The O’Reilly Data Show Podcast: Forough Poursabzi Sangdeh on the interdisciplinary nature of interpretable and interactive machine learning.
In this episode of the Data Show, I spoke with Forough Poursabzi-Sangdeh, a postdoctoral researcher at Microsoft Research New York City. Poursabzi works in the interdisciplinary area of interpretable and interactive machine learning. As models and algorithms become more widespread, many important considerations are becoming active research areas: fairness and bias, safety and reliability, security and privacy, and Poursabzi’s area of focus—explainability and interpretability.
We had a great conversation spanning many topics, including:
- Current best practices and state-of-the-art methods used to explain or interpret deep learning—or, more generally, machine learning models.
- The limitations of current model interpretability methods.
- The lack of clear/standard metrics for comparing different approaches used for model interpretability
- Many current AI and machine learning applications augment humans, and, thus, Poursabzi believes it’s important for data scientists to work closely with researchers in other disciplines.
- The importance of using human subjects in model interpretability studies.
[A version of this post appears on the O’Reilly Radar.]
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