Cohort: ESOL 42
Topic of the thesis: Critical Thinking, Bias, and Inclusion in AI-Driven Human Resources Management
Abstract: The aim of this thesis is to analyse the relationship between critical thinking and the use of artificial intelligence in the field of human resource management, a relationship that has grown increasingly as algorithmic systems now have a pivotal role in recruitment, performance evaluation, training, and retention in organisations. The study contributes by addressing a theoretical and practical question currently not entirely addressed in research: that is, when does the effective, and not the formally present, operational use of critical thinking exist in HR decisions under algorithmic control, and how can it be applied? The study is grounded in a critical interdisciplinary analysis of the literature, drawing on cognitive psychology, philosophy of mind, and organisational theory, and also adding AI ethics and employment law to construct a cumulative and complete theoretical argument. Instead of merely describing the field, the analysis will question the current assumption embedded in current AI-driven HR practices, also arguing the normative position about the place of human agency within the field. Moreover, the research indicates three interdependent risks that algorithmic management creates for HR practice: automatic and chronic replication of historical biases into predictive systems, the intrusion of surveillance architecture into domains of working life previously considered private, and the deterioration of skilled judgment. In response to these risks, the thesis proposes a structured approach to critical human-AI interaction and a three-level governance framework operating at the macro, meso, and individual levels. The central conclusion is that critical thinking functions not as an optional complement to algorithmic HR management, but as its epistemological precondition, because if intellectual autonomy and metacognitive self-regulated structured questioning of algorithmic outputs are not actively exercised throughout its use, mere attempts at debiasing or regulatory compliance will not be sufficient to guarantee the fairness, transparency, and accountability that responsible AI in HR requires.
Research interests: Diversity, Equity and Inclusion (DEI) in Organizations; Artificial Intelligence and Algorithmic Management
Graduated from: Università degli Studi di Milano
Degrees obtained: BA: Philosophy; MA: Management of Human Resources
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