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The critical need for explanation by ai systems has led to calls for algorithmic transparency, including the “right to explanation” in the eu general data protection regulation (gdpr). For researchers, this paper provides insights into what organizations consider important in the transparency and, in particular, explainability of ai systems For practitioners, this study suggests a systematic and structured way to define explainability requirements of ai systems. Local transparency refers to understanding how an algorithmic system makes a decision about a single case or instance, and global transparency refers to understanding how the system works overall.
Bibliographic details on good explanation for algorithmic transparency. This has led to calls for (more) algorithmic transparency (at), which refers to the disclosure of information about algorithms to enable understanding, critical review, and adjustment. The critical need for explanation and justification by ai systems has led to calls for algorithmic transparency, including the “right to explanation” in the eu general data protection. We study optimal algorithmic disclosure in a lending market where a lender uses a predictive algorithm to screen a borrower and maximizes profit
Transparency is essential for evaluating the fairness and accountability of ai algorithms.
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