13 research outputs found
Does the End Justify the Means?:On the Moral Justification of Fairness-Aware Machine Learning
Despite an abundance of fairness-aware machine learning (fair-ml) algorithms,
the moral justification of how these algorithms enforce fairness metrics is
largely unexplored. The goal of this paper is to elicit the moral implications
of a fair-ml algorithm. To this end, we first consider the moral justification
of the fairness metrics for which the algorithm optimizes. We present an
extension of previous work to arrive at three propositions that can justify the
fairness metrics. Different from previous work, our extension highlights that
the consequences of predicted outcomes are important for judging fairness. We
draw from the extended framework and empirical ethics to identify moral
implications of the fair-ml algorithm. We focus on the two optimization
strategies inherent to the algorithm: group-specific decision thresholds and
randomized decision thresholds. We argue that the justification of the
algorithm can differ depending on one's assumptions about the (social) context
in which the algorithm is applied - even if the associated fairness metric is
the same. Finally, we sketch paths for future work towards a more complete
evaluation of fair-ml algorithms, beyond their direct optimization objectives
Fairlearn: Assessing and Improving Fairness of AI Systems
Fairlearn is an open source project to help practitioners assess and improve
fairness of artificial intelligence (AI) systems. The associated Python
library, also named fairlearn, supports evaluation of a model's output across
affected populations and includes several algorithms for mitigating fairness
issues. Grounded in the understanding that fairness is a sociotechnical
challenge, the project integrates learning resources that aid practitioners in
considering a system's broader societal context
Can Fairness be Automated?:Guidelines and Opportunities for Fairness-aware AutoML
The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducing barriers for novices. However, decisions derived from ML models can reproduce, amplify, or even introduce unfairness in our societies, causing harm to (groups of) individuals. In response, researchers have started to propose AutoML systems that jointly optimize fairness and predictive performance to mitigate fairness-related harm. However, fairness is a complex and inherently interdisciplinary subject, and solely posing it as an optimization problem can have adverse side effects. With this work, we aim to raise awareness among developers of AutoML systems about such limitations of fairness-aware AutoML, while also calling attention to the potential of AutoML as a tool for fairness research. We present a comprehensive overview of different ways in which fairness-related harm can arise and the ensuing implications for the design of fairness-aware AutoML. We conclude that while fairness cannot be automated, fairness-aware AutoML can play an important role in the toolbox of an ML practitioner. We highlight several open technical challenges for future work in this direction. Additionally, we advocate for the creation of more user-centered assistive systems designed to tackle challenges encountered in fairness work
Look and You Will Find It:Fairness-Aware Data Collection through Active Learning
Machine learning models are often trained on data sets subject to selection bias. In particular, selection bias can be hard to avoid in scenarios where the proportion of positives is low and labeling is expensive, such as fraud detection. However, when selection bias is related to sensitive characteristics such as gender and race, it can result in an unequal distribution of burdens across sensitive groups, where marginalized groups are misrepresented and disproportionately scrutinized. Moreover, when the predictions of existing systems affect the selection of new labels, a feedback loop can occur in which selection bias is amplified over time. In this work, we explore the effectiveness of active learning approaches to mitigate fairnessrelated harm caused by selection bias. Active learning approaches aim to select the most informative instances from unlabeled data. We hypothesize that this characteristic steers data collection towards underexplored areas of the feature space and away from overexplored areas – including areas affectedby selection bias. Our preliminary simulation results confirm the intuition that active learning can mitigate the negative consequences of selection bias, compared to both the baseline scenario and random sampling.<br/
Characterizing Data Scientists' Mental Models of Local Feature Importance
Feature importance is an approach that helps to explain machine learning model predictions. It works through assigning importance scores to input features of a particular model. Different techniques exist to derive these scores, with widely varying underlying assumptions of what importance means. Little research has been done to verify whether these assumptions match the expectations of the target user, which is imperative to ensure that feature importance values are not misinterpreted. In this work, we explore data scientists’ mental models of (local) feature importance and compare these with the conceptual models of the techniques. We first identify several properties of local feature importance techniques that could potentially lead to misinterpretations. Subsequently, we explore the expectations data scientists have about local feature importance through an exploratory (qualitative and quantitative) survey of 34 data scientists in industry. We compare the identified expectations to the theory and assumptions behind the techniques and find that the two are not (always) in agreement