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4 key tests for your AI explainability toolkit
Enterprise-grade explainability solutions provide fundamental transparency into how machine learning models make decisions, as well as broader assessments of model quality and fairness. Is yours up to the job?
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Until recently, explainability was largely seen as an important but narrowly scoped requirement towards the end of the AI model development process. Now, explainability is being regarded as a multi-layered requirement that provides value throughout the machine learning lifecycle.
Furthermore, in addition to providing fundamental transparency into how machine learning models make decisions, explainability toolkits now also execute broader assessments of machine learning model quality, such as those around robustness, fairness, conceptual soundness, and stability. ....

Shayak Sen , Carnegie Mellon University , Indian Institute Of Technology , Quantitative Input Influence , Indian Institute , ஷயாக் சென் , கார்னகி மெலந் பல்கலைக்கழகம் , இந்தியன் நிறுவனம் ஆஃப் தொழில்நுட்பம் , அளவு உள்ளீடு செல்வாக்கு , இந்தியன் நிறுவனம் ,