Exploring or Exploiting? Social and Ethical Implications of Autonomous Experimentation in AI

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Harms from Increasingly Agentic Algorithmic Systems (2023, background): “Another line of work focusing on the long-term fairness implications of decisions [Bird et al. 2016; D’Amour et al. 2020b; Jabbari et al. 2017; Joseph et al. 2016; Liu et al. 2018; Zhang and Liu 2020].”
A snapshot of the frontiers of fairness in machine learning (2020, background): “This can sometimes be unethical, and a source of unfairness.(6) Several recent papers explore this issue.”
The Unfair Externalities of Exploration (2017, background): “Concerns have been raised about whether exploration in such scenarios could be unfair to some population groups, in the sense that some groups may experience too much of the downside of exploration without suﬃcient upside [2].”
On the Compatibility of Privacy and Fairness (2019, background): “In a limiting sense, when one talks about feature and model selection, there appears to be some tension here: an additional feature might increase the possible privacy loss of an individual, while the additional feature should only make equalized odds easier to satisfy [1].”