Transparency / Interpretability (ML & AI)

Multicore (+424)

Transparency and interpretability is the ability for the decision processes and inner workings of AI and machine learning systems to be understood by humans or other outside observers.

Present day machine learning systems are typically not very transparent or interpretable. You can use a model's output, but the model can't tell you why it made that output. This makes it hard to determine the cause of biases in ML models.