Equity Bias: An Ethical Framework for AI Design

arXiv:2604.21907v2 Announce Type: replace-cross Abstract: Equity Bias is a philosophical and practical framework for building more equitable AI systems. It treats AI as an interpretive agent that constructs meaning and knowledge rather than as a neutral calculator. Grounded in Gadamerian hermeneutics and epistemic injustice theory, Equity Bias views bias not as an error to eliminate but as a reflection of whose knowledge is encoded into systems. Since all knowledge is situated, neutral systems are not possible. While traditional approaches aim to reduce or remove bias, Equity Bias instead makes bias transparent and contestable. Its three-stage AI Life Cycle (Equity Archaeology, Co-Creating Meaning, and Ongoing Accountability) turns these principles into practice. Equity Bias gives developers, researchers, policymakers, and communities affected by AI a method for examining whose knowledge a system encodes and who has the power to contest it. By placing the politics of knowledge at the centre of equitable AI design, Equity Bias offers a distinct approach to AI ethics.