Research Papers

Explainable Artificial Intelligence (XAI) addresses the growing need for transparency and interpretability in AI systems, enabling trust and accountability in decision-making processes. This book offers a comprehensive guide to XAI, bridging foundational concepts with advanced methodologies. It explores interpretability in traditional models such as Decision Trees, Linear Regression, and Support Vector Machines, alongside the challenges of explaining deep learning architectures like CNNs, RNNs, and Large Language Models (LLMs), including BERT, GPT, and T5. The book presents practical techniques such as SHAP, LIME, Grad-CAM, counterfactual explanations, and causal inference, supported by Python code examples for real-world applications. Case studies illustrate XAI's role in healthcare, finance, and policymaking, demonstrating its impact on fairness and decision support. The book also covers evaluation metrics for explanation quality, an overview of cutting-edge XAI tools and frameworks, and emerging research directions, such as interpretability in federated learning and ethical AI considerations. Designed for a broad audience, this resource equips readers with the theoretical insights and practical skills needed to master XAI. Hands-on examples and additional resources are available at the companion GitHub repository: https://github.com/Echoslayer/XAI_From_Classical_Models_to_LLMs.

Transparency, Governance and Regulation of Algorithmic Tools Deployed in the Criminal Justice System: a UK Case Study

Miri Zilka, Holli Sargeant, Adrian Weller

We present a survey of tools used in the criminal justice system in the UK in three categories: data infrastructure, data analysis, and risk prediction. Many tools are currently in deployment, offering potential benefits, including improved efficiency and consistency. However, there are also important concerns. Transparent information about these tools, their purpose, how they are used, and by whom is difficult to obtain. Even when information is available, it is often insufficient to enable a satisfactory evaluation. More work is needed to establish governance mechanisms to ensure that tools are deployed in a transparent, safe and ethical way. We call for more engagement with stakeholders and greater documentation of the intended goal of a tool, how it will achieve this goal compared to other options, and how it will be monitored in deployment. We highlight additional points to consider when evaluating the trustworthiness of deployed tools and make concrete proposals for policy.

Purpose: The governance of artificial iintelligence (AI) systems requires a structured approach that connects high-level regulatory principles with practical implementation. Existing frameworks lack clarity on how regulations translate into conformity mechanisms, leading to gaps in compliance and enforcement. This paper addresses this critical gap in AI governance. Methodology/Approach: A five-layer AI governance framework is proposed, spanning from broad regulatory mandates to specific standards, assessment methodologies, and certification processes. By narrowing its scope through progressively focused layers, the framework provides a structured pathway to meet technical, regulatory, and ethical requirements. Its applicability is validated through two case studies on AI fairness and AI incident reporting. Findings: The case studies demonstrate the framework's ability to identify gaps in legal mandates, standardization, and implementation. It adapts to both global and region-specific AI governance needs, mapping regulatory mandates with practical applications to improve compliance and risk management. Practical Implications - By offering a clear and actionable roadmap, this work contributes to global AI governance by equipping policymakers, regulators, and industry stakeholders with a model to enhance compliance and risk management. Social Implications: The framework supports the development of policies that build public trust and promote the ethical use of AI for the benefit of society. Originality/Value: This study proposes a five-layer AI governance framework that bridges high-level regulatory mandates and implementation guidelines. Validated through case studies on AI fairness and incident reporting, it identifies gaps such as missing standardized assessment procedures and reporting mechanisms, providing a structured foundation for targeted governance measures.

A Review of the Role of Causality in Developing Trustworthy AI Systems

Niloy Ganguly, Dren Fazlija, Maryam Badar +9

State-of-the-art AI models largely lack an understanding of the cause-effect relationship that governs human understanding of the real world. Consequently, these models do not generalize to unseen data, often produce unfair results, and are difficult to interpret. This has led to efforts to improve the trustworthiness aspects of AI models. Recently, causal modeling and inference methods have emerged as powerful tools. This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models. We hope that our contribution will motivate future research on causality-based solutions for trustworthy AI.

We demonstrate how easy it is for modern machine-learned systems to violate common deontological ethical principles and social norms such as "favor the less fortunate," and "do not penalize good attributes." We propose that in some cases such ethical principles can be incorporated into a machine-learned model by adding shape constraints that constrain the model to respond only positively to relevant inputs. We analyze the relationship between these deontological constraints that act on individuals and the consequentialist group-based fairness goals of one-sided statistical parity and equal opportunity. This strategy works with sensitive attributes that are Boolean or real-valued such as income and age, and can help produce more responsible and trustworthy AI.

Algorithmic and data bias are gaining attention as a pressing issue in popular press - and rightly so. However, beyond these calls to action, standard processes and tools for practitioners do not readily exist to assess and address unfair algorithmic and data biases. The literature is relatively scattered and the needed interdisciplinary approach means that very different communities are working on the topic. We here provide a number of challenges encountered in assessing and addressing algorithmic and data bias in practice. We describe an early approach that attempts to translate the literature into processes for (production) teams wanting to assess both intended data and algorithm characteristics and unintended, unfair biases.

Responding to the rapid roll-out and large-scale commercialization of foundation models, large language models, and generative AI, an emerging body of work is shedding light on the myriad impacts these technologies are having across society. Such research is expansive, ranging from the production of discriminatory, fake and toxic outputs, and privacy and copyright violations, to the unjust extraction of labor and natural resources. The same has not been the case in some of the most prominent AI governance initiatives in the global north like the UK's AI Safety Summit and the G7's Hiroshima process, which have influenced much of the international dialogue around AI governance. Despite the wealth of cautionary tales and evidence of algorithmic harm, there has been an ongoing over-emphasis within the AI governance discourse on technical matters of safety and global catastrophic or existential risks. This narrowed focus has tended to draw attention away from very pressing social and ethical challenges posed by the current brute-force industrialization of AI applications. To address such a visibility gap between real-world consequences and speculative risks, this paper offers a critical framework to account for the social, political, and environmental dimensions of foundation models and generative AI. We identify 14 categories of risks and harms and map them according to their individual, social, and biospheric impacts. We argue that this novel typology offers an integrative perspective to address the most urgent negative impacts of foundation models and their downstream applications. We conclude with recommendations on how this typology could be used to inform technical and normative interventions to advance responsible AI.

Prior research has revealed that certain abstract concepts are linearly represented as directions in the representation space of LLMs, predominantly centered around English. In this paper, we extend this investigation to a multilingual context, with a specific focus on human values-related concepts (i.e., value concepts) due to their significance for AI safety. Through our comprehensive exploration covering 7 types of human values, 16 languages and 3 LLM series with distinct multilinguality (e.g., monolingual, bilingual and multilingual), we first empirically confirm the presence of value concepts within LLMs in a multilingual format. Further analysis on the cross-lingual characteristics of these concepts reveals 3 traits arising from language resource disparities: cross-lingual inconsistency, distorted linguistic relationships, and unidirectional cross-lingual transfer between high- and low-resource languages, all in terms of value concepts. Moreover, we validate the feasibility of cross-lingual control over value alignment capabilities of LLMs, leveraging the dominant language as a source language. Ultimately, recognizing the significant impact of LLMs' multilinguality on our results, we consolidate our findings and provide prudent suggestions on the composition of multilingual data for LLMs pre-training.

Language model alignment has become an important component of AI safety, allowing safe interactions between humans and language models, by enhancing desired behaviors and inhibiting undesired ones. It is often done by tuning the model or inserting preset aligning prompts. Recently, representation engineering, a method which alters the model's behavior via changing its representations post-training, was shown to be effective in aligning LLMs (Zou et al., 2023a). Representation engineering yields gains in alignment oriented tasks such as resistance to adversarial attacks and reduction of social biases, but was also shown to cause a decrease in the ability of the model to perform basic tasks. In this paper we study the tradeoff between the increase in alignment and decrease in helpfulness of the model. We propose a theoretical framework which provides bounds for these two quantities, and demonstrate their relevance empirically. First, we find that under the conditions of our framework, alignment can be guaranteed with representation engineering, and at the same time that helpfulness is harmed in the process. Second, we show that helpfulness is harmed quadratically with the norm of the representation engineering vector, while the alignment increases linearly with it, indicating a regime in which it is efficient to use representation engineering. We validate our findings empirically, and chart the boundaries to the usefulness of representation engineering for alignment.

Generative AI based art has proliferated in the past year, with increasingly impressive use cases from generating fake human faces to the creation of systems that can generate thousands of artistic images from text prompts - some of these images have even been "good" enough to win accolades from qualified judges. In this paper, we explore how Generative Models have impacted artistry, not only from a qualitative point of view, but also from an angle of exploitation of artists -- both via plagiarism, where models are trained on their artwork without permission, and via profit shifting, where profits in the art market have shifted from art creators to model owners. However, we posit that if deployed responsibly, AI generative models have the possibility of being a positive, new modality in art that does not displace or harm existing artists.

Ensuring fairness in artificial intelligence (AI) is important to counteract bias and discrimination in far-reaching applications. Recent work has started to investigate how humans judge fairness and how to support machine learning (ML) experts in making their AI models fairer. Drawing inspiration from an Explainable AI (XAI) approach called \emph{explanatory debugging} used in interactive machine learning, our work explores designing interpretable and interactive human-in-the-loop interfaces that allow ordinary end-users without any technical or domain background to identify potential fairness issues and possibly fix them in the context of loan decisions. Through workshops with end-users, we co-designed and implemented a prototype system that allowed end-users to see why predictions were made, and then to change weights on features to "debug" fairness issues. We evaluated the use of this prototype system through an online study. To investigate the implications of diverse human values about fairness around the globe, we also explored how cultural dimensions might play a role in using this prototype. Our results contribute to the design of interfaces to allow end-users to be involved in judging and addressing AI fairness through a human-in-the-loop approach.

Diversity-aware platform design is a paradigm that responds to the ethical challenges of existing social media platforms. Available platforms have been criticized for minimizing users' autonomy, marginalizing minorities, and exploiting users' data for profit maximization. This paper presents a design solution that centers the well-being of users. It presents the theory and practice of designing a diversity-aware platform for social relations. In this approach, the diversity of users is leveraged in a way that allows like-minded individuals to pursue similar interests or diverse individuals to complement each other in a complex activity. The end users of the envisioned platform are students, who participate in the design process. Diversity-aware platform design involves numerous steps, of which two are highlighted in this paper: 1) defining a framework and operationalizing the "diversity" of students, 2) collecting "diversity" data to build diversity-aware algorithms. The paper further reflects on the ethical challenges encountered during the design of a diversity-aware platform.