Balancing Performance and Explainability in Automated Decision-Making Systems
Automated decision-making (ADM) systems, while improving efficiency and accuracy, raise concerns about fairness and explainability. The GDPR and AI Act aim to safeguard rights in automated decisions, but the balance between performance and explainability remains a challenge.
ADM systems, designed to enhance human decision-making, can lead to significant impacts on individuals. The GDPR and AI Act provide rights to consent, information, intervention, and explanation for such decisions. However, complex models, often more accurate, can be less interpretable, creating a trade-off between explainability and performance.
Post-hoc methods like Shapley Values and LIME attempt to explain model reasoning but may fall short. Recent cases, such as welfare fraud detection and racial bias in AI hiring, highlight the need for robust explainability. In response, Explainable AI (XAI) focuses on making AI outputs understandable, using intrinsic and post-hoc methods. Yet, debate persists among legal experts about the suitability of explanations for protecting fairness.
ADMs should not be deployed in domains where human agency is crucial, as predictions can become self-fulfilling prophecies. Liberal democracies subject consequential decisions to standardized procedures and appeals processes to mitigate human failings.
Striking a balance between performance and explainability in ADM systems is crucial. While the GDPR and AI Act provide rights to protect individuals, the interpretation and effectiveness of explanations remain debated. Further research and policy clarification are needed to ensure fairness and accountability in automated decision-making.
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