Sesgo en la IA
Definición
El sesgo en IA se refiere a errores sistemáticos o resultados injustos (por ej. across demographics) arising from data, model diseño, or deployment. Mitigation includes data audits, fairness metrics, and debiasing methods.
Es a core concern in AI ethics and AI safety. Evaluation metrics for fairness (por ej. demographic parity, equalized odds) are used in audits and before deploying in regulated domains. Explainable AI can help identify when and why bias appears.
Cómo funciona
Bias can enter a través de datos de entrenamiento sesgados (underrepresentation, label bias), proxy variables (por ej. zip code for race), or feedback loops (model outputs influence future data). Detection uses fairness metrics (por ej. demographic parity, equalized odds, calibration by group) on evaluation sets stratified by protected attributes. Mitigation includes: data (reweighting, resampling, collecting more representative data); training (fairness constraints, adversarial debiasing); and post-processing (thresholds or rules per group). Trade-offs exist between fairness metrics and accuracy; legal and domain norms define which metrics and thresholds to use. Audits should be run before deployment and monitored in production.
Casos de uso
Bias work applies when model decisións affect people in regulated or sensitive domains (hiring, lending, scoring, content).
- Auditing hiring, lending, or scoring systems for discriminatory impact
- Fairness checks before deploying models in regulated domains
- Explainability and remediation when bias is detected