Predict Responsibly: Increasing Fairness by Learning to Defer
When machine learning models are used for high-stakes decisions, they should predict accurately, fairly, and responsibly. To fulfill these three requirements, a model must be able to output a reject option (i.e. say "``I Don*t Know") when it is not qualified to make a prediction. In this work, we propose learning to defer, a method by which a model can defer judgment to a downstream decision-maker such as a human user. We show that learning to defer generalizes the rejection learning framework in two ways: by considering the effect of other agents in the decision-making process, and by allowing for optimization of complex objectives. We propose a learning algorithm which accounts for potential biases held by decision-makerslater in a pipeline. Experiments on real-world datasets demonstrate that learning to defer can make a model not only more accurate but also less biased. Even when operated by highly biased users, we show that deferring models can still greatly improve the fairness of the entire pipeline.
 The proposed method is a classifier that is fair and works in collaboration with an unfair (but presumably accurate model). The novel classifier is the result of the optimisation of a loss function (composed of a part similar to a logistic regression model and a part being the disparate impact). Hence, it can be interpreted as a logistic loss with a fairness regularisation. The results are promising and the applications are very important for the acceptance of ML approaches in the society. However, I believe that the model could be made more general (than a fairness regularized logistic loss) and its theoretical properties studied. Finally, this paper used uncommon vocabulary (for the machine learning community) and it make is difficult to follow sometimes (for example, the use of a Decision-Maker entity). When reading the submitted paper, it was unclear (until section 6) how deferring could help fairness. Hence, the structure of the paper could maybe be improved by introducing the cost function earlier in the manuscript (as a fairness regularised loss). To conclude, although the application is of high interest and the numerical results encouraging, the methodological approach does not seem to be very novel. Minor comment : - The list of authors of the reference “Machine bias : theres software…” apperars incorrectly (some comma may be missing in the .bib file) and there is a small typo in the title. Possible extensions : - The proposed fairness aware loss could be made more general (and not only in the case of a logistic model) - It could also be generalised to a mixture of biased classifier (more than on DM). Edited : As noted by a fellow reviewer, the paper is a bit out of the scope of ICLR and may be more in line with other ML conferences.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Predict Responsibly: Increasing Fairness by Learning to Defer
When machine learning models are used for high-stakes decisions, they should predict accurately, fairly, and responsibly. To fulfill these three requirements, a model must be able to output a reject option (i.e. say "``I Don*t Know") when it is not qualified to make a prediction. In this work, we propose learning to defer, a method by which a model can defer judgment to a downstream decision-maker such as a human user. We show that learning to defer generalizes the rejection learning framework in two ways: by considering the effect of other agents in the decision-making process, and by allowing for optimization of complex objectives. We propose a learning algorithm which accounts for potential biases held by decision-makerslater in a pipeline. Experiments on real-world datasets demonstrate that learning to defer can make a model not only more accurate but also less biased. Even when operated by highly biased users, we show that deferring models can still greatly improve the fairness of the entire pipeline.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The proposed method is a classifier that is fair and works in collaboration with an unfair (but presumably accurate model). The novel classifier is the result of the optimisation of a loss function (composed of a part similar to a logistic regression model and a part being the disparate impact). Hence, it can be interpreted as a logistic loss with a fairness regularisation. The results are promising and the applications are very important for the acceptance of ML approaches in the society. However, I believe that the model could be made more general (than a fairness regularized logistic loss) and its theoretical properties studied. Finally, this paper used uncommon vocabulary (for the machine learning community) and it make is difficult to follow sometimes (for example, the use of a Decision-Maker entity). When reading the submitted paper, it was unclear (until section 6) how deferring could help fairness. Hence, the structure of the paper could maybe be improved by introducing the cost function earlier in the manuscript (as a fairness regularised loss). To conclude, although the application is of high interest and the numerical results encouraging, the methodological approach does not seem to be very novel. Minor comment : - The list of authors of the reference “Machine bias : theres software…” apperars incorrectly (some comma may be missing in the .bib file) and there is a small typo in the title. Possible extensions : - The proposed fairness aware loss could be made more general (and not only in the case of a logistic model) - It could also be generalised to a mixture of biased classifier (more than on DM). Edited : As noted by a fellow reviewer, the paper is a bit out of the scope of ICLR and may be more in line with other ML conferences.
assistant
 The proposed method is a classifier that is fair and works in collaboration with an unfair (but presumably accurate model). The novel classifier is the result of the optimisation of a loss function (composed of a part similar to a logistic regression model and a part being the disparate impact). Hence, it can be interpreted as a logistic loss with a fairness regularisation. The results are promising and the applications are very important for the acceptance of ML approaches in the society. However, I believe that the model could be made more general (than a fairness regularized logistic loss) and its theoretical properties studied. Finally, this paper used uncommon vocabulary (for the machine learning community) and it make is difficult to follow sometimes (for example, the use of a Decision-Maker entity). When reading the submitted paper, it was unclear (until section 6) how deferring could help fairness. Hence, the structure of the paper could maybe be improved by introducing the cost function earlier in the manuscript (as a fairness regularised loss). To conclude, although the application is of high interest and the numerical results encouraging, the methodological approach does not seem to be very novel. Minor comment : - The list of authors of the reference “Machine bias : theres software…” apperars incorrectly (some comma may be missing in the .bib file) and there is a small typo in the title. Possible extensions : - The proposed fairness aware loss could be made more general (and not only in the case of a logistic model) - It could also be generalised to a mixture of biased classifier (more than on DM). Edited : As noted by a fellow reviewer, the paper is a bit out of the scope of ICLR and may be more in line with other ML conferences.