Mastering AI Product Management/

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Explainability and Accuracy

Explainability and Accuracy

Understand the importance of ethics and responsibility in AI product management, noting the complexity of DL algorithms that often lack explainability.

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Optimizing for ethics, caveats, and responsibility

Ethics and responsibility play a foundational role in dealing with our customers’ data and behavior, and because most of us will build products that help assist humans in making decisions, eventually, someone is going to ask us how our product arrives at conclusions. Critical thinking is one of the foundational cornerstones of human reasoning, and if our product is rooted in DL, our answer won’t be able to truly satisfy anyone’s skepticism. Our heartfelt advice is this: don’t create a product that will harm people, get sued, or pose a risk to our business.

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Bias in AI/ML

If we’re leveraging ML or DL in a capacity that has even the potential to cause harmE.g., wrongful arrest or misdiagnosis. to others, if there’s a clear biasE.g., witholding loans from people of a certain background or search engines showing mostly images of women when looking up “nurse.” that affects underrepresented or minority groups in terms of race, gender, or culture, go back to the ideation phase. This is true whether that’s immediate or downstream harm. This is a general risk all of ML poses to us collectively: the notion that we’re coding our societal biases into AI without taking the necessary precautions to make sure the data we feed our algorithms is truly unbiased.

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Data privacy and biasing in AI
Data privacy and biasing in AI

Explainability

The ...