{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T18:34:10Z","timestamp":1792953250167,"version":"4.5.0"},"reference-count":37,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T00:00:00Z","timestamp":1792886400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    This article examines fair and trustworthy artificial intelligence in public administration, presenting Estonia as the main case and EU law as the wider legal frame. It builds on the EquiTech project, which has been successfully co-implemented by the TalTech Department of Law and its project partners. The project materials, including training materials for public servants and data scientists, the project proposal, and related academic work, form an important empirical and normative basis for the article. The central claim is that fairness in public-sector artificial intelligence (AI) cannot be reduced to technical accuracy, general transparency, or compliance with a single legal instrument. Wachter, Mittelstadt and Russell\u2019s work on why fairness cannot be automated is the paper\u2019s main theoretical anchor. Their concept of contextual equality helps explain why EU non-discrimination law does not translate easily into fixed fairness metrics, even though statistical evidence remains necessary. In the Estonian setting, this problem is visible in the gap between an advanced digital state and a still developing framework for assessing bias and discrimination risks across the full lifecycle of automated decision-making. The article argues that trustworthy public-sector AI requires legal authority, data governance, bias assessment, meaningful human oversight, administrative contestability, and institutional capacity. The EU\n                    <jats:italic>Artificial Intelligence Act<\/jats:italic>\n                    is a major step in this direction, but it cannot by itself solve the legal and practical problems raised by algorithmic discrimination in public administration.\n                  <\/jats:p>","DOI":"10.2478\/bjes-2026-0025","type":"journal-article","created":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T18:00:26Z","timestamp":1792951226000},"page":"291-310","source":"Crossref","is-referenced-by-count":0,"title":["Fair AI in public administration: Estonia and EU law"],"prefix":"10.2478","volume":"16","author":[{"given":"Archil","family":"Chochia","sequence":"first","affiliation":[{"name":"Department of Law , Tallinn University of Technology Ehitajate tee 5 Tallinn , Estonia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,10,25]]},"reference":[{"key":"2026102518001299351_j_bjes-2026-0025_ref_001","doi-asserted-by":"crossref","unstructured":"Adams-Prassl, J., Binns, R. and Kelly-Lyth, A. (2023) \u2018Directly discriminatory algorithms\u2019, Modern Law Review, 86(1), pp. 144\u2013175. Available at: https:\/\/doi.org\/10.1111\/1468-2230.12759","DOI":"10.1111\/1468-2230.12759"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_002","unstructured":"AI HLEG (2019) Ethics guidelines for trustworthy AI. European Commission High-Level Expert Group on AI."},{"key":"2026102518001299351_j_bjes-2026-0025_ref_003","doi-asserted-by":"crossref","unstructured":"Barocas, S. and Selbst, A.D. (2016) \u2018Big data\u2019s disparate impact\u2019, California Law Review, 104(3), pp. 671\u2013732. Available at: https:\/\/doi.org\/10.2139\/ssrn.2477899","DOI":"10.2139\/ssrn.2477899"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_004","doi-asserted-by":"crossref","unstructured":"Bell, M. (2002) Anti-discrimination law and the European Union. Oxford University Press. Available at: https:\/\/doi.org\/10.1093\/acprof:oso\/9780199244508.001.0001","DOI":"10.1093\/acprof:oso\/9780199244508.001.0001"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_005","doi-asserted-by":"crossref","unstructured":"Binns, R. (2018) \u2018Algorithmic accountability and public reason\u2019, Philosophy & Technology, 31(4), pp. 543\u2013556. Available at: https:\/\/doi.org\/10.1007\/s13347-017-0263-5","DOI":"10.1007\/s13347-017-0263-5"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_006","doi-asserted-by":"crossref","unstructured":"Burrell, J. (2016) \u2018How the machine \u201cthinks\u201d: understanding opacity in machine learning algorithms\u2019, Big Data & Society, 3(1), pp. 1\u201312. Available at: https:\/\/doi.org\/10.1177\/2053951715622512","DOI":"10.1177\/2053951715622512"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_007","unstructured":"Charter of Fundamental Rights of the European Union (2012) Official Journal C 326, pp. 391\u2013407."},{"key":"2026102518001299351_j_bjes-2026-0025_ref_008","doi-asserted-by":"crossref","unstructured":"Chouldechova, A. (2017) \u2018Fair prediction with disparate impact: a study of bias in recidivism prediction instruments\u2019, Big Data, 5(2), pp. 153\u2013163. Available at: https:\/\/doi.org\/10.1089\/big.2016.0047","DOI":"10.1089\/big.2016.0047"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_009","unstructured":"Citron, D.K. and Pasquale, F. (2014) \u2018The scored society: due process for automated predictions\u2019, Washington Law Review, 89(1), pp. 1\u201333."},{"key":"2026102518001299351_j_bjes-2026-0025_ref_010","doi-asserted-by":"crossref","unstructured":"Datta, A., Fredrikson, M., Ko, G., Mardziel, P. and Sen, S. (2017) \u2018Proxy non-discrimination in data-driven systems: theory and experiments with machine learnt programs\u2019, ArXiv. Available at: https:\/\/doi.org\/10.1145\/3133956.3134097","DOI":"10.1145\/3133956.3134097"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_011","doi-asserted-by":"crossref","unstructured":"Ellis, E. and Watson, P. (2012) EU anti-discrimination law. 2nd edn. Oxford University Press. Available at: https:\/\/doi.org\/10.1093\/acprof:oso\/9780199698462.001.0001","DOI":"10.1093\/acprof:oso\/9780199698462.001.0001"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_012","unstructured":"EquiTech project (no date) Improving response to risks of discrimination, bias and intolerance in automated decision-making systems to promote equality. European Commission, CERV-2023-EQUAL, Grant Agreement No. 101144709."},{"key":"2026102518001299351_j_bjes-2026-0025_ref_013","unstructured":"Eubanks, V. (2018) Automating inequality: how high-tech tools profile, police, and punish the poor. Picador, St Martin\u2019s Press."},{"key":"2026102518001299351_j_bjes-2026-0025_ref_014","unstructured":"Gerards, J. and Xenidis, R. (2021) Algorithmic discrimination in Europe: challenges and opportunities for gender equality and non-discrimination law. Publications Office of the European Union."},{"key":"2026102518001299351_j_bjes-2026-0025_ref_015","doi-asserted-by":"crossref","unstructured":"G\u00f6ksal, S.I., Solarte-Vasquez, M.C. and Chochia, A. (2024) \u2018The EU AI Act\u2019s alignment within the European Union\u2019s regulatory framework on artificial intelligence\u2019, International and Comparative Law Review, 24(2), pp. 25\u201353. Available at: https:\/\/doi.org\/10.2478\/iclr-2024-0017","DOI":"10.2478\/iclr-2024-0017"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_016","doi-asserted-by":"crossref","unstructured":"Hacker, P. (2018) \u2018Teaching fairness to artificial intelligence: existing and novel strategies against algorithmic discrimination under EU law\u2019, Common Market Law Review, 55(4), pp. 1143\u20131185. Available at: https:\/\/doi.org\/10.54648\/COLA2018095","DOI":"10.54648\/COLA2018095"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_017","doi-asserted-by":"crossref","unstructured":"Joamets, K. (2025) \u2018Towards fair AI in Estonia\u2019s public service: discussing and disseminating bias prevention in automated decision-making\u2019, TalTech Journal of European Studies, 15(3), pp. 201\u2013224. Available at: https:\/\/doi.org\/10.2478\/bjes-2025-0037","DOI":"10.2478\/bjes-2025-0037"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_018","doi-asserted-by":"crossref","unstructured":"Kerikm\u00e4e, T. and P\u00e4rn-Lee, E. (2021) \u2018Legal dilemmas of Estonian artificial intelligence strategy: in between of e-society and global race\u2019, AI & Society, 36, pp. 561\u2013572. Available at: https:\/\/doi.org\/10.1007\/s00146-020-01009-8","DOI":"10.1007\/s00146-020-01009-8"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_019","doi-asserted-by":"crossref","unstructured":"Kerikm\u00e4e, T., Joamets, K. and Metin, E. (2025) \u2018Automated decision-making in Estonia\u2019s public sector: innovation, regulation, and challenges about non-discrimination\u2019, in F. Decarolis, B. Marchetti and L. Torchia (eds) European digital regulation and its impact on Member States, European Union and its neighbours in a globalized world, 27, pp. 149\u2013186. Springer. Available at: https:\/\/doi.org\/10.1007\/978-3-032-06490-5_6","DOI":"10.1007\/978-3-032-06490-5_6"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_020","unstructured":"Kleinberg, J., Mullainathan, S. and Raghavan, M. (2017) \u2018Inherent trade-offs in the fair determination of risk scores\u2019, Proceedings of Innovations in Theoretical Computer Science, pp. 1\u201323. Available at: https:\/\/doi.org\/10.48550\/arXiv.1609.05807"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_021","doi-asserted-by":"crossref","unstructured":"Laux, J., Wachter, S. and Mittelstadt, B. (2024) \u2018Trustworthy artificial intelligence and the European Union AI Act: on the conflation of trustworthiness and acceptability of risk\u2019, Regulation & Governance, 18(1), pp. 3\u201332. Available at: https:\/\/doi.org\/10.1111\/rego.12512","DOI":"10.1111\/rego.12512"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_022","unstructured":"Lips, S. and Joamets, K. (2025a) Educational material for data scientists, data protection specialists and developers. Project 101144709, EquiTech. Tallinn University of Technology. Unpublished training material."},{"key":"2026102518001299351_j_bjes-2026-0025_ref_023","unstructured":"Lips, S. and Joamets, K. (2025b) Study material for public servants. Project 101144709, EquiTech. Tallinn University of Technology. Unpublished training material."},{"key":"2026102518001299351_j_bjes-2026-0025_ref_024","unstructured":"Mittelstadt, B. (2016) \u2018Automation, algorithms, and politics: auditing for transparency in content personalization systems\u2019, International Journal of Communication, 10, pp. 4991\u20135002."},{"key":"2026102518001299351_j_bjes-2026-0025_ref_025","unstructured":"O\u2019Neil, C. (2016) Weapons of math destruction: how big data increases inequality and threatens democracy. Crown Publishing Group."},{"key":"2026102518001299351_j_bjes-2026-0025_ref_026","doi-asserted-by":"crossref","unstructured":"Pasquale, F. (2015) The black box society: the secret algorithms that control money and information. Harvard University Press. Available at: https:\/\/doi.org\/10.4159\/harvard.9780674736061","DOI":"10.4159\/harvard.9780674736061"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_027","doi-asserted-by":"crossref","unstructured":"Pilving, I. and Mikiver, M. (2020) \u2018A Kratt as an administrative body: algorithmic decisions and principles of administrative law\u2019, Juridica International, 29, pp. 47\u201361. Available at: https:\/\/doi.org\/10.12697\/JI.2020.29.05","DOI":"10.12697\/JI.2020.29.05"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_028","unstructured":"\u2018Council regulation (EU) 2016\/679 of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95\/46\/EC (General Data Protection Regulation)\u2019 (2016) Official Journal L 119. Available at: http:\/\/data.europa.eu\/eli\/reg\/2016\/679\/oj (Accessed: 5 August 2026)."},{"key":"2026102518001299351_j_bjes-2026-0025_ref_029","unstructured":"\u2018Council regulation (EU) 2024\/1689 of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No. 300\/2008, (EU) No. 167\/2013, (EU) No. 168\/2013, (EU) 2018\/858, (EU) 2018\/1139 and (EU) 2019\/2144 and Directives 2014\/90\/EU, (EU) 2016\/797 and (EU) 2020\/1828 (Artificial Intelligence Act)\u2019 (2024) Official Journal L 2024\/1689. Available at: http:\/\/data.europa.eu\/eli\/reg\/2024\/1689\/oj (Accessed: 5 August 2026)."},{"key":"2026102518001299351_j_bjes-2026-0025_ref_030","doi-asserted-by":"crossref","unstructured":"Selbst, A.D., Boyd, D., Friedler, S.A., Venkatasubramanian, S. and Vertesi, J. (2019) \u2018Fairness and abstraction in sociotechnical systems\u2019, Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, pp. 59\u201368. Available at: https:\/\/doi.org\/10.1145\/3287560.3287598","DOI":"10.1145\/3287560.3287598"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_031","doi-asserted-by":"crossref","unstructured":"Smuha, N.A. (2021) \u2018From a \u201crace to AI\u201d to a \u201crace to AI regulation\u201d: regulatory competition for artificial intelligence\u2019, Law, Innovation and Technology, 13(1), pp. 57\u201384. Available at: https:\/\/doi.org\/10.1080\/17579961.2021.1898300","DOI":"10.1080\/17579961.2021.1898300"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_032","doi-asserted-by":"crossref","unstructured":"Wachter, S. (2020) \u2018Affinity profiling and discrimination by association in online behavioural advertising\u2019, Berkeley Technology Law Journal, 35(2), pp. 367\u2013430. Available at: https:\/\/doi.org\/10.2139\/ssrn.3388639","DOI":"10.2139\/ssrn.3388639"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_033","doi-asserted-by":"crossref","unstructured":"Wachter, S., Mittelstadt, B. and Floridi, L. (2017) \u2018Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation\u2019, International Data Privacy Law, 7(2), pp. 76\u201399. Available at: https:\/\/doi.org\/10.1093\/idpl\/ipx005","DOI":"10.1093\/idpl\/ipx005"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_034","doi-asserted-by":"crossref","unstructured":"Wachter, S., Mittelstadt, B. and Russell, C. (2018) \u2018Counterfactual explanations without opening the black box: automated decisions and the GDPR\u2019, Harvard Journal of Law & Technology, 31(2), pp. 841\u2013887. Available at: https:\/\/doi.org\/10.2139\/ssrn.3063289","DOI":"10.2139\/ssrn.3063289"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_035","doi-asserted-by":"crossref","unstructured":"Wachter, S., Mittelstadt, B. and Russell, C. (2021) \u2018Why fairness cannot be automated: bridging the gap between EU non-discrimination law and AI\u2019, Computer Law & Security Review, 41, article 105567. Available at: https:\/\/doi.org\/10.1016\/j.clsr.2021.105567","DOI":"10.1016\/j.clsr.2021.105567"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_036","doi-asserted-by":"crossref","unstructured":"Weerts, H., Xenidis, R., Tarissan, F., Palmer Olsen, H. and Pechenizkiy, M. (2023) \u2018Algorithmic unfairness through the lens of EU non-discrimination law: or why the law is not a decision tree\u2019, Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pp. 805\u2013816. Available at: https:\/\/doi.org\/10.1145\/3593013.3594044","DOI":"10.1145\/3593013.3594044"},{"key":"2026102518001299351_j_bjes-2026-0025_ref_037","doi-asserted-by":"crossref","unstructured":"Zolkin, V., Chochia, A. and Hoffmann, T. (2023) \u2018Automated decision-making in the EU Member State\u2019s public administration: the compliance of automated decisions of the Estonian Unemployment Insurance Fund with Estonian administrative procedure law\u2019, European Studies: The Review of European Law, Economics and Politics, 10(2), pp. 178\u2013202. Available at: https:\/\/doi.org\/10.2478\/eustu-2023-0017","DOI":"10.2478\/eustu-2023-0017"}],"container-title":["TalTech Journal of European Studies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/reference-global.com\/pdf\/10.2478\/bjes-2026-0025","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T18:00:32Z","timestamp":1792951232000},"score":1,"resource":{"primary":{"URL":"https:\/\/reference-global.com\/article\/10.2478\/bjes-2026-0025"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,25]]},"references-count":37,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,10,25]]},"published-print":{"date-parts":[[2026,12,1]]}},"alternative-id":["10.2478\/bjes-2026-0025"],"URL":"https:\/\/doi.org\/10.2478\/bjes-2026-0025","relation":{},"ISSN":["2674-4619"],"issn-type":[{"value":"2674-4619","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,25]]}}}