Paper:
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-makers later 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.

Review:
Strengths: 
1. This paper proposes a novel framework for ensuring fairness in the classification pipeline. To this end, this work explores models that learn to defer. 
2. The work is conceptually very interesting. The idea of learning to defer (as proposed in the paper) as a means to fairness is not only novel but also quite apt. 
3. Experimental results demonstrate that the proposed learning strategy can not only increase predictive accuracy but also reduce bias in decisions. 
Weaknesses: 
1. While this work is conceptually quite novel and interesting, the technical novelty and contributions seem fairly minimal. 
2. The proposed formulations are essentailly regularized variants of fairly standard classification models and the optimization also relies upon standard search procedures.
3. Experimental analysis on deferring to a biased decision maker (Section 7.3) is rather limited. 
Summary: This paper proposes a novel framework for ensuring fairness in the classification pipeline. More specifically, the paper outlines a strategy called learn to defer which enables the design of predictive models which not only classify accurately and fairly but also defer if necessary. Deferring a decision is used as a mechanism to ensure both fairness and accuracy. Furthermore, the authors consider two variants depending on if the model has some information about the decision maker or not. Experimental results on real world datasets demonstrate the effectiveness of the proposed approach in building an end to end pipeline that ensures accuracy and fairness.  
Novelty: The main novelty of this work stems from the idea of introducing learning to defer mechanisms in the context of fairness. While the ideas of learning to defer have already been studied in the context of classification models, this is the first contribution which leverages learning to defer strategy as a means to achieve fairness. However, beyond this conceptual novelty, the work does not demonstrate a lot of technical novelty or depth. The objective functions proposed are simple extensions of work done by Zafar et. al. (WWW, AISTATS 2017). The optimization procedures being used are also fairly standard. Furthermore, the authors do not carry out any rigorous theoretical analysis either.   
Other detailed comments:
1. I would strongly encourage the authors to carry out a more in-depth theoretical analysis of the proposed framework (Refer to "Provably Fair Representations" McNamara et. al. 2017)
2. Experimental evaluation can also be strengthened. More specifically, analysis in Section 7.3 can be made more thorough. Instead of just sticking to one scenario where the decision maker is extremely biased (how are you quantifying this?), how about plotting a graph where x axis denotes the extent of bias in decision-maker's judgments and y-axis captures the model performance?
3. Overall, the paper is quite well written and is well motivated. There are however some typos and incorrect figure refernces (e.g., Section 7.2 first line, Figure 7.2, there is no such figure).