Positives The paper is well-written and includes a through literature review. The following paper is also very relevant to the submission: Shrivastava, Ashish, et al. "Learning from simulated and unsupervised images through adversarial training." Novelty of the method over [44] is not major. Still, I believe no one has shown that computing flow on simulated data and using it for training improves over RGB only (although the improvement is quite marginal). Simulation pipeline proposed in the paper seems to be quite useful.

Fairness and robustness are often considered as orthogonal dimensions when evaluating machine learning models. However, recent work has revealed interactions between fairness and robustness, showing that fairness properties are not necessarily maintained under distribution shift. In healthcare settings, this can result in e.g. a model that performs fairly according to a selected metric in "hospital A" showing unfairness when deployed in "hospital B". While a nascent field has emerged to develop provable fair and robust models, it typically relies on strong assumptions about the shift, limiting its impact for real-world applications. In this work, we explore the settings in which recently proposed mitigation strategies are applicable by referring to a causal framing. Using examples of predictive models in dermatology and electronic health records, we show that real-world applications are complex and often invalidate the assumptions of such methods. Our work hence highlights technical, practical, and engineering gaps that prevent the development of robustly fair machine learning models for real-world applications. Finally, we discuss potential remedies at each step of the machine learning pipeline.

How should we be thinking about machine learning and AI? originally appeared on Quora - the knowledge sharing network where compelling questions are answered by people with unique insights. The big issue that I am interested in is: what does it mean for us to live with machines that are not sentient but appear as if they are? Let's start by quickly defining sentience and intelligence (at least for this discussion). We'll say that intelligence is the ability solve complex problems, handle varied input and achieve goals. An abacus is not intelligent.

The big issue that I am interested in is: what does it mean for us to live with machines that are not sentient but appear as if they are? Let's start by quickly defining sentience and intelligence (at least for this discussion). We'll say that intelligence is the ability solve complex problems, handle varied input and achieve goals. An abacus is not intelligent. Sentience is the ability to feel, to have first person experience.

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