14 10.1163/22131035-14020001 | enigbokan who were predominantly men.67 Historical biases may therefore be inherited by algorithms based on historical data.68 Various issues pertaining to the data-entering procedure and the algorithm’s design itself may lead to discriminatory results.69 The first issue arises when training data is inaccurate, outdated, incomplete, or non-representative, or when there is ‘selection bias’, where certain groups are under or overrepresented in the input data.70 Given that an algorithm’s design reflects the designer’s values, the second instance may be the result of biased assumptions made by the developer throughout the model-building process, whether consciously or unconsciously.71 For instance, UK Home Office utilised a streaming tool to automatically sort visa applications and classify their risk, though the final decision of an application rests with human decision makers. The algorithm sorted visa applicants into different risk groups to determine high, medium and low levels of risk. This algorithm discriminated based on nationality to categorise applications. Some nationalities appeared to be automatically streamed into the high-risk group. Equality concerns were brought up by the disparate processing of applications.72 Even though ai may not be intended to be discriminatory, it may nevertheless result in discrimination since algorithms have the ability to reinforce preexisting biases and stereotypes, even when the data set does not explicitly contain particular information about the protected group.73 For instance, searches for names associated with black persons were more likely to be accompanied by advertisements featuring arrest records, even when no 67 68 69 70 71 72 73 ‘Why Amazon’s Automated Hiring Tool Discriminated Against Women’12 October 2018 <https://www.aclu.org/news/womens-rights/why-amazons-automated-hiring-tool -discriminated-against> accessed 30 September 2025. Alina Ko¨chling and Marius Claus Wehner, ‘Discriminated by an Algorithm: a Systematic Review of Discrimination and Fairness by Algorithmic Decision-making in the context of hr Recruitment and hr Development’ (2020)13 Business Research 795–848. Niklas, (n 50) at 520. Ibid, 522. Brent Daniel Mittelstadt and others, ‘The Ethics of Algorithms: Mapping the Debate’(2016) 3(2) Big Data & Society 1–15. The Digital Freedom Fund <https://digitalfreedomfund.org/wp-content/uploads/2022/01 /DFF-07-Foxglove-Update.pdf> accessed 30 September 2025. Noémi Nagy, ‘Humanity’s New Frontier: Human Rights Implications of Artificial Intelligence and new Technologies’ (2023) 64 Hungarian Journal of Legal Studies 241; Stéphanie Laulhé Shaelou and Yulia Razmetaeva, ‘Challenges to Fundamental Human Rights in the Age of Artificial Intelligence Systems: Shaping the Digital Legal Order while Upholding Rule of Law Principles and European Values’(2024) 24 era Forum 567–587. International Human Rights Law Review (2025) 1–31