Research Group Christoph Kern

AI-driven decision-making systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health. While these systems offer great potential benefits to institutional decision-making processes, such as improved efficiency and reliability, these systems face the challenge of aligning machine learning (ML) models with the complex realities of public sector decision-making. In this paper, we examine five key challenges where misalignment can occur, including distribution shifts, label bias, the influence of past decision-making on the data side, as well as competing objectives and human-in-the-loop on the model output side. Our findings suggest that standard ML methods often rely on assumptions that do not fully account for these complexities, potentially leading to unreliable and harmful predictions. To address this, we propose a shift in modeling efforts from focusing solely on predictive accuracy to improving decision-making outcomes. We offer guidance for selecting appropriate modeling frameworks, including counterfactual prediction and policy learning, by considering how the model estimand connects to the decision-maker’s utility. Additionally, we outline technical methods that address specific challenges within each modeling approach. Finally, we argue for the importance of external input from domain experts and stakeholders to ensure that model assumptions and design choices align with real-world policy objectives, taking a step towards harmonizing AI and public sector objectives.

Algorithmic profiling is increasingly used in the public sector with the hope of allocating limited public resources more effectively and objectively. One example is the prediction-based profiling of job seekers to guide the allocation of support measures by public employment services. However, empirical evaluations of potential side-effects such as unintended discrimination and fairness concerns are rare in this context. We systematically compare and evaluate statistical models for predicting job seekers’ risk of becoming long-term unemployed concerning subgroup prediction performance, fairness metrics, and vulnerabilities to data analysis decisions. Focusing on Germany as a use case, we evaluate profiling models under realistic conditions using large-scale administrative data. We show that despite achieving high prediction performance on average, profiling models can be considerably less accurate for vulnerable social subgroups. In this setting, different classification policies can have very different fairness implications. We therefore call for rigorous auditing processes before such models are put to practice.

National Statistical Organizations (NSOs) increasingly draw on Machine Learning (ML) to improve the timeliness and cost-effectiveness of their products. When introducing ML solutions, NSOs must ensure that high standards with respect to robustness, reproducibility, and accuracy are upheld as codified, e.g., in the Quality Framework for Statistical Algorithms (QF4SA; Yung et al. 2022, Statistical Journal of the IAOS). At the same time, a growing body of research focuses on fairness as a pre-condition of a safe deployment of ML to prevent disparate social impacts in practice. However, fairness has not yet been explicitly discussed as a quality aspect in the context of the application of ML at NSOs. We employ the QF4SA quality framework and present a mapping of its quality dimensions to algorithmic fairness. We thereby extend the QF4SA framework in several ways: First, we investigate the interaction of fairness with each of these quality dimensions. Second, we argue for fairness as its own, additional quality dimension, beyond what is contained in the QF4SA so far. Third, we emphasize and explicitly address data, both on its own and its interaction with applied methodology. In parallel with empirical illustrations, we show how our mapping can contribute to methodology in the domains of official statistics, algorithmic fairness, and trustworthy machine learning.

Estimating heterogeneous treatment effects is important to tailor treatments to those individuals who would most likely benefit. However, conditional average treatment effect predictors may often be trained on one population but possibly deployed on different, possibly unknown populations. We use methodology for learning multi-accurate predictors to post-process CATE T-learners (differenced regressions) to become robust to unknown covariate shifts at the time of deployment. The method works in general for pseudo-outcome regression, such as the DR-learner. We show how this approach can combine (large) confounded observational and (smaller) randomized datasets by learning a confounded predictor from the observational dataset, and auditing for multi-accuracy on the randomized controlled trial. We show improvements in bias and mean squared error in simulations with increasingly larger covariate shift, and on a semi-synthetic case study of a parallel large observational study and smaller randomized controlled experiment. Overall, we establish a connection between methods developed for multi-distribution learning and achieve appealing desiderata (e.g. external validity) in causal inference and machine learning.

Automated decision-making (ADM) systems are being deployed across a diverse range of critical problem areas such as social welfare and healthcare. Recent work highlights the importance of causal ML models in ADM systems, but implementing them in complex social environments poses significant challenges. Research on how these challenges impact the performance in specific downstream decision-making tasks is limited. Addressing this gap, we make use of a comprehensive real-world dataset of jobseekers to illustrate how the performance of a single CATE model can vary significantly across different decision-making scenarios and highlight the differential influence of challenges such as distribution shifts on predictions and allocations.

We address the challenges and implications of ensuring fairness in algorithmic decision-making (ADM) practices related to ethnicity. Expanding beyond the U.S.-centric approach to race, we provide an overview of ethnic classification schemes in European countries and emphasize how the distinct approaches to ethnicity in Europe can impact fairness assessments in ADM. Drawing on large-scale German survey data, we highlight differences in ethnic disadvantage across subpopulations defined by different measures of ethnicity. We build prediction models in the labor market, health, and finance domain and investigate the fairness implications of different ethnic classification schemes across multiple prediction tasks and fairness metrics. Our results show considerable variation in fairness scores across ethnic classifications, where error disparities for the same model can be twice as large when using different operationalizations of ethnicity. We argue that ethnic classifications differ in their ability to identify ethnic disadvantage across ADM domains and advocate for context-sensitive operationalizations of ethnicity and its transparent reporting in fair machine learning (ML) applications.

Data practices shape research and practice on fairness in machine learning (fair ML). Critical data studies offer important reflections and critiques for the responsible advancement of the field. In this work, we present a comprehensive analysis of fair ML datasets, demonstrating how unreflective yet common practices hinder the reach and reliability of algorithmic fairness findings. We systematically study protected information encoded in tabular datasets and their usage in 280 experiments across 142 publications. Our analyses identify three main areas of concern: (1) a lack of representation for certain protected attributes in both data and evaluations, (2) the widespread exclusion of minorities during data preprocessing, and (3) a lack of transparency about consequential yet overlooked dataset processing choices. We further note additional factors, such as limitations in publicly available data, privacy considerations and a general lack of awareness that further contribute to these issues. Through exemplary analyses on the usage of popular datasets, we demonstrate how opaque data choices significantly impact minorities, fairness metrics, and the resulting model comparison. To address these challenges, we propose a set of recommendations for data usage in fairness research centered on transparency and responsible inclusion. This study underscores the need for a critical reevaluation of data practices in fair ML and offers directions to improve both the sourcing and usage of datasets.

A vast number of systems across the world use algorithmic decision making (ADM) to (partially) automate decisions that have previously been made by humans. The downstream effects of ADM systems critically depend on the decisions made during a systems’ design, implementation, and evaluation, as biases in data can be mitigated or reinforced along the modeling pipeline. Many of these decisions are made implicitly, without knowing exactly how they will influence the final system. To study this issue, we draw on insights from the field of psychology and introduce the method of multiverse analysis for algorithmic fairness. In our proposed method, we turn implicit decisions during design and evaluation into explicit ones and demonstrate their fairness implications. By combining decisions, we create a grid of all possible “universes” of decision combinations. For each of these universes, we compute metrics of fairness and performance. Using the resulting dataset, one can investigate the variability and robustness of fairness scores and see how and which decisions impact fairness. We demonstrate how multiverse analyses can be used to better understand fairness implications of design and evaluation decisions using an exemplary case study of predicting public health care coverage for vulnerable populations. Our results highlight how decisions regarding the evaluation of a system can lead to vastly different fairness metrics for the same model. This is problematic, as a nefarious actor could optimise or “hack” a fairness metric to portray a discriminating model as fair merely by changing how it is evaluated. We illustrate how a multiverse analysis can help to address this issue.

Given the increasing usage of automated prediction systems in the context of high-stakes de- cisions, a growing body of research focuses on methods for detecting and mitigating biases in algorithmic decision-making. One important framework to audit for and mitigate biases in predictions is that of Multi-Calibration, introduced by Hebert-Johnson et al. (2018). The underlying fairness notion, Multi-Calibration, promotes the idea of multi-group fairness and requires calibrated predictions not only for marginal populations, but also for subpopulations that may be defined by complex intersections of many attributes. A simpler variant of Multi- Calibration, referred to as Multi-Accuracy, requires unbiased predictions for large collections of subpopulations. Hebert-Johnson et al. (2018) proposed a boosting-style algorithm for learning multi-calibrated predictors. Kim et al. (2019) demonstrated how to turn this al- gorithm into a post-processing strategy to achieve multi-accuracy, demonstrating empirical effectiveness across various domains. This package provides a stable implementation of the multi-calibration algorithm, called MCBoost. In contrast to other Fair ML approaches, MC- Boost does not harm the overall utility of a prediction model, but rather aims at improving calibration and accuracy for large sets of subpopulations post-training. MCBoost comes with strong theoretical guarantees, which have been explored formally in Hebert-Johnson et al. (2018), Kim et al. (2019), Dwork et al. (2019), Dwork et al. (2020) and Kim et al. (2021).