Research Summary: Equity Bias: An Ethical Framework for AI Design

The 'Equity Bias' framework offers an alternative ethical paradigm for AI development, positing that artificial intelligence operates as an interpretive agent that generates meaning rather than an objective calculator. Grounded in Gadamerian hermeneutics and epistemic injustice theory, the framework argues that completely neutral systems are impossible because all encoded knowledge is situated. Instead of striving to eliminate bias as an error, the model establishes a three-stage lifecycle—Equity Archaeology, Co-Creating Meaning, and Ongoing Accountability—aimed at rendering encoded perspectives transparent and contestable.

Why it matters

This framework challenges the prevailing corporate and technical assumption that algorithmic bias can be fully eliminated through technical optimisation. By demonstrating that neutrality is unattainable, it necessitates a strategic shift toward epistemic transparency and contestability in AI governance. Adopting this approach redefines enterprise risk management from seeking zero-bias models to managing documented, defensible perspectives.

Key insights

• AI systems operate as interpretive agents that construct meaning and knowledge rather than neutral computational engines.
• Because all knowledge is inherently situated, creating completely neutral artificial intelligence systems is fundamentally unachievable.
• Algorithmic bias reflects whose specific knowledge has been encoded into systems, rather than an operational defect to be eradicated.
• The proposed framework pivots from standard bias reduction to making systemic biases transparent and contestable.
• Implementation is operationalised through a three-stage lifecycle consisting of Equity Archaeology, Co-Creating Meaning, and Ongoing Accountability.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2604.21907