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"Consider an <strong>AI system for approving loan applications</strong>, and which <em>denies</em> an application—this is a situation where <em>explainable AI is important</em> and <strong>explainable AI principles can help</strong>:<br/>1. The applicant would likely wish to understand <em>why they were denied</em> (or have the <em>right to know</em> under GDPR rules)—<strong>evidence</strong> to support this (eg. rating factor impacts) would benefit the explanation<br/>2. An explanation that is <strong>understandable</strong> by the applicant would improve customer experience<br/>3. An explanation that is <strong>accurate</strong> would minimize confusion for the applicant and <em>reduce the risk of misinformation</em> by the financial institution offering the loan<br/>4. An explanation should be within the <strong>limits</strong> of the AI system’s design—for instance, the applicant should fit the demographic, financial or other factors that the AI system was designed for, else the system’s <em>output</em> and any <em>explanations</em> could be misleading"
<https://highdemandskills.com/xai-principles/>