FreaAI was developed by IBM‘s Haifa Research team to uncover defects in machine learning models by automatically analyzing human-interpretable pieces of data to anticipate when algorithms are functioning and when they aren’t.
The researchers tackled the problem of locating underperforming data slices in order to assess ML systems’ performance. They created automated slicing criteria and implemented them in FreaAI, resulting in valid, statistically significant, and understandable slices.
In some circumstances, having an incorrect answer is acceptable, but it’s critical that they understand and control the scope of a mistake and the circumstances under which it could occur, according to the IBM blog.
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