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Tanusree Ghosh

The rise of generative AI presents a profound duality. On one hand, it offers a powerful solution to data scarcity and privacy challenges in biometrics. On the other, it is weaponized to create deepfakes that threaten digital integrity. Existing detectors for these deepfakes are brittle, failing against real-world transformations and novel generative models. This dissertation confronts this duality head-on. First, I establish the viability of synthetic data for building fair and private biometric systems. Second, to counter the malicious use of this technology, this dissertation develops deepfake detectors designed to be robust, generalizable, and efficient by construction. My work introduces novel, lightweight feature sets on different cues (e.g., colour cue-based Relative Chrominance Difference, Gradient features, Depth cues, etc.) that are inherently resilient to OSN transformations and improve generalisation to unseen forgeries. Whereas, accomplished results confirm state-of-the-art performance, achieving high accuracy in challenging real-world scenarios with a significant reduction in model complexity, my current and future work focuses on achieving superior generalisation while being OSN manipulation resistant.

Müge Fidan

The PhD studies of the author focus on a human-centered and computationally-challenging interdisciplinary problem of the Stable Roommates problem and its variations. Motivated by real-world applications, and by the fact that the Stable Roommates problem does not always admit a stable solution, the goal is to develop novel computational methods to solve these problems, that are not only computationally efficient but also yield solutions that are fair, personalized, and applicable in real-world to benefit humans.

Jessica Vandenberg, Keisha Bailey, Claire Aguiar, Cecilia Xuning Zhang, Danny Schmidt, Treshonda Rutledge, Bradford Mott, Joseph P. Wilson

Introducing AI concepts in the earliest years of schooling can help children make sense of intelligent technologies, yet few resources exist for K–2 classrooms. This paper presents the design and outcomes of a professional development (PD) program supporting K–2 teachers as they explored AI literacy and co-designed unplugged classroom activities. Grounded in AI4K12's Five Big Ideas in AI framework, the PD combined hands-on learning, collaborative design, and micro-teaching opportunities. Guiding activities included Train the AI (pattern recognition), What Happens Next? (consequences of AI use), Who Did the Robot Hear? (data diversity), and Teach the Robot (model training). Educators then created screen-free, English Language Arts-aligned activities using storytelling, sorting, and embodied play to introduce AI topics such as machine learning and fairness in age-appropriate ways. The PD emphasized integrating AI into existing K–2 literacy routines, lowering implementation barriers while supporting vocabulary development, reasoning, and empathy. Teacher reflections revealed growing confidence in adapting AI topics for young learners and highlighted the value of peer collaboration, clear language, and tactile materials.

Phan Xuan Tan, Eiji Kamioka, Van Nguyen

Generative AI has moved from pilots to everyday practice, delivering gains in productivity and accessibility while surfacing present-day risks—hallucinations and reliability failures, bias and unfairness, prompt-injection attacks, and so on. These trends make AI safety education a core competency. In this paper, we survey global AI safety curricula and, in the Japanese context, observe strong policy momentum but relatively few courses that explicitly combine capability instruction with systematic safety evaluation. In response, we developed a 7-week, graduate-level intensive at a private science and engineering university in Japan, with enrollment open to international exchange students at both the undergraduate and graduate levels. The curriculum progresses from machine-learning foundations to generative models and alignment, with introductory agent topics included to support risk reasoning. Delivery combines weekly lectures, invited talks from academia and industry, structured group discussions, and a final presentation plus a paper-style final project focused on risk evaluation and mitigation planning. An end-of-course survey indicates high perceived learning and positive experience and one student project later resulted in a peer-reviewed workshop paper at ICLR 2025.

Jayakrishnan Madathil, Neeldhara Misra, Aditi Sethia

We study almost envy-freeness in house allocation, where m houses are to be allocated among n agents so that every agent receives exactly one house. An envy-free allocation need not exist, and therefore we may have to settle for relaxations. We study different aggregate measures of envy as markers of fairness. In particular, we define the amount of envy experienced by an agent a w.r.t. an allocation to be the number of agents that agent a envies under that allocation. We quantify the envy generated by an allocation using three different metrics: 1) the number of agents who are envious; 2) the maximum amount of envy experienced by any agent; and 3) the total amount of envy experienced by all agents, and look for allocations that minimize one of the three metrics. We prove a host of algorithmic and hardness results. We also suggest practical approaches for these problems via integer linear program (ILP) formulations and report the findings of our experimental evaluation of ILPs. Finally, we study the price of fairness, which quantifies the loss of welfare we must suffer due to the fairness requirements, and present tight bounds as well as algorithms that simultaneously optimize both welfare and fairness.

Daxin Liu, Gerhard Lakemeyer

Belief-based programming is a probabilistic extension of the GOLOG program family where every action and sensing result can be noisy and every test condition refers to the agent’s subjective beliefs. Inherited from GOLOG programs, the action-centered feature makes belief programs fairly suitable for high-level robot control under uncertainty. An important step before deploying such a program is to verify whether it satisfies certain properties. At least two problems exist in verifying such programs: how to formally specify program properties and what is the complexity of the verification problem. In this paper, we propose a formalism for belief programs based on a modal logic of actions and beliefs which allows us to conveniently express PCTL-like temporal properties. We also investigate the decidability and undecidability of the verification problem.

Warut Suksompong, Nicholas Teh

Voting is one of the most prominent applications of preference aggregation and computational social choice. While much of the literature focuses on models involving discrete candidates, there has been a growing interest in voting over divisible resources, such as budget, space, and time. In this survey, we review existing work on voting in divisible settings, including fundamental models of budget aggregation, fair mixing, and cake sharing. We also establish connections among these models, highlight unifying themes across different frameworks, and suggest directions for future research.

Pavel Naumov

Traditionally, the goal of mechanism design was to promote socially desirable behaviour of rational agents, to achieve fairness, or to promote efficiency. I would like to suggest a new subfield of mechanism design, Responsible Mechanism Design, focused on achieving individual accountability of agents for their contributions to the outcome of collective decisions.

Hen-Hsen Huang

Large language models (LLMs) have become indispensable, but the most celebrated efficiency methods---mixture-of-experts (MoE), speculative decoding, and complex retrieval-augmented generation (RAG)---were built for hyperscale providers with vast infrastructure and elite teams. Outside that context, their benefits collapse into overhead, fragility, and wasted carbon. The result is that a handful of Big Tech companies benefit, while thousands of hospitals, schools, governments, and enterprises are left without viable options. We argue that the next frontier is not greater sophistication at scale, but robust simplicity: efficiency that thrives under modest resources and minimal expertise. We propose a new research agenda: retrofitting pretrained models with more efficient architectures without retraining, inventing lightweight fine-tuning that preserves alignment, making reasoning economical despite long chains of thought, enabling dynamic knowledge management without heavy RAG pipelines, and adopting Overhead-Aware Efficiency (OAE) as a standard benchmark. By redefining efficiency to include adoption cost, sustainability, and fairness, we can democratize LLM deployment---ensuring that optimization reduces inequality and carbon waste rather than amplifying them.

XuWen Zhang, Xiao Xue, Xia Xie, Qun Ma

Crowdsourced delivery platforms (e.g., Meituan, Uber Eats, DoorDash) have become vital infrastructure in urban logistics, yet their competitive order-grabbing mechanisms often lead to strategy homogenization, inefficiency, and income inequality. This paper presents ARDE (Adaptive Regulation via Dual-layer Evolution), an evolutionary governance framework that integrates individual reinforcement learning with adaptive platform-level regulation. The outer agent dynamically generates governance signals based on system diagnostics (strategy entropy, Gini coefficient, completion rate), while inner agents employ Diffusion Q-Learning guided by a language-model-driven reward shaping module to promote fairness and strategy diversity. Experiments on real-world datasets show that ARDE achieves stable diversity (0.997 ± 0.184), reduces inequality (Gini change 1.3%), and maintains high efficiency. Further comparison (ARDE-PPO vs. MAPPO) confirms that its advantages stem from explicit hierarchical governance rather than algorithmic coincidence. Overall, ARDE offers a scalable and interpretable paradigm for reconciling individual rationality with collective welfare in gig economies and other multi-agent socio-technical systems.

Chenyu Zhang, Xiaohang Luo

Formative feedback is widely recognized as one of the most effective drivers of student learning, yet it remains difficult to implement equitably at scale. In large or low-resource courses, instructors often lack the time, staffing, and bandwidth required to review and respond to every student reflection, creating gaps in support precisely where learners would benefit most. This paper presents a theory-grounded system that uses five coordinated role-based LLM agents (Evaluator, Equity Monitor, Metacognitive Coach, Aggregator, and Reflexion Reviewer) to score learner reflections with a shared rubric and to generate short, bias-aware, learner-facing comments. The agents first produce structured rubric scores, then check for potentially biased or exclusionary language, add metacognitive prompts that invite students to think about their own thinking, and finally compose a concise feedback message of at most 120 words. The system includes simple fairness checks that compare scoring error across lower and higher scoring learners, enabling instructors to monitor and bound disparities in accuracy. We evaluate the pipeline in a 12-session AI literacy program with adult learners. In this setting, the system produces rubric scores that approach expert-level agreement, and trained graders rate the AI-generated comments as helpful, empathetic, and well aligned with instructional goals. Taken together, these results show that multi-agent LLM systems can deliver equitable, high-quality formative feedback at a scale and speed that would be impossible for human graders alone. The approach demonstrates how structured agent roles, fairness checks, and learning-science principles can work together to support instructors while preserving pedagogical intent. More broadly, the work points toward a future where feedback-rich learning becomes feasible for any course size or context, advancing long-standing goals of equity, access, and instructional capacity in education.

Khadija Zanna, Akane Sano

Ensuring fairness in machine learning requires understanding how sensitive attributes like race or gender causally influence outcomes. Existing causal discovery (CD) methods often struggle to recover fairness-relevant pathways in the presence of noise, confounding, or data corruption. Large language models (LLMs) offer a complementary signal by leveraging semantic priors from variable metadata. We propose a hybrid LLM-guided CD framework that extends a breadth-first search strategy with active learning and dynamic scoring. Variable pairs are prioritized for querying using a composite score combining mutual information, partial correlation, and LLM confidence, enabling more efficient and robust structure discovery. To evaluate fairness sensitivity, we introduce a semi-synthetic benchmark based on the UCI Adult dataset, embedding domain-informed bias pathways alongside noise and latent confounders. We assess how well CD methods recover both global graph structure and fairness-critical paths (e.g., sex→education→income). Our results demonstrate that LLM-guided methods, including our active, dynamically scored variant, outperform baselines in recovering fairness-relevant structure under noisy conditions. We analyze when LLM-driven insights complement statistical dependencies and discuss implications for fairness auditing in high-stakes domains.

Zichong Wang, Jie Yang, Jun Zhuang, Puqing Jiang, Mingzhe Chen, Ye Hu, Wenbin Zhang

Graph neural networks (GNNs) excel at modeling graph-structured data but often inherit and amplify biases, leading to substantial efforts in developing fair GNNs. However, most existing approaches assume full access to sensitive attribute information, which is often impractical in real-world scenarios due to privacy concerns or risks of discrimination. To address this limitation, this paper focuses on graph fairness with limited sensitive attribute information, ensuring applicability to real-world contexts where current methods fall short. Specifically, we introduce an innovative fairness optimization strategy, propose a novel framework named FGLISA, and provide a theoretical perspective linking limited sensitive attribute information access to fairness objectives, thus enabling fair graph learning in real-world applications with limited sensitive attribute information. Experiments on diverse real-world datasets and tasks validate the effectiveness of our approach in achieving both fairness and predictive performance.

Xuwei Tan, Yuanlong Wang, Thai-Hoang Pham, Ping Zhang, Xueru Zhang

As machine learning systems become increasingly integrated into human-centered domains such as healthcare, ensuring fairness while maintaining high predictive performance is critical. Existing bias mitigation techniques often impose a trade-off between fairness and accuracy, inadvertently degrading performance for certain demographic groups. In high-stakes domains like clinical diagnosis, such trade-offs are ethically and practically unacceptable. In this study, we propose a fairness-without-harm approach by learning distinct representations for different demographic groups and selectively applying demographic experts consisting of group-specific representations and personalized classifiers through a no-harm constrained selection. We evaluate our approach on three real-world medical datasets—covering eye disease, skin cancer, and X-ray diagnosis—as well as two face datasets. Extensive empirical results demonstrate the effectiveness of our approach in achieving fairness without harm.

Disa Sariola, Patrick Button, Aron Culotta, Nicholas Mattei

Classifiers trained on historical data are deployed in the real world to automate decisions from hiring to loan issuance. Judging the fairness and efficiency of these systems, and their human counterparts, is a complex and important topic studied across both computational and social sciences. One common way to address bias in classifiers is to resample the training data to offset distributional disparities. In the hiring domain, where results may vary by a protected class, many interventions from the literature equalize the hiring rate within the training set to alleviate bias. While simple and seemingly effective, these methods have typically only been evaluated using data obtained through convenience samples, e.g., data from a real-world hiring process, introducing selection and label bias. In the social and health sciences, audit studies, in which fictitious "testers" (resumes) are sent to subjects (job openings) in a randomized control trial, provide high-quality data that support rigorous estimates of discrimination by controlling for confounding factors. We investigate how data from audit studies can be used to improve our ability to both train and evaluate automated hiring algorithms. Specifically, we use data from a large audit study of age discrimination in hiring to test common resampling methods from the fair machine learning literature. We find that audit data of real-world hiring reveals cases where equalizing base rates across classes appears to achieve parity using traditional measures, but in fact has an absolute ~10% disparity when measured appropriately. We also show that corrections based on individual treatment effect estimation methods combined with audit study data can overcome these issues, underscoring the need for rigorous data collection in fairness research.

Ahmed Sabir, Markus Kängsepp, Rajesh Sharma

The increased use of large language models (LLMs) in sensitive domains leads to growing interest in how their confidence scores correspond to fairness and bias. This study examines the alignment between LLM-predicted confidence and human-annotated bias judgments. Focusing on gender bias, the research investigates probability confidence calibration in contexts involving gendered pronoun resolution. The goal is to evaluate if calibration metrics based on predicted confidence scores effectively capture fairness-related disparities in LLMs. The results show that, among the six state-of-the-art models, Gemma-2 demonstrates the worst calibration according to the gender bias benchmark. The primary contribution of this work is a fairness-aware evaluation of LLMs confidence calibration, offering guidance for ethical deployment. In addition, we introduce a new calibration metric, Gender-ECE, designed to measure gender disparities in resolution tasks.

Michael Richards, Danny Cowser, Daniel Nielson, Jacob W. Crandall

Institutions are key to creating societies that are efficient, fair, and benevolent. Despite their importance, the complexities of human (networked) societies make it difficult to understand how formal institutions form and how they shape human communities. Artificial intelligence (AI) can potentially raise understanding in this regard. Thus, in this paper, we present a simulation model utilizing AI agents to simulate networked societies that contain formal institutions. We then observe the outputs of the resulting model under different societal conditions and formal institutions, and (where applicable) compare and contrast these outputs with political and economic theories. Our model outputs (a) address how inequality impacts societal prosperity, (b) illuminate how institutions can potentially impact poverty, and (c) give insights into the attributes of formal institutions that individuals are inclined to support. These and future simulation models can potentially inform how AI can support the design and development of institutions that facilitate healthier communities and nations.

Ayan Majumdar, Deborah D. Kanubala, Kavya Gupta, Isabel Valera

Fairness studies of algorithmic decision-making systems often simplify complex decision processes, such as bail or lending decisions, into binary classification tasks (e.g., approve or not approve). However, these approaches overlook that such decisions are not inherently binary; they also involve non-binary treatment decisions (e.g., loan or bail terms) that can influence the downstream outcomes (e.g., loan repayment or reoffending). We argue that treatment decisions are integral to the decision-making process and, therefore, should be central to fairness analyses. Consequently, we propose a causal framework that extends and complements existing fairness notions by explicitly distinguishing between decision-subjects’ covariates and the treatment decisions. Our framework leverages path-specific counterfactual reasoning to: (i) measure treatment disparity and its downstream effects in historical data; and (ii) mitigate the impact of past unfair treatment decisions when automating decision-making. We use our framework to empirically analyze four widely used loan approval datasets to reveal potential disparity in non-binary treatment decisions and their discriminatory impact on outcomes, highlighting the need to incorporate treatment decisions in fairness assessments. Finally, by intervening in treatment decisions, we show that our framework effectively mitigates treatment discrimination from historical loan approval data to ensure fair risk score estimation and (non-binary) decision-making processes that benefit all stakeholders.

Chenxi Lin, Weikang Yuan, Zhuoren Jiang, Biao Huang, Ruitao Zhang, Jianan Ge, Yueqian Xu, Jianxing Yu

Understanding human attitudes, preferences, and behaviors through social surveys is essential for academic research and policymaking. Yet traditional surveys face persistent challenges, including fixed-question formats, high costs, limited adaptability, and difficulties ensuring cross-cultural equivalence. While recent studies explore large language models (LLMs) to simulate survey responses, most are limited to structured questions, overlook the entire survey process, and risks under-representing marginalized groups due to training data biases. We introduce AlignSurvey, the first benchmark that systematically replicates and evaluates the full social survey pipeline using LLMs. It defines four tasks aligned with key survey stages: social role modeling, semi-structured interview modeling, attitude stance modeling and survey response modeling. It also provides task-specific evaluation metrics to assess alignment fidelity, consistency, and fairness at both individual and group levels, with a focus on demographic diversity. To support AlignSurvey, we construct a multi-tiered dataset architecture: (i) the Social Foundation Corpus, a cross-national resource with 44K+ interview dialogues and 400K+ structured survey records; and (ii) a suite of Entire-Pipeline Survey Datasets, including the expert-annotated AlignSurvey-Expert (ASE) and two nationally representative surveys for cross-cultural evaluation. We release the SurveyLM family, obtained through two-stage fine-tuning of open-source LLMs, and offer reference models for evaluating domain-specific alignment. All datasets, models, and tools are available at github and huggingface to support transparent and socially responsible research.

Hao Li, Haotian Chen, Ruoyuan Gong, Juanjuan Wang, Hao Jiang

Redistricting plays a central role in shaping how votes are translated into political power. While existing computational methods primarily aim to generate large ensembles of legally valid districting plans, they often neglect the strategic dynamics involved in the selection process. This oversight creates opportunities for partisan actors to cherry-pick maps that, while technically compliant, are politically advantageous. Simply satisfying formal constraints does not ensure fairness when the selection process itself can be manipulated. We propose Agentmandering, a framework that reimagines redistricting as a turn-based negotiation between two agents representing opposing political interests. Drawing inspiration from game-theoretic ideas, particularly the Choose-and-Freeze protocol, our method embeds strategic interaction into the redistricting process via large language model (LLM) agents. Agents alternate between selecting and freezing districts from a small set of candidate maps, gradually partitioning the state through constrained and interpretable choices. Evaluation on post-2020 U.S. Census data across all states shows that Agentmandering significantly reduces partisan bias and unfairness, while achieving 2 to 3 orders of magnitude lower variance than standard baselines. These results demonstrate both fairness and stability, especially in swing-state scenarios.