Οι αρχές/απαιτήσεις της δικαιοσύνης και της ισότητας/μη διάκρισης κατά τη χρήση τεχνητής νοημοσύνης στο πλαίσιο της εργασίας/των εργασιακών σχέσεων
The principles/requirements of fairness and equality/non-discrimination when using artificial intelligence in the context of work/employment relations

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Keywords
Τεχνητή νοημοσύνη ; AI ; Αρχή της δικαιοσύνης ; Εργασιακές σχέσεις ; Artificial intelligenceAbstract
We are currently experiencing the Fourth Industrial Revolution, which includes innovative and disruptive technologies. At the center of attention recently is artificial intelligence (AI), a technology capable of replicating human cognitive functions. AI is also being applied in the sensitive area of work, giving rise to discussions about algorithmic management of labor relations. Algorithmic decisions and recommendations offer multiple benefits, but they also raise significant ethical concerns, particularly related to algorithmic bias. Algorithms that embed subconscious prejudices or are based on biased data tend to favor certain social groups over others, thereby worsening existing injustices and inequalities in society. Ensuring fairness in algorithms is both an ethical and a legal obligation.
In this study, we will examine the concept of artificial intelligence, the categories into which it is divided, and how it functions. We will particularly focus on the issue of algorithmic bias, analyzing the causes that lead to it and mentioning the specific case of indirect algorithmic bias. Next, we will explore the concept of fairness in AI systems, not only as a legal requirement but also as an ethical principle and technical standard. Through practical examples, we will present cases where the use of AI in employment relations has led to instances of algorithmic bias, including a case study that highlights the challenges in achieving fair algorithmic decisions. Additionally, we will review the existing legal framework aimed at protecting workers from unfair algorithmic decisions. This includes the General Data Protection Regulation, anti-discrimination legislation from the European Union and Greece, the AI Act, the Directive on platform workers, and Law 4961/2022. We will analyze how this framework seeks to ensure fairness in algorithmic processes through both preventive and corrective measures. Finally, we will present conclusions and reflections that emerged from the examination of these topics.