Dear Rahat Hameed

Achieving a fundamental and universally-agreed definition of fairness in AI is highly challenging due to the inherently subjective and context-dependent nature of fairness, which varies across cultures, societies, and applications. While technical frameworks and mathematical definitions of fairness (e.g., demographic parity, equalized odds) exist, they often conflict with one another and cannot simultaneously satisfy all ethical, legal, and social considerations. Furthermore, fairness is deeply intertwined with values, biases, and trade-offs that differ among stakeholders, making consensus difficult. While ongoing research and interdisciplinary collaboration aim to refine and standardize fairness principles, a single, universally accepted definition is unlikely, as fairness in AI will always require contextual interpretation and adaptive implementation.

I would be happy for you read my articles and recommend them to increase visibility.

Achieving a fundamental and universally-agreed definition of fairness in AI is challenging due to the inherent complexity of fairness itself, which is influenced by cultural, social, and contextual factors. Different stakeholders—such as ethicists, technologists, policymakers, and affected communities—often have varying perspectives on what constitutes fairness, leading to multiple interpretations. For instance, fairness can be defined in terms of equal outcomes, equal opportunities, or the absence of bias, each with its own implications. Additionally, the dynamic nature of societal values means that any consensus on fairness may evolve over time. Therefore, while frameworks for fairness can be developed, a singular, universally-accepted definition remains elusive, necessitating ongoing dialogue and adaptation to diverse contexts.