Bias and unfairness in Machine Learning (ML) are challenging to detect and mitigate, particularly in critical fields such as finance, hiring, and healthcare. While numerous unfairness mitigation techniques exist, most evaluation frameworks assess only a limited set of fairness metrics, primarily focusing on the trade-off between fairness and accuracy. We introduce FAIR-CARE, a new open-source and robust approach that consists of an evaluation pipeline designed for the systematic assessment of unfairness mitigation techniques. Our approach simultaneously evaluates multiple fairness and performance metrics across various ML models. We conduct a comparative analysis on healthcare datasets with diverse distributions—including target class, protected attribute, and their joint distributions—to identify the most effective mitigation technique for each processing type (pre-, in-, and post-processing). Furthermore, we determine the best-performing techniques across different datasets, fairness metrics, performance metrics, and ML models. Finally, we provide practical insights into the application of these techniques, offering actionable guidance for both researchers and practitioners. • Fairness and accuracy can coexist in machine learning models, even on unbalanced datasets. • Mitigation technique performance varies by dataset type and processing stage. • FAIR-CARE identifies top-performing mitigation techniques for each processing type and highlights trade-offs where applicable.

In many recommendation applications, items may have limited availability thereby causing conflict among users interested in the same items. Over time, this results in unequal user treatment: few users are recommended the limited items and receive preferential treatment, while the rest is left with sub-optimal recommendations, ultimately leading them to leave. In this paper, we formalize the novel problem of compensating users in multi-session recommendations under limited item availability. Our aim is to generate recommendations that not only optimize accuracy, but also compensate users over time for the loss of accuracy incurred in previous iterations. We design compensation strategies that serve users and items in different orders and accommodate various recommendation adoption models. Our algorithms are integrated into SoCRATe (System for Compensating Recommendations with Availability and Time), a framework that enables us to study loss compensation over time. Our experiments on real data demonstrate that to best compensate users for the incurred loss, traditional recommenders need to be revisited to account for item availability. Our experiments on synthetic data explore different parameters of our solution and show that it is much faster than an optimal (brute-force) compensation strategy, while achieving comparable results.

Today's technologies are becoming more and more pervasive and advanced software systems can replace human beings in many different tasks. This is especially true in the case of automated decision-making systems based on machine learning (ML). Important ethical implications arise when such decision systems are used in sensitive contexts (e.g., justice or loans). The elicitation of these implications, that is, of the ethical requirements behind ML-based systems is a new challenge we must address to avoid societal risks. This is particularly urgent for fairness since this notion lacks a precise and commonly accepted definition, thus hampering its assessment. This paper aims to give a comprehensive definition of fairness, present a unified taxonomy of alternative interpretations, define a new decision tree that can guide the choice of the correct interpretation, and carry out a preliminary assessment with experiments in a real-world context.

We demonstrate SoCRATe, an online system dedicated to providing adaptive recommendations to users when items have limited availability. SoCRATe is relevant to several real-world applications, among which movie and task recommendations. SoCRATe has several appealing features: (i) watching users as they consume recommendations and accounting for user feedback in refining recommendations in the next round; (ii) implementing loss compensation strategies to make up for sub-optimal recommendations, in terms of accuracy, when items have limited availability; (iii) deciding when to re-generate recommendations on a need-based fashion. SoCRATe accommodates real users as well as simulated users to enable testing multiple recommendation choice models. To frame evaluation, SoCRATe introduces a new set of measures that capture recommendation accuracy, user satisfaction and item consumption over time. All these features make SoCRATe unique and able to adapt recommendations to user preferences in a resource-limited setting. A video of SoCRATe is available at https://youtu.be/4wlaScc_rUo.

In our everyday lives, technologies based on data play an increasingly important role. With the widespread adoption of decision making systems also in very sensitive environments, fairness has become a very important topic of discussion within the data science community. In this context, it is crucial to ensure that the data on which we base these decisions, are fair, and do not reflect historical biases. In this demo, we propose FAIR-DB (FunctionAl dependencIes to discoveR Data Bias), a system that exploiting the notion of Functional Dependency, a particular type of constraint on the data, can discover unethical behaviours in a dataset. The proposed solution is implemented as a web-based application, that, given an input dataset, generates such dependencies, walks the user trough their analysis, and finally provides many insights about bias present in the data. Our tool uses a novel metric to evaluate the unfairness present in datasets, identifies the attributes that encompass discrimination (e.g. ethnicity, sex or religion), and provides very precise information about the groups treated unequally. We also provide a detailed description of the system architecture and present a demonstration scenario, based on a real-world dataset frequently used in the field of computer ethics.