Part 5/14:
Remediation: Identifying problems is insufficient without prescribed solutions. For example, if an AI system forecasts an unfavorable outcome, it should suggest actionable steps—this is achieved through techniques like counterfactual inference.
Fairness and Bias Mitigation: Addressing data biases prevents discriminatory outcomes, fostering equitable decision-making.
Collectively, these elements form a "halo" that supports the core statistical model, elevating AI from mere prediction engines to responsible decision-making partners.