Tidying Up the Conversational Recommender Systems' Biases

The growing popularity of language models has sparked interest in conversational recommender systems (CRS) within both industry and research circles. However, concerns regarding biases in these systems have emerged. While individual components of CRS have been subject to bias studies, a literature gap remains in understanding specific biases unique to CRS and how these biases may be amplified or reduced when integrated into complex CRS models. In this paper, we provide a concise review of biases in CRS by surveying recent literature. We examine the presence of biases throughout the system's pipeline and consider the challenges that arise from combining multiple models. Our study investigates biases in classic recommender systems and their relevance to CRS. Moreover, we address specific biases in CRS, considering variations with and without natural language understanding capabilities, along with biases related to dialogue systems and language models. Through our findings, we highlight the necessity of adopting a holistic perspective when dealing with biases in complex CRS models

Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns

As machine learning is increasingly used to make real-world decisions, recent research efforts aim to define and ensure fairness in algorithmic decision making. Existing methods often assume a fixed set of observable features to define individuals, but lack a discussion of certain features not being observed at test time. In this paper, we study fairness of naive Bayes classifiers, which allow partial observations. In particular, we introduce the notion of a discrimination pattern, which refers to an individual receiving different classifications depending on whether some sensitive attributes were observed. Then a model is considered fair if it has no such pattern. We propose an algorithm to discover and mine for discrimination patterns in a naive Bayes classifier, and show how to learn maximum likelihood parameters subject to these fairness constraints. Our approach iteratively discovers and eliminates discrimination patterns until a fair model is learned. An empirical evaluation on three real-world datasets demonstrates that we can remove exponentially many discrimination patterns by only adding a small fraction of them as constraints

Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness

Adversarial perturbation is used to expose vulnerabilities in machine learning models, while the concept of individual fairness aims to ensure equitable treatment regardless of sensitive attributes. Despite their initial differences, both concepts rely on metrics to generate similar input data instances. These metrics should be designed to align with the data's characteristics, especially when it is derived from causal structure and should reflect counterfactuals proximity. Previous attempts to define such metrics often lack general assumptions about data or structural causal models. In this research, we introduce a causal fair metric formulated based on causal structures that encompass sensitive attributes. For robustness analysis, the concept of protected causal perturbation is presented. Additionally, we delve into metric learning, proposing a method for metric estimation and deployment in real-world problems. The introduced metric has applications in the fields adversarial training, fair learning, algorithmic recourse, and causal reinforcement learning

A unifying framework for fairness-aware influence maximization

The problem of selecting a subset of nodes with greatest influence in a graph, commonly known as influence maximization, has been well studied over the past decade. This problem has real world applications which can potentially affect lives of individuals. Algorithmic decision making in such domains raises concerns about their societal implications. One of these concerns, which surprisingly has only received limited attention so far, is algorithmic bias and fairness. We propose a flexible framework that extends and unifies the existing works in fairness-aware influence maximization. This framework is based on an integer programming formulation of the influence maximization problem. The fairness requirements are enforced by adding linear constraints or modifying the objective function. Contrary to the previous work which designs specific algorithms for each variant, we develop a formalism which is general enough for specifying different notions of fairness. A problem defined in this formalism can be then solved using efficient mixed integer programming solvers. The experimental evaluation indicates that our framework not only is general but also is competitive with existing algorithms

Counterexample-Guided Learning of Monotonic Neural Networks

The widespread adoption of deep learning is often attributed to its automatic feature construction with minimal inductive bias. However, in many real-world tasks, the learned function is intended to satisfy domain-specific constraints. We focus on monotonicity constraints, which are common and require that the function's output increases with increasing values of specific input features. We develop a counterexample-guided technique to provably enforce monotonicity constraints at prediction time. Additionally, we propose a technique to use monotonicity as an inductive bias for deep learning. It works by iteratively incorporating monotonicity counterexamples in the learning process. Contrary to prior work in monotonic learning, we target general ReLU neural networks and do not further restrict the hypothesis space. We have implemented these techniques in a tool called COMET. Experiments on real-world datasets demonstrate that our approach achieves state-of-the-art results compared to existing monotonic learners, and can improve the model quality compared to those that were trained without taking monotonicity constraints into account