Equity Bias: An Ethical Framework for AI Design

TL;DR

The paper proposes the Equity Bias framework, emphasizing transparency and contestability of bias in AI, grounded in hermeneutic philosophy and epistemic injustice, with a three-phase lifecycle methodology.

Contribution

It introduces a novel ethical framework for AI that shifts focus from bias elimination to transparency, incorporating participatory design and continuous accountability.

Findings

Framework broadens perspectives shaping AI systems.

Three-phase methodology guides ethical AI development.

Encourages ongoing evaluation and accountability in AI.

Abstract

Equity Bias is a philosophical and practical framework for building smarter, more equitable AI systems. Grounded in hermeneutic philosophy and epistemic injustice theory, it treats bias not as an error to eliminate but as a reflection of whose knowledge is encoded into systems. While traditional approaches aim to reduce or remove bias, Equity Bias instead makes bias transparent and contestable. In doing so, it broadens whose perspectives shape AI and provides a lens for understanding AI systems as interpretive agents. The framework introduces a three-phase AI Life Cycle methodology: 'Equity Archaeology' (mapping knowledge and assumptions), 'Co-Creating Meaning' (participatory design), and 'Ongoing Accountability' (continuous evaluation). Equity Bias guides developers, researchers, and policymakers towards AI that is ethically accountable and capable of addressing complex real-world challenges.