ML classifiers/systems are opaque in how they work i.e. hyperparameters are rarely understandable in the scope of the objective, and thus why these models work is difficult to explain/understand.

University of Michigan - Ann Arbor

Obstacles that machine learning faces : 
1. Adaptability
2. Explainability
3. Understanding Causal Relationships

Three obstacles for Machine Learning

Pearl, Judea. “The Seven Tools of Causal Inference, with Reflections on Machine Learning.” Communications of the ACM, vol. 62, no. 3, 2019, pp. 54–60., doi:10.1145/3241036.

https://dl.acm.org/doi/pdf/10.1145/3241036

The Seven Tools of Causal Inference, with Reﬂections on Machine Learning

ML systems cannot respond to new stimuli that they have not been trained on. i.e. if a ML isn't specifically programmed to learn specific trends in data given to it, it will be unable to learn optimally from the data.

Adaptability

Explainability

Refers to understanding the relationships between different variables, how they affect one another, they effects of changing one variable on the others, asking 'What if?' questions.
Cannot be understood by statistics alone.

Learn Before

Related