Research Papers

Cancer remains a leading global health challenge and a major cause of mortality. This study leverages machine learning (ML) to predict the survivability of cancer patients with metastatic patterns using the comprehensive MSK-MET dataset, which includes genomic and clinical data from 25,775 patients across 27 cancer types. We evaluated five ML models-XGBoost, Naïve Bayes, Decision Tree, Logistic Regression, and Random Fores using hyperparameter tuning and grid search. XGBoost emerged as the best performer with an area under the curve (AUC) of 0.82. To enhance model interpretability, SHapley Additive exPlanations (SHAP) were applied, revealing key predictors such as metastatic site count, tumor mutation burden, fraction of genome altered, and organ-specific metastases. Further survival analysis using Kaplan-Meier curves, Cox Proportional Hazards models, and XGBoost Survival Analysis identified significant predictors of patient outcomes, offering actionable insights for clinicians. These findings could aid in personalized prognosis and treatment planning, ultimately improving patient care.

Counterfactual explanations have been successfully applied to create human interpretable explanations for various black-box models. They are handy for tasks in the image domain, where the quality of the explanations benefits from recent advances in generative models. Although counterfactual explanations have been widely applied to classification models, their application to regression tasks remains underexplored. We present two methods to create counterfactual explanations for image regression tasks using diffusion-based generative models to address challenges in sparsity and quality: 1) one based on a Denoising Diffusion Probabilistic Model that operates directly in pixel-space and 2) another based on a Diffusion Autoencoder operating in latent space. Both produce realistic, semantic, and smooth counterfactuals on CelebA-HQ and a synthetic data set, providing easily interpretable insights into the decision-making process of the regression model and reveal spurious correlations. We find that for regression counterfactuals, changes in features depend on the region of the predicted value. Large semantic changes are needed for significant changes in predicted values, making it harder to find sparse counterfactuals than with classifiers. Moreover, pixel space counterfactuals are more sparse while latent space counterfactuals are of higher quality and allow bigger semantic changes.

Raman spectroscopy is becoming more common for medical diagnostics with deep learning models being increasingly used to leverage its full potential. However, the opaque nature of such models and the sensitivity of medical diagnosis together with regulatory requirements necessitate the need for explainable AI tools. We introduce SpecReX, specifically adapted to explaining Raman spectra. SpecReX uses the theory of actual causality to rank causal responsibility in a spectrum, quantified by iteratively refining mutated versions of the spectrum and testing if it retains the original classification. The explanations provided by SpecReX take the form of a responsibility map, highlighting spectral regions most responsible for the model to make a correct classification. To assess the validity of SpecReX, we create increasingly complex simulated spectra, in which a "ground truth" signal is seeded, to train a classifier. We then obtain SpecReX explanations and compare the results with another explainability tool. By using simulated spectra we establish that SpecReX localizes to the known differences between classes, under a number of conditions. This provides a foundation on which we can find the spectral features which differentiate disease classes. This is an important first step in proving the validity of SpecReX.

Machine learning (ML) models may suffer from significant performance disparities between patient groups. Identifying such disparities by monitoring performance at a granular level is crucial for safely deploying ML to each patient. Traditional subgroup analysis based on metadata can expose performance disparities only if the available metadata (e.g., patient sex) sufficiently reflects the main reasons for performance variability, which is not common. Subgroup discovery techniques that identify cohesive subgroups based on learned feature representations appear as a potential solution: They could expose hidden stratifications and provide more granular subgroup performance reports. However, subgroup discovery is challenging to evaluate even as a standalone task, as ground truth stratification labels do not exist in real data. Subgroup discovery has thus neither been applied nor evaluated for the application of subgroup performance monitoring. Here, we apply subgroup discovery for performance monitoring in chest x-ray and skin lesion classification. We propose novel evaluation strategies and show that a simplified subgroup discovery method without access to classification labels or metadata can expose larger performance disparities than traditional metadata-based subgroup analysis. We provide the first compelling evidence that subgroup discovery can serve as an important tool for comprehensive performance validation and monitoring of trustworthy AI in medicine.

This paper examines the intricate interplay among AI safety, security, and governance by integrating technical systems engineering with principles of moral imagination and ethical philosophy. Drawing on foundational insights from Weapons of Math Destruction and Thinking in Systems alongside contemporary debates in AI ethics, we develop a comprehensive multi-dimensional framework designed to regulate AI technologies deployed in high-stakes domains such as defense, finance, healthcare, and education. Our approach combines rigorous technical analysis, quantitative risk assessment, and normative evaluation to expose systemic vulnerabilities inherent in opaque, black-box models. Detailed case studies, including analyses of Microsoft Tay (2016) and the UK A-Level Grading Algorithm (2020), demonstrate how security lapses, bias amplification, and lack of accountability can precipitate cascading failures that undermine public trust. We conclude by outlining targeted strategies for enhancing AI resilience through adaptive regulatory mechanisms, robust security protocols, and interdisciplinary oversight, thereby advancing the state of the art in ethical and technical AI governance.

The rapid advancement of generative AI has enabled the creation of pre-mortem digital twins, AI-driven replicas that mimic the behavior, personality, and knowledge of living individuals. These digital doppelgangers serve various functions, including enhancing productivity, enabling creative collaboration, and preserving personal legacies. However, their development raises critical ethical, legal, and societal concerns. Issues such as identity fragmentation, psychological effects on individuals and their social circles, and the risks of unauthorized cloning and data exploitation demand careful examination. Additionally, as these AI clones evolve into more autonomous entities, concerns about consent, ownership, and accountability become increasingly complex. This paper differentiates pre-mortem AI clones from post-mortem generative ghosts, examining their unique ethical and legal implications. We explore key challenges, including the erosion of personal identity, the implications of AI agency, and the regulatory gaps in digital rights and privacy laws. Through a research-driven approach, we propose a framework for responsible AI governance, emphasizing identity preservation, consent mechanisms, and autonomy safeguards. By aligning technological advancements with societal values, this study contributes to the growing discourse on AI ethics and provides policy recommendations for the ethical deployment of pre-mortem AI clones.

Educating a Responsible AI Workforce: Piloting a Curricular Module on AI Policy in a Graduate Machine Learning Course

James Weichert, Hoda Eldardiry

As artificial intelligence (AI) technologies begin to permeate diverse fields-from healthcare to education-consumers, researchers and policymakers are increasingly raising concerns about whether and how AI is regulated. It is therefore reasonable to anticipate that alignment with principles of 'ethical' or 'responsible' AI, as well as compliance with law and policy, will form an increasingly important part of AI development. Yet, for the most part, the conventional computer science curriculum is ill-equipped to prepare students for these challenges. To this end, we seek to explore how new educational content related to AI ethics and AI policy can be integrated into both ethics- and technical-focused courses. This paper describes a two-lecture 'AI policy module' that was piloted in a graduate-level introductory machine learning course in 2024. The module, which includes an in-class active learning game, is evaluated using data from student surveys before and after the lectures, and pedagogical motivations and considerations are discussed. We find that the module is successful in engaging otherwise technically-oriented students on the topic of AI policy, increasing student awareness of the social impacts of a variety of AI technologies and developing student interest in the field of AI regulation.

Powerful new frontier AI technologies are bringing many benefits to society but at the same time bring new risks. AI developers and regulators are therefore seeking ways to assure the safety of such systems, and one promising method under consideration is the use of safety cases. A safety case presents a structured argument in support of a top-level claim about a safety property of the system. Such top-level claims are often presented as a binary statement, for example "Deploying the AI system does not pose unacceptable risk". However, in practice, it is often not possible to make such statements unequivocally. This raises the question of what level of confidence should be associated with a top-level claim. We adopt the Assurance 2.0 safety assurance methodology, and we ground our work by specific application of this methodology to a frontier AI inability argument that addresses the harm of cyber misuse. We find that numerical quantification of confidence is challenging, though the processes associated with generating such estimates can lead to improvements in the safety case. We introduce a method for better enabling reproducibility and transparency in probabilistic assessment of confidence in argument leaf nodes through a purely LLM-implemented Delphi method. We propose a method by which AI developers can prioritise, and thereby make their investigation of argument defeaters more efficient. Proposals are also made on how best to communicate confidence information to executive decision-makers.

Explainable AI has garnered considerable attention in recent years, as understanding the reasons behind decisions or predictions made by AI systems is crucial for their successful adoption. Explaining classifiers' behavior is one prominent problem. Work in this area has proposed notions of both local and global explanations, where the former are concerned with explaining a classifier's behavior for a specific instance, while the latter are concerned with explaining the overall classifier's behavior regardless of any specific instance. In this paper, we focus on global explanations, and explain classification in terms of ``minimal'' necessary conditions for the classifier to assign a specific class to a generic instance. We carry out a thorough complexity analysis of the problem for natural minimality criteria and important families of classifiers considered in the literature.

Artificial intelligence (AI) and machine learning (ML) have made tremendous advancements in the past decades. From simple recommendation systems to more complex tumor identification systems, AI/ML systems have been utilized in a plethora of applications. This rapid growth of AI/ML and its proliferation in numerous private and public sector applications, while successful, has also opened new challenges and obstacles for regulators. With almost little to no human involvement required for some of the new decision-making AI/ML systems, there is now a pressing need to ensure the responsible use of these systems. Particularly in federal government use-cases, the use of AI technologies must be carefully governed by appropriate transparency and accountability mechanisms. This has given rise to new interdisciplinary fields of AI research such as \textit{Responsible AI (RAI)}. In this position paper we provide a brief overview of development in RAI and discuss some of the motivating principles commonly explored in the field. An overview of the current regulatory landscape relating to AI is also discussed with analysis of different Executive Orders, policies and frameworks. We then present examples of how federal agencies are aiming for the responsible use of AI, specifically we present use-case examples of different projects and research from the Census Bureau on implementing the responsible use of AI. We also provide a brief overview for a Responsible AI Assessment Toolkit currently under-development aimed at helping federal agencies operationalize RAI principles. Finally, a robust discussion on how different policies/regulations map to RAI principles, along with challenges and opportunities for regulation/governance of responsible AI within the federal government is presented.

Traditional recommender systems based on revealed preferences often fail to capture the fundamental duality in user behavior, where consumption choices are driven by both inherent value (enrichment) and instant appeal (temptation). Consequently, these systems may generate recommendations that prioritize short-term engagement over long-lasting user satisfaction. We propose a novel recommender design that explicitly models the tension between enrichment and temptation. We introduce a behavioral model that accounts for how both enrichment and temptation influence user choices, while incorporating the reality of off-platform alternatives. Building on this model, we formulate a novel recommendation objective aligned with maximizing consumed enrichment and prove the optimality of a locally greedy recommendation strategy. Finally, we present an estimation framework that leverages the distinction between explicit user feedback and implicit choice data while making minimal assumptions about off-platform options. Through comprehensive evaluation using both synthetic simulations and real-world data from the MovieLens dataset, we demonstrate that our approach consistently outperforms competitive baselines that ignore temptation dynamics either by assuming revealed preferences or recommending solely based on enrichment. Our work represents a paradigm shift toward more nuanced and user-centric recommender design, with significant implications for developing responsible AI systems that genuinely serve users' long-term interests rather than merely maximizing engagement.

The rapid proliferation of multimodal generative models has sparked critical discussions on their reliability, fairness and potential for misuse. While text-to-image models excel at producing high-fidelity, user-guided content, they often exhibit unpredictable behaviors and vulnerabilities that can be exploited to manipulate class or concept representations. To address this, we propose an evaluation framework to assess model reliability by analyzing responses to global and local perturbations in the embedding space, enabling the identification of inputs that trigger unreliable or biased behavior. Beyond social implications, fairness and diversity are fundamental to defining robust and trustworthy model behavior. Our approach offers deeper insights into these essential aspects by evaluating: (i) generative diversity, measuring the breadth of visual representations for learned concepts, and (ii) generative fairness, which examines the impact that removing concepts from input prompts has on control, under a low guidance setup. Beyond these evaluations, our method lays the groundwork for detecting unreliable, bias-injected models and tracing the provenance of embedded biases. Our code is publicly available at https://github.com/JJ-Vice/T2I_Fairness_Diversity_Reliability. Keywords: Fairness, Reliability, AI Ethics, Bias, Text-to-Image Models