As algorithms are increasingly used to make decisions of social consequence, the social values encoded in these decision-making procedures are the subject of increasing study, with fairness being a chief concern. The Conference on Fairness, Accountability, and Transparency (FAT) scheduled on Feb 23 and 24 this year in New York is an annual conference dedicated to bringing theory and practice of fair and interpretable Machine Learning, Information Retrieval, NLP, Computer Vision, Recommender systems, and other technical disciplines. This year’s program includes 17 peer-reviewed papers and 6 tutorials from leading experts in the field. The conference will have three sessions. Session 4 of the two-day conference on Saturday, February 24, is in the field of fair classification. In this article, we give our readers a peek into the four papers that have been selected for presentation in Session 4.
What is the paper about?
This paper provides a simple approach to the Fairness-aware problem which involves suitably thresholding class-probability estimates. It has been awarded Best paper in Technical contribution category.
The authors have studied the inherent tradeoffs in learning classifiers with a fairness constraint in the form of two questions:
- What is the best accuracy we can expect for a given level of fairness?
- What is the nature of these optimal fairness aware classifiers?
The authors showed that for cost-sensitive approximate fairness measures, the optimal classifier is an instance-dependent thresholding of the class probability function. They have quantified the degradation in performance by a measure of alignment of the target and sensitive variable. This analysis is then used to derive a simple plugin approach for the fairness problem.
For Fairness-aware learning, the authors have designed an algorithm targeting a particular measure of fairness.
- They have reduced two popular fairness measures (disparate impact and mean difference) to cost-sensitive risks.
- They show that for cost-sensitive fairness measures, the optimal Fairness-aware classifier is an instance-dependent thresholding of the class-probability function.
- They quantify the intrinsic, method independent impact of the fairness requirement on accuracy via a notion of alignment between the target and sensitive feature.
The ability to theoretically compute the tradeoffs between fairness and utility is perhaps the most interesting aspect of their technical results.
They have stressed that the tradeoff is intrinsic to the underlying data. That is, any fairness or unfairness, is a property of the data, not of any particular technique.
They have theoretically computed what price one has to pay (in utility) in order to achieve a desired degree of fairness: in other words, they have computed the cost of fairness.
What is the paper about?
This paper considers how to use a sensitive attribute such as gender or race to maximize fairness and accuracy, assuming that it is legal and ethical. Simple linear classifiers may use the raw data, upweight/oversample data from minority groups, or employ advanced approaches to fitting linear classifiers that aim to be accurate and fair. However, an inherent tradeoff between accuracy on one group and accuracy on another still prevails. This paper defines and explores decoupled classification systems, in which a separate classifier is trained on each group. The authors present experiments on 47 datasets. The experiments are “semi-synthetic” in the sense that the first binary feature was used as a substitute sensitive feature. The authors found that on many data sets the decoupling algorithm improves performance while less often decreasing performance.
- The paper describes a simple technical approach for a practitioner using ML to incorporate sensitive attributes.
- This approach avoids unnecessary accuracy tradeoffs between groups and can accommodate an application-specific objective, generalizing the standard ML notion of loss.
- For a certain family of “weakly monotonic” fairness objectives, the authors provide a black-box reduction that can use any off-the-shelf classifier to efficiently optimize the objective.
- This work requires the application designer to pin down a specific loss function that trades off accuracy for fairness.
- Experiments demonstrate that decoupling can reduce the loss on some datasets for some potentially sensitive features
A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions
What is the paper about?
The work is based on the use of predictive analytics in the area of child welfare. It won the best paper award in the Technical and Interdisciplinary Contribution. The authors have worked on developing, validating, fairness auditing, and deploying a risk prediction model in Allegheny County, PA, USA.
- The authors have described competing models that are being developed in the Allegheny County as part of an ongoing redesign process in comparison to the previous models.
- Next, they investigate the predictive bias properties of the current tool and a Random forest model that has emerged as one of the best performing competing models. Their predictive bias assessment is motivated both by considerations of human bias and recent work on fairness criteria.
- They then discuss some of the challenges in incorporating algorithms into human decision-making processes and reflect on the predictive bias analysis in the context of how the model is actually being used.
- They also propose an “oracle test” as a tool for clarifying whether particular concerns pertain to the statistical properties of a model or if these concerns are targeted at other potential deficiencies.
The goal in Allegheny County is to improve both the accuracy and equity of screening decisions by taking a Fairness-aware approach to incorporating prediction models into the decision-making pipeline.
- The paper reports on the lessons learned so far by the authors, their approaches to predictive bias assessment, and several outstanding challenges in the child maltreatment hotline context.
- This report contributes to the ongoing conversation concerning the use of algorithms in supporting critical decisions in government—and the importance of considering fairness and discrimination in data-driven decision making.
- The paper discussion and general analytic approach are also broadly applicable to other domains where predictive risk modeling may be used.
What is the paper about?
Plenty of moral and political philosophers have expended significant efforts in formalizing and defending the central concepts of discrimination, egalitarianism, and justice. Thus it is unsurprising to know that the attempts to formalize ‘fairness’ in machine learning contain echoes of these old philosophical debates. This paper draws on existing work in moral and political philosophy in order to elucidate emerging debates about fair machine learning. It answers the following questions:
- What does it mean for a machine learning model to be ‘fair’, in terms which can be operationalized?
- Should fairness consist of ensuring everyone has an equal probability of obtaining some benefit, or should we aim instead to minimize the harms to the least advantaged?
- Can the relevant ideal be determined by reference to some alternative state of affairs in which a particular social pattern of discrimination does not exist?
- This paper aims to provide an overview of some of the relevant philosophical literature on discrimination, fairness, and egalitarianism in order to clarify and situate the emerging debate within fair machine learning literature.
- The author addresses the conceptual distinctions drawn between terms frequently used in the fair ML literature–including ‘discrimination’ and ‘fairness’–and the use of related terms in the philosophical literature.
- He suggests that ‘fairness’ as used in the fair machine learning community is best understood as a placeholder term for a variety of normative egalitarian considerations.
- He also provides an overview of implications for the incorporation of ‘fairness’ into algorithmic decision-making systems.
We hope you like the coverage of Session 4. Don’t miss our coverage on Session 5 on Fat recommenders and more.