## 1 Introduction  
Decisional processes undertaken by humans are at the core of most organizations, from policy setting to IT and IT-security operations. These processes rely on cognitive resources (information, conceptual models, etc.) to help decision-makers in making appropriate decisions leading to meaningful courses of action. A prime example in the security domain is the operation of a security operation center, where technology (e.g., an SIEM--Security Information and Event Management system) supplies information to human operators who have to decide whether a specific event must be investigated [1]. The high complexity and repetitiveness of the information in input to these processes is known to lead to systematic biases in the analyst's decision-making process [2]. How to manage these shortcomings is still an open organizational issue [3], however, the identification of the sources of these biases is a first crucial step in that direction [1]. These issues are not unique to security decisions, and extend to other application domains, such as decisions for hiring or on loans requests, which have been shown to suffer from systematic, oftentimes implicit or unknown biases [4, 5]. The common underlying thread is that any process relying on human judgment must be monitored to uncover unknown and implicit biases and that can only happen by systematically reviewing decisions through objective analyses that 'let the data speak'. Uncovering biases from observational data is a broad and still open problem that requires a thorough exploratory analysis and understanding of the data, as well as rigorous estimations of effect sizes. The literature generally considers association rule mining for the former [6], while regression models are often used to evaluate effect sizes and rigorously evaluate evidence in the data [7]. However, when taken individually, these techniques are affected by intrinsic drawbacks. The outcome of association rule mining typically consists of several thousands of rules that cannot be easily operationalized. While a number of approaches have been proposed to prune the rule set, for instance by removing redundant rules [8] or assessing rule statistical significance [9], there is little or no support for analysts in delving deeper into the obtained outcome. Furthermore, different approaches typically lead to different outcomes, and there are no clear guidelines on how to determine the method and related parameters that best suit the data at hand, which require a deep understanding of the underlying statistical principles by the user. On the other hand, regression models are of little use without clearly defined hypotheses and a clear understanding of the data generation process. In this work, we propose a novel methodology for uncovering systematic biases in data generating processes that combines the benefits of both worlds by leveraging principles from both association rule mining and regression analysis. We use association rule mining to extract the candidate hypotheses of biases from an exploration of data. These hypotheses are used to build regression models which provide us with statistical evidence for the presence/absence of biases in the decisional process, and effectively act as a cream-skimming mechanism to filter out hypotheses that are equivalent or that do not add significant information to uncover the data generation mechanism. This evidence can then be used by an analyst to take action and tackle the decision bias at the source. We demonstrate our methodology in the context of discrimination detection to uncover the systematic use of sensitive data. In particular, we study the ability of the methodology to determine whether decisional processes are affected by biases that lead to unfair treatment because of personal characteristics or membership to certain (protected) societal groups. Nonetheless, our methodology is general and can be applied to the analysis of other decisional processes, e.g., for the security analysis of network traffic to uncover patterns of compromise.