Background: With the discussion of fairness in Machine Learning (ML) gaining traction in recent years, the idea of viewing fairness through the causal lens has become prominent. The main idea behind this is that by looking at the causal structure underlying the data used for an ML model, we can see and evaluate more concisely which influences of the sensitive variables on the target variable are problematic and how they are problematic. Doing so allows not only a nuanced view of fairness and an informed choice of fairness measures but also more targeted approaches (such as path-specific bias mitigation) to handle fairness issues. Objectives: Mainly, two important points have hindered the practical use of the causal lens and causality-based bias mitigation. First, a classification of different graphical structures with different fairness implications involving a sensitive variable and a target variable is still missing, as is a discussion of how different contexts can shape our evaluation of fairness. Second, the construction of such graphical models is not trivial and error-prone. However, recent work showed that combining background knowledge and data-driven network structure learning may lead to more accurate graphs. In this work, we attempt to address and tackle these two practical shortcomings. Methods: Our first contribution is a classification and discussion of causal structures with different fairness implications and how contexts shape our assessment. Our second contribution is an advancement in learning more accurate graphs by adapting structure learning algorithms, and a detailed evaluation of graph correctness and subsequent fairness implications. Results: We show that when including background knowledge naturally available in fairness settings, graph learning becomes more accurate, which also has positive implications for accurate fairness assessments. Conclusions: Our work may pave the way for a broader adoption of causal ML fairness by providing concrete suggestions about the implications of causal structures and contexts, and learning more accurate graphs. We also address current limitations and highlight the need for stakeholder inclusion.

We present a new approach to goal recognition that involves comparing observed facts with their expected probabilities. These probabilities depend on a specified goal g and initial state s0. Our method maps these probabilities and observed facts into a real vector space to compute heuristic values for potential goals. These heuristic values estimate the likelihood of a given goal being the true objective of the observed agent. As obtaining exact expected probabilities for observed facts in an observation sequence is often practically infeasible, we propose and empirically validate a method for approximating these probabilities. Our empirical results show that the proposed approach offers improved goal recognition precision compared to state-of-the-art techniques while reducing computational complexity.

Recently, discussions on fairness and algorithmic bias have gained prominence in the learning analytics and educational data mining communities. To quantify algorithmic bias, researchers and practitioners often use popular fairness metrics, e.g., demographic parity, without discussing their choices. This can be considered problematic, as the choices should strongly depend on the underlying data generation mechanism, the potential application, and normative beliefs. Likewise, whether and how one should deal with the indicated bias depends on these aspects. This paper presents and discusses several theoretical cases to highlight precisely this. By providing a set of examples, we hope to facilitate a practice where researchers discuss potential fairness concerns by default.