Kevin Robinson

ALT 1 Introduction Fairness research in Machine Learning (ML) has seen rapid growth in recent years; however, it remains largely rooted in Western concerns and histories: the injustices they focus on (e.g., along race and gender), the datasets they study (e.g., ImageNet), the measurement scales they use (e.g., Fitzpatrick), and the legal tenets they draw from (e.g., equal opportunity). This implicitly Western take is troublingly becoming a universal ethical framework for ML, e.g., AI strategies from India [1], Tunisia [51], and Mexico [32] all derive from this work, but fails to account for the several assumptions conventional algorithmic fairness makes about the availability and efficacy of the surrounding institutions and infrastructures. However, these infrastructures, values, and legal systems cannot be naively generalised to various non-Western countries.