Addressing racial bias in artificial intelligence (AI) is crucial to avoid discriminatory outcomes that disproportionately affect people of color. Diverse and representative data, bias detection and mitigation, ethical design, transparency, community engagement, evaluation, and policy interventions are essential in fostering fairness and equity in AI systems.
- Racial bias in artificial intelligence refers to discriminatory outcomes that disproportionately impact people of color, perpetuating inequalities and reinforcing stereotypes.
- To tackle racial bias in AI, diverse and representative data, bias detection and mitigation techniques, ethical and inclusive design practices, transparency, and community engagement are essential.
- By incorporating these measures, AI systems can strive towards fairness, accountability, and transparency, promoting equal opportunities and avoiding discriminatory outcomes for individuals from marginalized racial or ethnic backgrounds.