Research Papers

Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces

Ahmad-Reza Ehyaei, Kiarash Mohammadi, Amir-Hossein Karimi +2

As responsible AI gains importance in machine learning algorithms, properties such as fairness, adversarial robustness, and causality have received considerable attention in recent years. However, despite their individual significance, there remains a critical gap in simultaneously exploring and integrating these properties. In this paper, we propose a novel approach that examines the relationship between individual fairness, adversarial robustness, and structural causal models in heterogeneous data spaces, particularly when dealing with discrete sensitive attributes. We use causal structural models and sensitive attributes to create a fair metric and apply it to measure semantic similarity among individuals. By introducing a novel causal adversarial perturbation and applying adversarial training, we create a new regularizer that combines individual fairness, causality, and robustness in the classifier. Our method is evaluated on both real-world and synthetic datasets, demonstrating its effectiveness in achieving an accurate classifier that simultaneously exhibits fairness, adversarial robustness, and causal awareness.

In XAI it is important to consider that, in contrast to explanations for professional audiences, one cannot assume common expertise when explaining for laypeople. But such explanations between humans vary greatly, making it difficult to research commonalities across explanations. We used the dual nature theory, a techno-philosophical approach, to cope with these challenges. According to it, one can explain, for example, an XAI's decision by addressing its dual nature: by focusing on the Architecture (e.g., the logic of its algorithms) or the Relevance (e.g., the severity of a decision, the implications of a recommendation). We investigated 20 game explanations using the theory as an analytical framework. We elaborate how we used the theory to quickly structure and compare explanations of technological artifacts. We supplemented results from analyzing the explanation contents with results from a video recall to explore how explainers justified their explanation. We found that explainers were focusing on the physical aspects of the game first (Architecture) and only later on aspects of the Relevance. Reasoning in the video recalls indicated that EX regarded the focus on the Architecture as important for structuring the explanation initially by explaining the basic components before focusing on more complex, intangible aspects. Shifting between addressing the two sides was justified by explanation goals, emerging misunderstandings, and the knowledge needs of the explainee. We discovered several commonalities that inspire future research questions which, if further generalizable, provide first ideas for the construction of synthetic explanations.

Explainable Artificial Intelligence (XAI) has received widespread interest in recent years, and two of the most popular types of explanations are feature attributions, and counterfactual explanations. These classes of approaches have been largely studied independently and the few attempts at reconciling them have been primarily empirical. This work establishes a clear theoretical connection between game-theoretic feature attributions, focusing on but not limited to SHAP, and counterfactuals explanations. After motivating operative changes to Shapley values based feature attributions and counterfactual explanations, we prove that, under conditions, they are in fact equivalent. We then extend the equivalency result to game-theoretic solution concepts beyond Shapley values. Moreover, through the analysis of the conditions of such equivalence, we shed light on the limitations of naively using counterfactual explanations to provide feature importances. Experiments on three datasets quantitatively show the difference in explanations at every stage of the connection between the two approaches and corroborate the theoretical findings.

PWSHAP: A Path-Wise Explanation Model for Targeted Variables

Lucile Ter-Minassian, Oscar Clivio, Karla Diaz-Ordaz +2

Predictive black-box models can exhibit high accuracy but their opaque nature hinders their uptake in safety-critical deployment environments. Explanation methods (XAI) can provide confidence for decision-making through increased transparency. However, existing XAI methods are not tailored towards models in sensitive domains where one predictor is of special interest, such as a treatment effect in a clinical model, or ethnicity in policy models. We introduce Path-Wise Shapley effects (PWSHAP), a framework for assessing the targeted effect of a binary (e.g.~treatment) variable from a complex outcome model. Our approach augments the predictive model with a user-defined directed acyclic graph (DAG). The method then uses the graph alongside on-manifold Shapley values to identify effects along causal pathways whilst maintaining robustness to adversarial attacks. We establish error bounds for the identified path-wise Shapley effects and for Shapley values. We show PWSHAP can perform local bias and mediation analyses with faithfulness to the model. Further, if the targeted variable is randomised we can quantify local effect modification. We demonstrate the resolution, interpretability, and true locality of our approach on examples and a real-world experiment.

Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performance, and epidemic spread. Today, these and many more issues can be studied at global scale thanks to the digital footprints that we generate when browsing the Web or using social media platforms. Unfortunately, scientists often struggle to access to such data primarily because it is proprietary, and even when it is shared with privacy guarantees, such data is either no representative or too big. In this tutorial, we will discuss recent advances and future directions in network modeling. In particular, we focus on how to exploit synthetic networks to study real-world problems such as data privacy, spreading dynamics, algorithmic bias, and ranking inequalities. We start by reviewing different types of generative models for social networks including node-attributed and scale-free networks. Then, we showcase how to perform a network selection analysis to characterize the mechanisms of edge formation of any given real-world network.

As machine learning models become increasingly prevalent in medical diagnostics, the need for interpretability and transparency becomes paramount. The XAI Renaissance signifies a significant shift in the field, aiming to redefine the interpretability of medical diagnostic models. This paper explores the innovative approaches and methodologies within the realm of Explainable AI (XAI) that are revolutionizing the interpretability of medical diagnostic models. By shedding light on the underlying decision-making process, XAI techniques empower healthcare professionals to understand, trust, and effectively utilize these models for accurate and reliable medical diagnoses. This review highlights the key advancements in XAI for medical diagnostics and their potential to transform the healthcare landscape, ultimately improving patient outcomes and fostering trust in AI-driven diagnostic systems.

Large Language Models (LLMs) have emerged as transformative forces in the realm of natural language processing, wielding the power to generate human-like text. However, despite their potential for content creation, they carry the risk of eroding our Semantic Capital (SC) - the collective knowledge within our digital ecosystem - thereby posing diverse social epistemic challenges. This paper explores the evolution, capabilities, and limitations of these models, while highlighting ethical concerns they raise. The study contribution is two-fold: first, it is acknowledged that, withstanding the challenges of tracking and controlling LLM impacts, it is necessary to reconsider our interaction with these AI technologies and the narratives that form public perception of them. It is argued that before achieving this goal, it is essential to confront a potential deontological tipping point in an increasing AI-driven infosphere. This goes beyond just adhering to AI ethical norms or regulations and requires understanding the spectrum of social epistemic risks LLMs might bring to our collective SC. Secondly, building on Luciano Floridi's taxonomy for SC risks, those are mapped within the functionality and constraints of LLMs. By this outlook, we aim to protect and enrich our SC while fostering a collaborative environment between humans and AI that augments human intelligence rather than replacing it.

This paper follows calls for critical approaches to computing and conceptualisations of intersectional, feminist, decolonial HCI and AI design and asks what a feminist intersectional perspective in HCXAI research and design might look like. Sketching out initial research directions and implications for explainable AI design, it suggests that explainability from a feminist perspective would include the fostering of response-ability - the capacity to critically evaluate and respond to AI systems - and would centre marginalised perspectives.

Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces

Georgii Mikriukov, Gesina Schwalbe, Christian Hellert +1

Safety-critical applications require transparency in artificial intelligence (AI) components, but widely used convolutional neural networks (CNNs) widely used for perception tasks lack inherent interpretability. Hence, insights into what CNNs have learned are primarily based on performance metrics, because these allow, e.g., for cross-architecture CNN comparison. However, these neglect how knowledge is stored inside. To tackle this yet unsolved problem, our work proposes two methods for estimating the layer-wise similarity between semantic information inside CNN latent spaces. These allow insights into both the flow and likeness of semantic information within CNN layers, and into the degree of their similarity between different network architectures. As a basis, we use two renowned explainable artificial intelligence (XAI) techniques, which are used to obtain concept activation vectors, i.e., global vector representations in the latent space. These are compared with respect to their activation on test inputs. When applied to three diverse object detectors and two datasets, our methods reveal that (1) similar semantic concepts are learned regardless of the CNN architecture, and (2) similar concepts emerge in similar relative layer depth, independent of the total number of layers. Finally, our approach poses a promising step towards semantic model comparability and comprehension of how different CNNs process semantic information.

A Conceptual Algorithm for Applying Ethical Principles of AI to Medical Practice

Debesh Jha, Gorkem Durak, Vanshali Sharma +12

Artificial Intelligence (AI) is poised to transform healthcare delivery through revolutionary advances in clinical decision support and diagnostic capabilities. While human expertise remains foundational to medical practice, AI-powered tools are increasingly matching or exceeding specialist-level performance across multiple domains, paving the way for a new era of democratized healthcare access. These systems promise to reduce disparities in care delivery across demographic, racial, and socioeconomic boundaries by providing high-quality diagnostic support at scale. As a result, advanced healthcare services can be affordable to all populations, irrespective of demographics, race, or socioeconomic background. The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care. In contrast to humans, AI can potentially uncover complex relationships in the data from a large set of inputs and lead to new evidence-based knowledge in medicine. However, integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability. In this study, we examine recent advances in AI-enabled medical image analysis, current regulatory frameworks, and emerging best practices for clinical integration. We analyze both technical and ethical challenges inherent in deploying AI systems across healthcare institutions, with particular attention to data privacy, algorithmic fairness, and system transparency. Furthermore, we propose practical solutions to address key challenges, including data scarcity, racial bias in training datasets, limited model interpretability, and systematic algorithmic biases. Finally, we outline a conceptual algorithm for responsible AI implementations and identify promising future research and development directions.

As the use of deep neural networks continues to grow, understanding their behaviour has become more crucial than ever. Post-hoc explainability methods are a potential solution, but their reliability is being called into question. Our research investigates the response of post-hoc visual explanations to naturally occurring transformations, often referred to as augmentations. We anticipate explanations to be invariant under certain transformations, such as changes to the colour map while responding in an equivariant manner to transformations like translation, object scaling, and rotation. We have found remarkable differences in robustness depending on the type of transformation, with some explainability methods (such as LRP composites and Guided Backprop) being more stable than others. We also explore the role of training with data augmentation. We provide evidence that explanations are typically less robust to augmentation than classification performance, regardless of whether data augmentation is used in training or not.

Explainable AI (XAI) methods provide explanations of AI models, but our understanding of how they compare with human explanations remains limited. In image classification, we found that humans adopted more explorative attention strategies for explanation than the classification task itself. Two representative explanation strategies were identified through clustering: One involved focused visual scanning on foreground objects with more conceptual explanations diagnostic for inferring class labels, whereas the other involved explorative scanning with more visual explanations rated higher for effectiveness. Interestingly, XAI saliency-map explanations had the highest similarity to the explorative attention strategy in humans, and explanations highlighting discriminative features from invoking observable causality through perturbation had higher similarity to human strategies than those highlighting internal features associated with higher class score. Thus, humans differ in information and strategy use for explanations, and XAI methods that highlight features informing observable causality match better with human explanations, potentially more accessible to users.