The Myth of Complete AI-Fairness

Perfect fairness in AI is impossible, not just difficult — The paper argues that complete fairness is an unattainable goal, similar to how fairness has never been fully achieved in human institutions throughout Western civilization. Rather than pursuing an impossible standard, the focus should shift to establishing practical, measurable fairness metrics.

Fairness requires defined metrics and thresholds, not abstract ideals — The author emphasizes that meaningful progress comes from creating specific, quantifiable standards for fairness rather than relying on vague ethical principles. This allows organizations to measure whether their AI systems meet acceptable fairness benchmarks.

Trust in AI depends on realistic fairness standards — By establishing achievable fairness thresholds, developers can build legitimate trust with users and stakeholders. Pursuing unattainable perfection may actually undermine trust when systems inevitably fall short of impossible ideals.

The ethics discussion needs a practical reframing — Current AI ethics conversations often focus on abstract notions of justice, but the paper suggests this approach is counterproductive. A more pragmatic framework that acknowledges inherent limitations while setting concrete fairness targets would better serve responsible AI development.