Hybrid machine/human ML

September 13, 2021 — September 23, 2023

computers are awful
statistics
UI
unsupervised

Placeholder to discuss the details of designing algorithms that learn to augment humans.

Additional distinction: will they complement or compete with our existing faculties?

1 Human-in-the-loop learning

Figure 1: Human in the loop

There is probably a lot more going on in the world of human-in-the-loop learning than I am aware of. For now see adaptive design of experiments.

2 Our robot regency

How long will it be worthwhile augmenting humans before it is more efficient to replace them? See the robot regency.

3 Incoming

4 References

Agarwal, D’souza, and Hooker. 2021. Estimating Example Difficulty Using Variance of Gradients.” arXiv:2008.11600 [Cs].
Carter, and Nielsen. 2017. Using Artificial Intelligence to Augment Human Intelligence.” Distill.
Charusaie, Mozannar, Sontag, et al. 2022. Sample Efficient Learning of Predictors That Complement Humans.” In Proceedings of the 39th International Conference on Machine Learning.
Danaher. 2018. Toward an Ethics of AI Assistants: An Initial Framework.” Philosophy & Technology.
Donahue, Kollias, and Gollapudi. 2023. When Are Two Lists Better Than One?: Benefits and Harms in Joint Decision-Making.”
Fügener, Grahl, Gupta, et al. 2021. Will Humans-in-the-Loop Become Borgs? Merits and Pitfalls of Working with AI.” MIS Quarterly.
Hilgard, Rosenfeld, Banaji, et al. 2020. Learning Representations by Humans, for Humans.” arXiv:1905.12686 [Cs, Stat].
Hohenstein, Kizilcec, DiFranzo, et al. 2023. Artificial Intelligence in Communication Impacts Language and Social Relationships.” Scientific Reports.
Lee. 2020. The Coevolution: The Entwined Futures of Humans and Machines.
Meyer, Khademi, Têtu, et al. 2022. Impact of Artificial Intelligence on Pathologists’ Decisions: An Experiment.” Journal of the American Medical Informatics Association.