Latest AI Ethics & Fairness Research Papers
The newest AI Ethics & Fairness papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks AI Ethics & Fairness so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.
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- ETHICAL CONSIDERATIONS AND POLICY FRAMEWORKS FOR TEACHING WITH ARTIFICIAL INTELLIGENCE IN SECONDARY SCHOOL SETTINGSAsma Khalid,Dr. Naveed Azmat, Nafeesa Rani · Zenodo (CERN European Organ... · Dec 31, 2026
- Algorithmic Justice and Responsible AI Journalism: A Comparative Communication Policy Perspective in East AsiaRenxiudi Huang, Linjia Bai · PhilPapers (PhilPapers Foun... · Dec 31, 2026
As generative artificial intelligence and automated content curation rapidly reshape the global media landscape, the intersection of algorithmic justice and media governance has become a critical frontier for sustainable development. This s…
- ETHICAL CONSIDERATIONS AND POLICY FRAMEWORKS FOR TEACHING WITH ARTIFICIAL INTELLIGENCE IN SECONDARY SCHOOL SETTINGSAsma Khalid,Dr. Naveed Azmat, Nafeesa Rani · Zenodo (CERN European Organ... · Dec 31, 2026
- The Role of Generative Artificial Intelligence in Korean Language Learning: Applications, Challenges, and Instructional StrategiesYundong Wu · OpenAlex · Dec 31, 2026
This conceptual narrative review examines the role of generative artificial intelligence (AI) in Korean language learning. The paper synthesizes recent scholarship on AI-assisted language learning, Korean language education, technology acce…
- Fostering an enabling environment for health AI innovation and scale: The need for tailored ethics training for innovators in low- and middle-income countries[object Object], [object Object], [object Object], [object Object] et al. · AI Ethics 2026 · Dec 31, 2026
Artificial intelligence (AI) is a rapidly evolving technology with transformative potential across many fields, including health. Interest in AI is growing globally, including in low- and middle-income countries (LMICs), but few studies hav…
- Device-Induced Mobility Heterogeneity and Its Impact on Federated Next-Location PredictionAndré Felipe Zanella, Paula Delgado de Santos, José Suárez-Varela, David Solans Noguero et al. · arXiv · Oct 29, 2026
Predicting human mobility is central to network management, urban planning, and location-based services, yet privacy concerns are increasingly pushing such tasks toward federated learning. A key challenge in this setting is the strong heter…
- When the Top Answer Decides: Gender Bias in Decision ModelsYeb Havinga · arXiv · Oct 29, 2026
Decision models are language models built to route, classify and triage: they return option probabilities from one forward pass, and an application acts on the top answer. They are offered off the shelf, trained for the decision format rath…
- Data Provenance and Ethics in the Curricula of U.S. Health AI Education ProgramsRaffaele Giancotti, Francesca Santucci, Amama Anjum, Monique de Villa et al. · arXiv · Oct 29, 2026
Failures of deployed clinical artificial intelligence (AI) systems have often originated in how their training data were generated. Whether the programs that train the health AI workforce teach practitioners to examine data provenance is un…
- The Indian Financial Automated Advice Bias (IFAAB) Dataset : Auditing Socioeconomic and Demographic Disparities in LLM-Generated Financial Advice in IndiaKriti Mahajan · arXiv · Oct 28, 2026
Large language models are increasingly used for financial guidance, yet little is known about whether their advice changes across social identities in Global South contexts. We introduce the Indian Financial Automated Advice Bias (IFAAB) be…
- Responsible Quantum Machine Learning for Medical Imaging: A Fairness Audit Across Sensitive SubgroupsGiovanni dos Santos Franco, Felipe Mahlow, Ellison Fernando Cardoso, José Adenaldo Santos Bittencourt et al. · arXiv · Oct 27, 2026
Quantum machine learning (QML) is increasingly explored for medical classification, yet its behaviour under subgroup-aware fairness criteria remains understudied. We present an empirical fairness audit of classical and quantum classifiers f…
- No Tokenizer, Still a Gap: Cross-Lingual Fairness of Homoglyph and Invisible-Character WatermarkingAlexander Nemecek, Yuqiao Xu, Joseph Falkenburg, Erman Ayday · arXiv · Oct 27, 2026
Homoglyph and invisible-character watermarks are edit-based watermarks. Instead of changing how a model generates text, they hide a signal in the text after it is written. They swap some characters for homoglyphs, which look the same, or ve…
- Being judged through the record: A Rosenhan-inspired test of psychiatric label sensitivity in large language modelsShaked Rosenblum, Teddy Lazebnik, Ziv Ben-Zion · arXiv · Oct 26, 2026
The integration of large language models (LLMs) into clinical decision-making raises unresolved questions regarding diagnostic bias. We investigate whether a documented psychiatric history alters LLM recommendations when the current clinica…
- Learning by Collaborating in the Age of AI: Research Mentorship for Polymathic, Workflow-Centered ScienceTian Zheng · arXiv · Oct 25, 2026
Artificial intelligence (AI) is changing how data-driven research is produced. As AI tools make information and technical execution more accessible, mentorship must concentrate on judgment, ethics, and the design of end-to-end AI-augmented …
- AI Alignment for Digital Democracy: Fairer Voters Representation against LLM BiasesYi Qi, Srijoni Majumdar, Evangelos Pournaras · arXiv · Oct 23, 2026
AI representation is emerging in digital democracy, assisting in voter emulation and decision-making. Large language models (LLMs) behave differently depending on the model, the prompt context, and the voter information they receive, repres…
- Playing Bias: Epistemic Games for Interpreting Text-to-Image ModelsAmalia Foka · arXiv · Oct 23, 2026
Text-to-image (T2I) systems do not merely render prompts; they circulate defaults for "what X looks like," shaping recognition through pose, camera, costume, props, and setting. While technical audits quantify disparities, they rarely culti…
- VERA: A Responsible AI Dashboard for Assessing LLM Deployments in Light of the EU AI ActRobin Quériaux, Solveig Bachellery, Léa Déléris, Saurabhsingh Rajput et al. · arXiv · Oct 22, 2026
Institutions that deploy large language models in regulated settings often need technical evidence of responsible AI practices before approving a deployment. COMPL-AI provides a technical interpretation of the EU AI Act that organises this …
- What Lies Beyond Perception: A Partition Theory of the Limits of AI Evaluation and OversightAhmed M. Mahran · arXiv · Oct 22, 2026
Replacing the judge can move a benchmark score, and models can be trained to hide capabilities. Both put the evaluator in question: what can it certify? We develop a partition theory of evaluation from one principle: judgement is bounded by…
- Invisibilization as a Source of Probability Bias in Language ModelsChristian Arnold, Ernesto Calvo, Guadalupe Gonzalez, Sebastián Vallejo Vera et al. · arXiv · Oct 22, 2026
Language models are aligned to avoid using protected attributes such as race, ethnicity, and gender to inform their responses. Therefore, they increasingly refuse or hedge when such attributes appear in a prompt. However, if a system must d…
- Uncensored Is Not Unbiased: Abliteration Removes Refusals but Not Script-Dependent Bias About TaiwanJu-Chun Ko · arXiv · Oct 21, 2026
"Abliterated" ("uncensored") builds of open-weight language models remove the activation direction that mediates refusal, and are popular partly on the assumption that a model that no longer refuses is free of political censorship. We test …
- Uncensored Is Not Unbiased: Abliteration Removes Refusals but Not Script-Dependent Bias About TaiwanJu-Chun Ko · arXiv · Oct 21, 2026
"Abliterated" ("uncensored") builds of open-weight language models remove the activation direction that mediates refusal, and are popular partly on the assumption that a model that no longer refuses is also free of political censorship. We …
- Situating Vulnerability: An Ethical Governance Framework for Anthropomorphic AI InteractionHanhui Xu, Jiacheng Ji, Yuhan Wang, Haoan Jin et al. · arXiv · Oct 21, 2026
Large language models increasingly address users in the register of care: they remember personal details, adopt a first-person emotional voice, and sustain relationships across many sessions. This anthropomorphic mode of interaction generat…
- Situating Vulnerability: An Ethical Governance Framework for Anthropomorphic AI InteractionHanhui Xu, Jiacheng Ji, Yuhan Wang, Haoan Jin et al. · arXiv · Oct 21, 2026
Large language models increasingly address users in the register of care: they remember personal details, adopt a first-person emotional voice, and sustain relationships across many sessions. This anthropomorphic mode of interaction generat…
- Causal Humility: Governing the Authority of Fairness Claims Under Causal UncertaintyKhadija Zanna, Rodrigo Ferreira, Frederick L. Oswald, Akane Sano · arXiv · Oct 19, 2026
AI systems increasingly shape consequential decisions in employment, lending, healthcare, and public services, making fairness an important problem for auditing and governance. Fairness assessment often requires more than identifying dispar…
- Aligning Language Models with Indigenous Community Cosmovision through Constitutional AISilvia Fernández-Sabido, Jorge Reyes-Magaña, Jesús Adrián Chay Tun, Jean Carlos Buenfil et al. · arXiv · Oct 18, 2026
Large language models (LLMs) are predominantly trained on corpora that reflect Western, anglophone worldviews. This asymmetry is not neutral: when a model answers questions about territory, community authority, or the relationship with natu…
- Generative AI Produces and Transmits Beauty BiasAJ Alvero, Deepak Varuvel Dennison, Alexandra Crespin, Heeyon Kim et al. · arXiv · Oct 15, 2026
Societies reward people judged to be attractive by standards intertwined with gender and race. In this paper, we examine if and how generative AI may contribute to these dynamics. We audited 27 text-to-image generators released between 2022…
- Beyond What Is Visible: Context-Aware Sentiment Across Online Communities Among Crisis EventsZihui Ma, Yiheng Chen, Lingyao Li, Chen Chen et al. · arXiv · Oct 15, 2026
Social media offers timely insight into disaster experiences, yet online communities may not represent those affected on the ground. Uneven participation can bias crisis information systems toward data-rich locations and obscure communities…
- ChronoCulture: Evaluating Cultural Awareness Across Time in Large Language ModelsRusali Saha, Nitya Khurana, Ruihong Huang, Kuan-Hao Huang · arXiv · Oct 14, 2026
Cultural attitudes and social norms evolve over time, yet most cultural benchmarks for large language models (LLMs) treat culture as a static attribute of a country or region. We introduce ChronoCulture, a benchmark of 10,360 temporal pairs…
- Social Bias or Social Desirability? Comparing Factorial Survey Experiments for Humans and Large Language ModelsFranziska Weeber, Thomas Hinz, Konstantin Mozer, Sam Sievers et al. · arXiv · Oct 14, 2026
Factorial survey experiments are a survey method from the social sciences that uses descriptions of socially complex situation (``vignettes'') to elicit judgments such as social bias. They provide two major benefits: (a), they reduce the ef…
- Moral Combat: Exploiting Moral Foundations Theory to Jailbreak LLMsAdel Khorramrouz, Yi-Chia Wang, Sharon Levy · arXiv · Oct 14, 2026
Social psychology has long described how people can be persuaded to interpret a harmful act as moral. Recent research suggests that Large Language Models (LLMs) echo aspects of human psychology, from cognitive biases to social conformity. T…
- Fairness under Prediction Error in Predict-then-Optimize PipelinesHilda Adwubi Osei · arXiv · Oct 13, 2026
Fairness constraints in predict-then-optimize pipelines are necessarily enforced on predicted outcomes $\hat{y}$, since true outcomes $y$ are unobserved at decision time. We study whether a demographic parity constraint enforced this way co…