This presentation explains how AI systems can suffer from the same biases as human experts, and how that could lead to biased results. It examines how testers, data scientists, and other stakeholders can develop test cases to recognize biases, both in data and the resulting system, and how to address those biases.
1. How data influences how machine learning systems make decisions.
2. How selecting the wrong data, or ambiguous data, can bias machine learning results.
3. Why we don’t have insight into how machine learning systems make decisions.
4. How we can identify and correct bias in machine learning systems.
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