Pokaż uproszczony rekord

dc.contributor.authorBogucki, Artur
dc.date.accessioned2025-08-04T10:36:11Z
dc.date.available2025-08-04T10:36:11Z
dc.date.issued2025-07-15
dc.identifier.issn0208-6069
dc.identifier.urihttp://hdl.handle.net/11089/56101
dc.description.abstractThis study examines the integration of artificial intelligence (AI) in tax law and administration, underscoring key ethical, legal, and socioeconomic dimensions. It explores how AI can improve tax compliance and foster innovation, yet simultaneously raising concerns regarding fairness, transparency, and accountability. Specific risks, including data bias, breaches of privacy, and over-reliance on automated risk-scoring, illustrate the need for robust legal frameworks such as the GDPR and the AI Act. Socioeconomic implications – notably labour displacement and income inequality – spotlight the necessity for equitable policies and responsible AI governance. Drawing on Ethical, Legal, and Social Aspects (ELSA) as well as Responsible Research and Innovation (RRI) frameworks, this research provides recommendations for a comprehensive approach, emphasising stakeholder engagement, transparency, and continuous oversight. Ultimately, a balanced blend of technological ingenuity and principled governance is essential to ensure that AI’s transformative potential truly serves the public interest in tax law and administration.en
dc.description.abstractNiniejsze opracowanie dotyczy zastosowania sztucznej inteligencji (AI) w administracji podatkowej, przez pryzmat zagadnień etycznych, prawnych i społeczno-ekonomicznych. Analiza sugeruje, że choć AI może usprawnić pobór podatków i wspierać innowacyjność, to jednocześnie rodzi obawy związane z kwestiami takimi jak rzetelność, przejrzystość i rozliczalność działań. Szczególne ryzyko wynika między innymi z obecności błędów w danych treningowych, zagrożeń dla prywatności i nadmiernego polegania na automatycznych systemach punktacji ryzyka, co podkreśla znaczenie koherentnych regulacji prawnych, w tym RODO i Aktu o Sztucznej Inteligencji. Społeczno-ekonomiczne skutki AI – zwłaszcza potencjalne wypieranie miejsc pracy oraz wzrost nierówności dochodowych – wskazują na potrzebę odpowiedzialnych regulacji i polityk publicznych. W niniejszym opracowaniu, w oparciu o ramy Etycznych, Prawnych i Społecznych Aspektów (ELSA) oraz Odpowiedzialnych Badań i Innowacji (RRI), proponuje się rekomendacje łączące zaangażowanie interesariuszy, transparentność oraz stały nadzór. Ostatecznie, jedynie połączenie innowacyjności technologicznej z zasadami prawnymi i etycznymi gwarantuje, że zastosowanie AI w operacji administracji podatkowej będzie służyć dobru społecznemu zgodnie z zasadami godnej zaufania sztucznej inteligencji.pl
dc.language.isoen
dc.publisherWydawnictwo Uniwersytetu Łódzkiegopl
dc.relation.ispartofseriesActa Universitatis Lodziensis. Folia Iuridicaen
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectAI Governanceen
dc.subjectTrustworthy AIen
dc.subjectbias mitigationen
dc.subjectAI Acten
dc.subjectalgorithmic transparencyen
dc.subjectzarządzanie sztuczną inteligencjąpl
dc.subjectgodna zaufania sztuczna inteligencjapl
dc.subjectakt o sztucznej inteligencjipl
dc.subjecttendencyjności uprzedzeńpl
dc.subjectprzejrzystość algorytmicznapl
dc.titleEthical, Legal, and Socioeconomic Aspects of Implementing Artificial Intelligence in Tax Administrationen
dc.title.alternativeEtyczne, prawne i społeczno-ekonomiczne aspekty wdrażania sztucznej inteligencji w administracji podatkowejpl
dc.typeArticle
dc.page.number19-36
dc.contributor.authorAffiliationSGH Warsaw School of Economicsen
dc.identifier.eissn2450-2782
dc.referencesAcemoglu, Daron. Sara Johnson. 2023. Power and Progress: Our Thousand-Year Struggle over Technology and Prosperity. London: Hachette UK.en
dc.referencesAlon-Barkat, Sigal. Madalina Busuioc. 2023. “Human–AI Interactions in Public Sector Decision Making: ‘Automation Bias’ and ‘Selective Adherence’ to Algorithmic Advice.” Journal of Public Administration Research and Theory 33(1): 153–169. https://doi.org/10.1093/jopart/muac007en
dc.referencesBevacqua, John. Vanessa Renolds. 2018. “The Digital Divide and Taxpayer Rights – Cautionary Findings from the United States.” eJournal of Tax Research 16: 714–739.en
dc.referencesBlank, Joshua D. Leigh Osofsky. 2021. “The Social Justice of Legal Drafting: Tax Law and Beyond.” In 114th Annual Conference on Taxation. Washington, DC: National Tax Association.en
dc.referencesBrownsword, Roger. 2024. “Law, Technology, and Our Governance Dilemma.” Laws 13(3): 30. https://doi.org/10.3390/laws13030030en
dc.referencesCitron, Danielle Keats. 2008. “Open Code Governance.” University of Chicago Legal Forum 2008: 355–387.en
dc.referencesCNIL (Commission Nationale de l’Informatique et des Libertés). 2022. AI: Ensuring GDPR Compliance. September 21, 2022. https://www.cnil.fr/en/ai-ensuring-gdprcomplianceen
dc.referencesCumberland, Torsten. 2024. “A Look at the Risks and Opportunities for AI in Tax Administrations.” OECD AI Policy Observatory. https://oecd.ai/en/wonk/risks-and-opportunities-ai-tax-administrationsen
dc.referencesde la Feria, Rita. M.A. Grau Ruiz. 2021. “The Robotisation of Tax Administration.” In International Conference on Inclusive Robotics for a Better Society. Edited by M.A. Grau Ruiz. 115–123. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-04305-5_19en
dc.referencesEngstrom, David Freeman. Daniel E. Ho. Catherine M. Sharkey. Mariano-Florentino Cuéllar. 2020. “Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies.” NYU School of Law. Public Law Research Paper No. 20–54. https://doi.org/10.2139/ssrn.3551505en
dc.referencesEuropean Commission. 2018a. Artificial Intelligence for Europe. COM(2018) 237 final. Brussels: European Commission. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2018%3A237%3AFINen
dc.referencesEuropean Commission. 2018b. “Commission Appoints Expert Group on AI and Launches the Euro-pean AI Alliance.” Digital Strategy. https://digital-strategy.ec.europa.eu/en/news/commission-appoints-expert-group-ai-and-launches-european-ai-allianceen
dc.referencesEuropean Commission. 2018c. Coordinated Plan on Artificial Intelligence. COM(2018) 795 final. Brussels: European Commission. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM%3A2018%3A795%3AFINen
dc.referencesEuropean Court of Human Rights (Grand Chamber). n.d. L.B. v. Hungary, Application No. 36345/16. https://hudoc.echr.coe.int/eng?i=001-212208en
dc.referencesEuropean Union. 2016. Regulation (EU) 2016/679 of the European Parliament and of the Council 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 (General Data Protection Regulation). Official Journal L119/1. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32016R0679en
dc.referencesHadwick, David. 2022. “Behind the One-Way Mirror: Reviewing the Legality of EU Tax Algorithmic Governance.” EC Tax Review 31(4): 184–201. https://doi.org/10.54648/ECTA2022019en
dc.referencesHadwick, David. 2024. “Slipping Through the Cracks: The Carve-outs for AI Tax Enforcement Systems in the EU AI Act.” European Papers 9(3): 936–955.en
dc.referencesHadwick, David. Shujing Lan. 2021. “Lessons to Be Learned from the Dutch Childcare Allowance Scandal: A Comparative Review of Algorithmic Governance by Tax Administrations in the Netherlands, France and Germany.” World Tax Journal 13(4): 609–645. https://doi.org/10.59403/27410paen
dc.referencesInternal Revenue Service (IRS). 2024. IRS Communication on Data Disclosure. March 10, 2024. https://www.irs.gov/newsroom/irs-communication-on-data-disclosureen
dc.referencesKeen, Michael. Joel Slemrod. 2017. “Optimal Tax Administration.” Journal of Public Economics 152: 133–142. https://doi.org/10.1016/j.jpubeco.2017.04.006en
dc.referencesKhogali, H.O.S. Mekid. 2023. “The Blended Future of Automation and AI: Examining Some Long-term Societal and Ethical Impact Features.” Technology in Society 73: 102–232. https://doi.org/10.1016/j.techsoc.2023.102232en
dc.referencesKleinberg, Jon. Himabindu Lakkaraju. Jure Leskovec. Jens Ludwig. Sendhil Mullainathan. 2018. “Human Decisions and Machine Predictions.” The Quarterly Journal of Economics 133(1): 237–293. https://doi.org/10.3386/w23180en
dc.referencesKuźniacki, Błażej. Mariana Almada. Krzysztof Tyliński. Łukasz Górski. Barbara Winogradska. Rik Zeldenrust. 2022. “Towards eXplainable Artificial Intelligence (XAI) in Tax Law: The Need for a Minimum Legal Standard.” World Tax Journal 14: 573–616. https://doi.org/10.59403/2yhh9paen
dc.referencesMaat, E.P. 2022. “Google v. CNIL: A Commentary on the Territorial Scope of the Right to Be Forgotten.” European Review of Private Law 30(2): 241–252. https://doi.org/10.54648/ERPL2022013en
dc.referencesMayson, Sandra G. 2018. “Bias In, Bias Out.” Yale Law Journal 128: 2218–2230.en
dc.referencesMcCarty, L. Thorne. 1977. “Reflections on ‘Taxman’: An Experiment in Artificial Intelligence and Legal Reasoning.” Harvard Law Review 90: 837–893. https://doi.org/10.2307/1340132en
dc.referencesMIT Future Tech. 2024a. The AI Risk Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks from Artificial Intelligence. https://airisk.mit.edu/en
dc.referencesMIT Future Tech. 2024b. AI Risk Repository. https://airisk.mit.edu/en
dc.referencesOECD (Organisation for Economic Co-operation and Development). 2020. Forum on Tax Administration: Tax Administration 3.0 – The Digital Transformation of Tax Administration. https://www.oecd.org/tax/forum-on-tax-administration/publications-and-products/tax-administration-3-0-the-digital-transformation-of-tax-administration.pdfen
dc.referencesOkunogbe, Omowunmi. Victor Pouliquen. 2022. “Technology, Taxation, and Corruption: Evidence from the Introduction of Electronic Tax Filing.” American Economic Journal: Economic Policy 14(1): 341–372. https://doi.org/10.1257/pol.20200123en
dc.referencesPassi, Samir. Maria Vorvoreanu. 2022. Overreliance on AI Literature Review. Microsoft Research. https://www.microsoft.com/en-us/research/uploads/prod/2022/06/Aether-Overreliance-on-AI-Review-Final-6.21.22.pdfen
dc.referencesPeeters, Brigitte. 2024. “European Law Restrictions on Tax Authorities’ Use of Artificial Intelligence Systems: Reflections on Some Recent Developments.” EC Tax Review 33(2). https://doi.org/10.54648/ECTA2024006en
dc.referencesRanchordás, Sofia. 2022. “Connected but Still Excluded? Digital Exclusion Beyond Internet Access.” In The Cambridge Handbook of Life Sciences, Information Technology and Human Rights. Edited by Marcello Ienca, Oreste Pollicino, Laura Liguori, Portolano Cavallo, Elisa Stefanini, Roberto Andorno. 244–258. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781108775038.020en
dc.referencesRanchordas, Sofia. Luisa Scarcella. 2021. “Automated Government for Vulnerable Citizens: Intermediating Rights.” William & Mary Bill of Rights Journal 30: 373–408. https://doi.org/10.2139/ssrn.3938032en
dc.referencesRenda, Andrea. 2024. Towards a European Large-Scale Initiative on Artificial Intelligence. Brussels: Centre for European Policy Studies (CEPS). CEPS Deep Dive. https://www.ceps.eu/ceps-publications/towards-a-european-large-scale-initiative-on-artificial-intelligence/en
dc.referencesRenda, Andrea. Pierre-Alexandre Balland. 2023. Forge Ahead or Fall Behind: Why We Need a United Europe of Artificial Intelligence. CEPS Explainer. https://cdn.ceps.eu/wp-content/uploads/2023/11/CEPS-Explainer-2023-13_United-Europe-of-Artificial-Intelligence.pdfen
dc.referencesRizzo, Alessandro. Gauri Hassan. 2024. AI Risk Management in Tax Audits: A Comparative Review of the EU and US Regulatory Approaches. Unpublished manuscript.en
dc.referencesRodrik, Dani. Stefanie Stantcheva. 2021. “Fixing Capitalism’s Good Jobs Problem.” Oxford Review of Economic Policy 37(4): 824–837. https://doi.org/10.1093/oxrep/grab024en
dc.referencesScarcella, Luisa. 2019. “Tax Compliance and Privacy Rights in Profiling and Automated Decision Making.” Internet Policy Review 8(4). https://doi.org/10.14763/2019.4.1441en
dc.referencesSunstein, Cass R. 2021. “Governing by Algorithm? No Noise and (Potentially) Less Bias.” Duke Law Journal 71: 1175–1213. https://doi.org/10.2139/ssrn.3925240en
dc.contributor.authorEmailaboguck@sgh.waw.pl
dc.identifier.doi10.18778/0208-6069.110.12
dc.relation.volume110


Pliki tej pozycji

Thumbnail

Pozycja umieszczona jest w następujących kolekcjach

Pokaż uproszczony rekord

https://creativecommons.org/licenses/by-nc-nd/4.0
Poza zaznaczonymi wyjątkami, licencja tej pozycji opisana jest jako https://creativecommons.org/licenses/by-nc-nd/4.0