Human-in-the-loop machine learning: a state of the art

Abstract Researchers are defining new types of interactions between humans and machine learning algorithms generically called human-in-the-loop machine learning. In this paper we review the state of the art of the techniques involved in the new forms of relationship between humans and ML algorithms.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 Depending on who is in control of the learning process, we can identify: active learning, in which the system remains in control; interactive machine learning, in which there is a closer interaction between users and learning systems; and machine teaching, where human domain experts have control over the learning process.
• 2 Aside from control, humans can also be involved in the learning process in other ways.
• 3 In curriculum learning human domain experts try to impose some structure on the examples presented to improve the learning; in explainable AI the focus is on the ability of the model to explain to humans why a given solution was chosen.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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