How can explainable AI contribute to mitigating biases in fraud detection systems according to the chapter?

Explainable AI can help mitigate biases in fraud detection systems by making the decision-making process transparent and understandable. This transparency allows for auditing and human oversight, which can identify and correct biased outcomes.

Explainable AI contributes to mitigating biases in fraud detection systems by providing transparency in how decisions are made. This transparency is crucial because it allows stakeholders to audit the AI models and understand the reasoning behind certain decisions, such as wrongful fraud flags. By making the decision-making process clear, explainable AI enables human oversight to review and potentially correct algorithmic decisions that may be biased. This approach helps address issues of discrimination that arise from biased data and flawed algorithms, ultimately contributing to fairer outcomes in fraud detection.