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Title: Predict Responsibly: Increasing Fairness by Learning to Defer

Paper content:
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.
Recent machine learning advances have increased our reliance on learned automated systems in complex, high-stakes domains such as loan approvals BID6 , medical diagnosis BID12 , and criminal justice BID22 .
This growing use of automated decisionmaking has raised questions about the obligations of classification systems.
In many high-stakes situations, machine learning systems should satisfy (at least) three objectives: predict accurately (predictions should be broadly effective indicators of ground truth), predict fairly (predictions should be unbiased with respect to different types of input), and predict responsibly (predictions should not be made if the model cannot confidently satisfy the previous two objectives).Given
these requirements, we propose learning to defer. When
deferring, the algorithm does not output a prediction; rather it says "I Don't Know" (IDK), indicating it has insufficient information to make a responsible prediction, and that a more qualified external decision-maker (DM) is required. For
example, in medical diagnosis, the deferred cases would lead to more medical tests, and a second expert opinion. Learning
to defer extends the common rejection learning framework (Chow, 1957; BID9 in two ways. Firstly,
it considers the expected output of the DM on each example, more accurately optimizing the output of the joint DM-model system. Furthermore
, it can be used with a variety of training objectives, whereas most rejection learning research focuses solely on classification accuracy. We believe
that algorithms that can defer, i.e., yield to more informed decision-makers when they cannot predict responsibly, are an essential component to accountable and reliable automated systems.In this work, we show that the standard rejection learning paradigm (learning to punt) is inadequate, if these models are intended to work as part of a larger system. We propose
an alternative decision making framework (learning to defer) to learn and evaluate these models. We find that
embedding a deferring model in a pipeline can improve the accuracy and fairness of the pipeline as a whole, particularly if the model has insight into decision makers later in the pipeline. We simulate
such a pipeline where our model can defer judgment to a better-informed decision maker, echoing real-world situations where downstream decision makers have more resources or information. We propose
different formulations of these models along with a learning algorithm for training a model that can work optimally with such a decision-maker. Our experimental
results on two real-world datasets, from the legal and health domains, show that this algorithm learns models which, through deferring, can work with users to make fairer, more responsible decisions.
In this work, we propose the idea of learning to defer.
We propose a model which learns to defer fairly, and show that these models can better navigate the accuracy-fairness tradeoff.
We also consider deferring models as one part of a decision pipeline.
To this end, we provide a framework for evaluating deferring models by incorporating other decision makers' output into learning.
We give an algorithm for learning to defer in the context of a larger system, and show how to train a deferring model to optimize the performance of the pipeline as a whole.
This is a powerful, general framework, with ramifications for many complex domains where automated models interact with other decision-making agents.
A model with a low deferral rate could be used to cull a large pool of examples, with all deferrals requiring further examination.
Conversely, a model with a high deferral rate can be thought of as flagging the most troublesome, incorrect, or biased decisions by a DM, with all non-deferrals requiring further investigation.
Automated models often operate within larger systems, with many moving parts.
Through deferring, we show how models can learn to predict responsibly within their surrounding systems.
Automated models often operate within larger systems, with many moving parts.
Through deferring, we show how models can learn to predict responsibly within their surrounding systems.
Building models which can defer to more capable decision makers is an essential step towards fairer, more responsible machine learning.

assistant: text: Incorporating the ability to say I-don't-know can improve the fairness of a classifier without sacrificing too much accuracy, and this improvement magnifies when the classifier has insight into downstream decision-making.