The Discriminatory Nature of AI: A Comprehensive Analysis through 5Ws and PESTEL Frameworks

This study investigates the discriminatory nature of Artificial Intelligence (AI) through a dual analytical approach: the 5Ws framework (What, When, Where, Why, Who) and PESTEL analysis (Political, Economic, Social, Technological, Environmental, Legal). Findings reveal systemic biases in AI algorithms, with 78% of cases linked to non-representative training data (Mehrabi et al., 2021). The research highlights AI’s violation of UNSDG 5 (Gender Equality) and UNSDG 10 (Reduced Inequalities), demonstrating how algorithmic discrimination exacerbates social inequities. Through a Utilitarian ethical lens, AI’s current implementation is deemed unethical due to its net negative societal impact. The paper proposes two actionable solutions: (1) multi-stakeholder awareness campaigns and (2) incentive-based regulatory frameworks. This work contributes to the ongoing discourse on ethical AI by quantifying discrimination patterns and offering policy-ready recommendations.