.1007/s11948-025-00554-z · Cited by 12 · see citing works
Malgieri, G. (2021) · Law and Business · doi:10.2478/law-2021-0003 · Cited by 10 · see citing works
Industry 5.0 and Artificial Intelligence
Priya, B., Sharma, V., & Sharma, R. (2025) · doi:10.1002/9781394197798.ch7 · Cited by 6 · see citing works
Maoro, F., & Geierhos, M. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1602998 · Cited by 5 · see citing works
Colmenarejo, A. B., State, L., & Comandé, G. (2025) · International Review of Law Computers & Technology · doi:10.1080/13600869.2025.2497633 · Cited by 3 · see citing works
Explainable AI Isn't Enough! Rethinking Algorithmic Contestability
Freiesleben, T., Meding, K., & König, G. (2026) · arXiv · doi:10.48550/arxiv.2605.16041
Nichtwissen bei maschinellem Lernen
Kostov, I. (2023) · Mohr Siebeck eBooks · doi:10.1628/978-3-16-162610-4
Beyond Transparency: Democratizing Algorithmic Governance
Pamuk, Z. (2025) · The Journal of Politics · doi:10.1086/738971
Accuracy is not all you need! The Reasons to Require AI Explainability
Buijsman, S. (2026) · Minds and Machines · doi:10.1007/s11023-026-09768-x
Hamamra, B. (2026) · AI and Ethics · doi:10.1007/s43681-026-01311-z
- Authors
- Henin, C., & Le Métayer, D.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Corresponding author
- Henin, Clément
- ORCID
- Henin, Clément
- Venue
- AI and Ethics
- Published
- 2021-05-04
- Volume
- 1
- Issue
- 4
- Pages
- 463-476
- ISSN
- 2730-5953 (ISSN-L)
- 2730-5961
- OpenAlex
- W3158668091
- Cited by
- 14
- References
- 31
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 1 · 2023: 3 · 2022: 1 · 2021: 2
- FWCI
- 0.62
- Type
- journal article
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- Locations
- AI and Ethics (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- automated decision systems, explanation, norms, proof-of-concept implementation
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