Algorithmic Bias in Automated Decision-Making: Hidden Risks through the Perspective of AI-Driven Tax Compliance. The EU’S Response from GDPR to the AI Act

Niki Georgiadou (), Georgios Thanasas (), Konstantinos Theodoridis () and Athanasios Mandilas ()
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Niki Georgiadou: University of Patras, Department of Management Science and Technology
Georgios Thanasas: University of Patras, Department of Management Science and Technology
Konstantinos Theodoridis: University of Patras, Department of Management Science and Technology
Athanasios Mandilas: Democritus University of Thrace, Department of Accounting and Finance

A chapter in Innovations in Finance, 2026, pp 115-124 from Springer

Abstract: Abstract This study discusses the common problem of algorithmic bias in artificial intelligence (AI) systems and its implications on tax compliance and enforcement. Although AI-driven decision-making has the potential for greater efficiency and scalability, it also carries the potential for perpetuating social disparities via biased algorithms. It lights the dilemma of balancing innovation and fairness, along with the necessity of responsible artificial intelligence practices for fair and transparent outcomes especially on tax compliance studying the regulatory responses taken by the European Union (EU), namely through the General Data Protection Regulation (GDPR) and the AI Act proposal.

Keywords: Algorithmic bias; Artificial Intelligence (AI); Tax Compliance; Tax Enforcement; GDPR; AI Act; Automated decision-making; Machine learning (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-19314-8_11

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DOI: 10.1007/978-3-032-19314-8_11

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