{ "count": 50, "items": [ { "id": 20187, "url": "https://www.engadget.com/2237637/stripe-reportedly-to-buy-paypal", "title": "Stripe is reportedly in talks to buy PayPal", "summary": "Stripe and private equity firm Advent previously offered a deal PayPal didn't take, the Wall Street Journal says. Now, they're negotiating a new price.", "authors": "staff@engadget.com (Mariella Moon)", "category": "news", "topics": "bias-fairness,finance-investment", "published_at": "2026-08-15T10:13:09.000Z", "source": "Engadget AI", "ethics_ai_record_url": "https://ethics.ai/record/20187" }, { "id": 19513, "url": "https://techcrunch.com/video/does-mark-zuckerberg-really-believe-ai-is-for-everyone", "title": "Does Mark Zuckerberg really believe AI is ‘for everyone’?", "summary": "Meta released Glimmer this week, an open-weight AI model anyone can download and run on their own hardware — a contrast to Muse Spark, the company’s more powerful model that stays locked behind its own APIs. The release landed alongside a letter from Mark Zuckerberg arguing AI should be “for everyone” rather than controlled by a handful of labs, but as Equity’s […]", "authors": "Theresa Loconsolo", "category": "news", "topics": "bias-fairness", "published_at": "2026-08-14T15:43:28.000Z", "source": "TechCrunch", "ethics_ai_record_url": "https://ethics.ai/record/19513" }, { "id": 19687, "url": "https://www.bloomberg.com/news/articles/2026-08-14/jane-street-took-15-billion-loss-in-july-as-ai-stocks-slumped", "title": "Jane Street Lost $15 Billion in Its First Down Month in a Decade", "summary": "Jane Street posted $15 billion of losses last month as AI-focused hedge fund Situational Awareness swooned and dragged down asset prices across equity markets, according to a person familiar with the ...", "authors": null, "category": "news", "topics": "bias-fairness,finance-investment", "published_at": "2026-08-14T15:02:00.000Z", "source": "Bloomberg Technology", "ethics_ai_record_url": "https://ethics.ai/record/19687" }, { "id": 19515, "url": "https://techcrunch.com/podcast/metas-open-ai-and-a-250m-deal-gone-very-wrong", "title": "Meta’s ‘open’ AI, and a $250M deal gone very wrong", "summary": "Meta released Glimmer this week, an open-weight AI model anyone can download and run on their own hardware — a contrast to Muse Spark, the company’s more powerful model that stays locked behind its own APIs. The release landed alongside a letter from Mark Zuckerberg arguing AI should be “for everyone” rather than controlled by a handful of labs, but as Equity’s […]", "authors": "Theresa Loconsolo, Kirsten Korosec, Anthony Ha, Rebecca Bellan", "category": "news", "topics": "bias-fairness", "published_at": "2026-08-14T14:00:00.000Z", "source": "TechCrunch", "ethics_ai_record_url": "https://ethics.ai/record/19515" }, { "id": 19116, "url": "https://arxiv.org/abs/2608.12669", "title": "From Fair Representation to Just Recognition in Generative AI", "summary": "arXiv:2608.12669v1 Announce Type: new Abstract: The fair AI/ML literature has long distinguished distributive fairness, concerning how automated systems allocate resources and opportunities, from representational fairness, concerning how they shape the ways individuals and social groups are perceived, understood, and accorded social status. Generative AI is rebalancing these normative dimensions. Unlike predictive systems, large language models (LLMs) and related technologies are fundamentally e", "authors": "Severin Engelmann, Daniel Susser", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-14T04:00:00.000Z", "source": "arXiv cs.CY", "ethics_ai_record_url": "https://ethics.ai/record/19116" }, { "id": 19119, "url": "https://arxiv.org/abs/2608.13022", "title": "Applied and Filtered: An End-to-End Algorithmic Fairness Audit of A Public Employment Agency", "summary": "arXiv:2608.13022v1 Announce Type: new Abstract: Algorithmic fairness evaluation commonly assesses AI systems as bounded technical components, abstracting away the organizational context in which they operate. We present, to our knowledge, the first independent end-to-end fairness audit of a semi-automated hiring system operated by Barcelona Activa, a public employment agency using the third-party TalentClue platform for candidate search and shortlisting. We analyze approximately 497,000 candidat", "authors": "Gemma Gald\\'on-Clavell", "category": "research", "topics": "bias-fairness,jobs-economy,transparency", "published_at": "2026-08-14T04:00:00.000Z", "source": "arXiv cs.CY", "ethics_ai_record_url": "https://ethics.ai/record/19119" }, { "id": 19121, "url": "https://arxiv.org/abs/2608.13444", "title": "Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements", "summary": "arXiv:2608.13444v1 Announce Type: new Abstract: Machine learning ethics researchers and critical HCI scholars have argued that algorithmically predicting gender is wrong. At the same time, other researchers rely on predicted gender labels to study gender disparities and develop algorithmic fairness techniques. How do we reconcile these two seemingly contradictory intuitions? We differentiate two ways gender prediction may be wrong: being illegitimate, thereby contributing to harm; and being inva", "authors": "Evan Dong, Angelina Wang", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-14T04:00:00.000Z", "source": "arXiv cs.CY", "ethics_ai_record_url": "https://ethics.ai/record/19121" }, { "id": 19130, "url": "https://arxiv.org/abs/2606.18479", "title": "The Illusion of Improvement: Reject Inference Strategies in Credit Scoring", "summary": "arXiv:2606.18479v2 Announce Type: replace-cross Abstract: Reject inference methods are widely used to mitigate survival bias in credit scoring, yet their effectiveness remains poorly understood. We systematically evaluate several such methods and uncover a structural failure mode: in a natural retraining cycle, models whose accuracy improves while recall collapses create an illusion of improvement that leads practitioners to believe the system is getting better when, in fact, its rejection quali", "authors": "Bruno Scarone, Ricardo Baeza-Yates", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-14T04:00:00.000Z", "source": "arXiv cs.CY", "ethics_ai_record_url": "https://ethics.ai/record/19130" }, { "id": 19549, "url": "https://link.springer.com/article/10.1007/s11948-026-00620-0", "title": "Language Models as a Challenge for Business Ethics – A Partially Open-Source Approach", "summary": "Large language models have become central infrastructures of contemporary digital economies while raising persistent ethical concerns regarding linguistic inequality, opacity, data governance, and the concentration of technological power. Much of the current debate on AI ethics focuses on normative principles such as fairness, transparency, and accountability. While these principles remain essential, they often do not sufficiently explain why ethically problematic outcomes persist under competit", "authors": null, "category": "research", "topics": "bias-fairness,regulation,transparency", "published_at": "2026-08-14T00:00:00.000Z", "source": "Science and Engineering Ethics", "ethics_ai_record_url": "https://ethics.ai/record/19549" }, { "id": 19554, "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1817529", "title": "Reassessing demographic bias in face attribute classification: a statistically grounded multi-model evaluation on FairFace and UTKFace", "summary": "Face analysis systems are widely used in security, authentication, and public-sector applications; however, demographic bias and the statistical reliability of reported performance remain key concerns. Many studies rely on aggregate accuracy without quantifying subgroup disparities or uncertainty, potentially overstating model fairness. This study presents a statistically grounded evaluation of demographic bias in face attribute classification across three representative architectures, ResNet50,", "authors": "Andisani Nemavhola", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-14T00:00:00.000Z", "source": "Frontiers in Artificial Intelligence", "ethics_ai_record_url": "https://ethics.ai/record/19554" }, { "id": 19164, "url": "https://arxiv.org/abs/2608.13444v1", "title": "Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements", "summary": "Machine learning ethics researchers and critical HCI scholars have argued that algorithmically predicting gender is wrong. At the same time, other researchers rely on predicted gender labels to study gender disparities and develop algorithmic fairness techniques. How do we reconcile these two seemingly contradictory intuitions? We differentiate two ways gender prediction may be wrong: being illegitimate, thereby contributing to harm; and being invalid, thereby producing unusable measurements. Ou", "authors": "Evan Dong, Angelina Wang", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-13T16:30:47.000Z", "source": "arXiv", "ethics_ai_record_url": "https://ethics.ai/record/19164" }, { "id": 19445, "url": "https://arxiv.org/abs/2608.13368v1", "title": "Sign Language Video Synthesis via Loss-Guided Multi-Expert GANs", "summary": "This preliminary technical report presents a framework for sign language video synthesis using a loss-guided multi-expert Generative Adversarial Network (GAN) to enhance communication for individuals with hearing impairments. Three specialized discriminators -- global, hand, and head -- each guide a corresponding expert branch in the generator toward a distinct visual region, enabling implicit feature specialization without explicit diversity losses. To stabilize this multi-discriminator system,", "authors": "Dingzhan Nong, Zhihao Ren, Ziqi Li, Tim Lo", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-13T15:32:40.000Z", "source": "arXiv cs.AI", "ethics_ai_record_url": "https://ethics.ai/record/19445" }, { "id": 19446, "url": "https://arxiv.org/abs/2608.13328v1", "title": "It's How You Ask: Gender-Associated Linguistic Bias in LLMs", "summary": "Professional communication is increasingly mediated by LLMs - but do these models serve all users equally? We show that when prompts contain linguistic features more commonly used by women (hedges, tag questions, collective reference), they systematically elicit shorter, less sophisticated, and less formal responses across three document types and four models. These effects persist after controlling for prompt complexity and feature carry-over. Explicit gender cues like sign-off names are encode", "authors": "Katherine Van Koevering, Anjalie Field", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-13T14:54:23.000Z", "source": "arXiv cs.AI", "ethics_ai_record_url": "https://ethics.ai/record/19446" }, { "id": 19465, "url": "https://www.medianama.com/2026/08/223-jio-financial-49-9-stake-nbfc-bank-of-america", "title": "Jio Financial to sell 49.9% stake in NBFC arm to Bank of America for $1.9 billion", "summary": "Jio Financial Services and Bank of America has signed an agreement which lets BofA acquire 49.9% stake in its NBFC subsidiary Jio Credit through a preferential allotment of equity shares and warrants. The post Jio Financial to sell 49.9% stake in NBFC arm to Bank of America for $1.9 billion appeared first on MEDIANAMA .", "authors": "Amit Singh", "category": "news", "topics": "bias-fairness", "published_at": "2026-08-13T09:52:01.000Z", "source": "MediaNama (IN)", "ethics_ai_record_url": "https://ethics.ai/record/19465" }, { "id": 19185, "url": "https://arxiv.org/abs/2608.13022v1", "title": "Applied and Filtered: An End-to-End Algorithmic Fairness Audit of A Public Employment Agency", "summary": "Algorithmic fairness evaluation commonly assesses AI systems as bounded technical components, abstracting away the organizational context in which they operate. We present, to our knowledge, the first independent end-to-end fairness audit of a semi-automated hiring system operated by Barcelona Activa, a public employment agency using the third-party TalentClue platform for candidate search and shortlisting. We analyze approximately 497,000 candidate-vacancy pipeline entries from September 2017 t", "authors": "Gemma Galdón-Clavell", "category": "research", "topics": "bias-fairness,jobs-economy,transparency", "published_at": "2026-08-13T09:46:27.000Z", "source": "arXiv", "ethics_ai_record_url": "https://ethics.ai/record/19185" }, { "id": 19475, "url": "https://arxiv.org/abs/2608.12957v1", "title": "I-SDPO: Instance-Level Adaptive Self-Distillation Policy Optimization", "summary": "Group Relative Policy Optimization (GRPO) learns from reward differences within a rollout group, but receives no useful relative signal when every sampled response is incorrect. Privileged self-distillation can fill this gap with dense token supervision, yet applying it throughout training creates a different failure mode: the teacher is a biased, low-variance surrogate for the reward objective, so persistent imitation can oppose reward-improving updates after the policy becomes capable of produ", "authors": "Yubo Zhang, Xinhong Ma, Zezhong Tan, Ziqiang Dong", "category": "research", "topics": "bias-fairness,regulation", "published_at": "2026-08-13T08:37:24.000Z", "source": "arXiv cs.LG", "ethics_ai_record_url": "https://ethics.ai/record/19475" }, { "id": 19406, "url": "https://techcabal.com/2026/08/13/techcabal-daily-jumia-bags-50-million", "title": "👨🏿‍🚀TechCabal Daily – Jumia bags $50 million", "summary": "In today's edition: Virtual asset firms to join CBN’s sandbox || Jumia secures $50 million equity funding || Shoprite’s Sixty60 is having a moment || Vodacom taps ex-Airtel CEO to join board", "authors": "Emmanuel Nwosu", "category": "news", "topics": "bias-fairness", "published_at": "2026-08-13T05:57:03.000Z", "source": "TechCabal (Africa)", "ethics_ai_record_url": "https://ethics.ai/record/19406" }, { "id": 19189, "url": "https://arxiv.org/abs/2608.12845v1", "title": "FSGR: Mitigating Token Frequency Bias for Fair SID-Based Generative Recommendation", "summary": "Semantic ID (SID)-based generative recommendation has recently achieved remarkable success. However, existing methods suffer from a previously overlooked fairness issue, which we term \\textbf{Token Frequency Bias}, where high-frequency SID tokens are systematically over-predicted while low-frequency SID tokens are under-predicted. This bias originates from the combined effects of imbalanced semantic codebooks during SID construction, and popularity bias together with the maximum likelihood estim", "authors": "Yuchen Zheng, Sihan Xu, Jingwen Yang, Xiangrui Cai, Haiwei Zhang, Xiaojie Yuan", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-13T05:34:51.000Z", "source": "arXiv", "ethics_ai_record_url": "https://ethics.ai/record/19189" }, { "id": 18890, "url": "https://www.ft.com/content/aa0ca006-5af4-4f94-9789-3e56f9f53e1d?syn-25a6b1a6=1", "title": "Wealth managers cut fees to win AI’s paper millionaires", "summary": "The rise of equity-rich tech workers at Anthropic and OpenAI is shifting negotiating power towards clients", "authors": null, "category": "news", "topics": "bias-fairness", "published_at": "2026-08-13T04:00:04.000Z", "source": "Financial Times Technology (headlines)", "ethics_ai_record_url": "https://ethics.ai/record/18890" }, { "id": 18711, "url": "https://arxiv.org/abs/2608.11251", "title": "Variable Selection in the Context of AI Fairness", "summary": "arXiv:2608.11251v1 Announce Type: new Abstract: Fairness in AI systems has become more important with recent regulatory demands, such as the EU AI Act. Traditional approaches often do not take into account philosophical ethics and social awareness. Variable selection processes, in particular, can introduce implicit bias, affecting equity across different subgroups. We discuss a mathematical approach that evaluates fairness in AI, aligning mathematical methodologies with ethical considerations an", "authors": "Ivan Luciano Danesi, Chiara Frigerio, Fabio Maccaferri, Giorgio Alessandro Motta, Pietro Zecca", "category": "research", "topics": "bias-fairness,regulation", "published_at": "2026-08-13T04:00:00.000Z", "source": "arXiv cs.CY", "ethics_ai_record_url": "https://ethics.ai/record/18711" }, { "id": 18714, "url": "https://arxiv.org/abs/2608.11491", "title": "The Accuracy Trap: Structural Scarcity Amplifies Relative Inequality in Algorithmic Allocation", "summary": "arXiv:2608.11491v1 Announce Type: new Abstract: Algorithmic systems increasingly rank individuals for access to scarce public resources, from child welfare interventions to cancer treatment referrals. The prevailing fairness frame treats disparity as a property of biased data or deficient models, with remedies through calibration and debiasing. Under structural scarcity, where demand exceeds supply by an order of magnitude, allocation becomes a rationing problem, and the statistical properties o", "authors": "Erina Seh-Young Moon, Matthew Tamura, Shion Guha", "category": "research", "topics": "bias-fairness,children-education", "published_at": "2026-08-13T04:00:00.000Z", "source": "arXiv cs.CY", "ethics_ai_record_url": "https://ethics.ai/record/18714" }, { "id": 18731, "url": "https://arxiv.org/abs/2508.05849", "title": "Public support for misinformation interventions depends on perceived fairness, effectiveness, and intrusiveness", "summary": "arXiv:2508.05849v3 Announce Type: replace Abstract: The proliferation of misinformation on social media has concerning possible consequences, such as the degradation of democratic norms. While recent research on countering misinformation has largely focused on analyzing the effectiveness of interventions, the factors associated with public support for these interventions have received little attention. We asked 1,010 American social media users to rate their support for and perceptions of ten mi", "authors": "Catherine King, Samantha C. Phillips, Kathleen M. Carley", "category": "research", "topics": "bias-fairness,misinformation", "published_at": "2026-08-13T04:00:00.000Z", "source": "arXiv cs.CY", "ethics_ai_record_url": "https://ethics.ai/record/18731" }, { "id": 18733, "url": "https://arxiv.org/abs/2509.15122", "title": "Prestige over merit: An adapted audit of LLM bias in peer review", "summary": "arXiv:2509.15122v2 Announce Type: replace Abstract: Large language models (LLMs) play a growing but largely informal role in scholarly peer review. Yet whether LLMs reproduce biases observed in human decision-making remains unclear. We adapt a resume-style audit to scientific publishing, developing a multi-role LLM simulation (editor/reviewer) that evaluates high-quality manuscripts across the physical, biological, and social sciences under randomized author identities (institutional prestige, g", "authors": "Anthony Howell, Jieshu Wang, Luyu Du, Julia Melkers, Varshil Shah", "category": "research", "topics": "bias-fairness,transparency,biotech", "published_at": "2026-08-13T04:00:00.000Z", "source": "arXiv cs.CY", "ethics_ai_record_url": "https://ethics.ai/record/18733" }, { "id": 19243, "url": "https://link.springer.com/article/10.1007/s00146-026-03229-w", "title": "Designing for rhythm: tempo-setting infrastructures and the temporal conditions of digital life", "summary": "Digital systems increasingly function as tempo-setting infrastructures that organize the temporal conditions under which cognition, communication, learning, and participation occur. Although research in human-computer interaction, platform studies, and AI ethics has extensively examined privacy, fairness, transparency, engagement, and well-being, the temporal organization of digital life has received comparatively little attention as a distinct object of sociotechnical analysis. We argue that di", "authors": null, "category": "research", "topics": "bias-fairness,privacy-surveillance,transparency", "published_at": "2026-08-13T00:00:00.000Z", "source": "AI & Society", "ethics_ai_record_url": "https://ethics.ai/record/19243" }, { "id": 19039, "url": "https://arxiv.org/abs/2608.12078v1", "title": "Better Slots, Better Worlds: Representation Quality & Robustness in Object-Centric World Models", "summary": "Learning world models from offline trajectories enables agents to accomplish different tasks through planning. Object-centric (OC) representations, which decompose a scene into a set of slots that bind to its objects, have been proposed as an inductive bias for world models that are more sample-efficient and generalize better. Yet prior object-centric world models (OCWMs) take the slot encoder as given and evaluate only in-distribution, leaving open whether the object-centric bias actually deliv", "authors": "Shukrullo Nazirjonov, Sai Prasanna, Anna Manasyan, Georg Martius", "category": "research", "topics": "bias-fairness,agents-autonomy", "published_at": "2026-08-12T14:02:36.000Z", "source": "arXiv cs.AI", "ethics_ai_record_url": "https://ethics.ai/record/19039" }, { "id": 18472, "url": "https://www.sciencedirect.com/science/article/pii/S2666920X26001268?dgcid=rss_sd_all", "title": "Neuro-symbolic pedagogical alignment (NSPA) for long-horizon classroom discourse analysis: Mitigating dialect bias via counterfactual preference optimization", "summary": "Publication date: Available online 10 August 2026 Source: Computers and Education: Artificial Intelligence Author(s): Qianyi Fang, Wenhe Liu", "authors": null, "category": "research", "topics": "bias-fairness,safety-alignment,children-education", "published_at": "2026-08-12T05:10:43.828Z", "source": "Computers and Education: Artificial Intelligence", "ethics_ai_record_url": "https://ethics.ai/record/18472" }, { "id": 18361, "url": "https://arxiv.org/abs/2608.10089", "title": "Status Association Does Not Reliably Predict Decision Leakage", "summary": "arXiv:2608.10089v1 Announce Type: cross Abstract: Bias evaluations often move too quickly from evidence that a model encodes a social association to claims that the same association will alter consequential decisions. We test whether that inference is warranted using Chilean surnames as controlled socioeconomic probes. We evaluate eight frozen model-provider cells on 1,032 prompts each, yielding 8,256 verified primary responses. The design separates forced latent association from matched consequ", "authors": "Abdullah X", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-12T04:00:00.000Z", "source": "arXiv cs.CY", "ethics_ai_record_url": "https://ethics.ai/record/18361" }, { "id": 18371, "url": "https://arxiv.org/abs/2606.07270", "title": "Two-Phase Simulated Annealing for Equitable Team Formation: Eliminating Complaints in Large Engineering Cohorts", "summary": "arXiv:2606.07270v2 Announce Type: replace Abstract: Contribution: This paper presents a novel two-phase algorithmic approach that decouples preference satisfaction from fairness optimization in student team formation, achieving both objectives without compromise. The method applies simulated annealing -- a core materials science technique -- to an educational challenge, demonstrating pedagogical integration of administrative processes. Background: Forming effective teams in large engineering coh", "authors": "Yiwei Sun, Xinru Deng, Dimitrios G Papageorgiou", "category": "research", "topics": "bias-fairness,children-education", "published_at": "2026-08-12T04:00:00.000Z", "source": "arXiv cs.CY", "ethics_ai_record_url": "https://ethics.ai/record/18371" }, { "id": 19058, "url": "https://arxiv.org/abs/2608.11495v1", "title": "Defending against Model Extraction for GNNs with Model Reprogramming", "summary": "Graph Neural Networks (GNNs) serve as the backbone for high-stakes applications in Machine-Learning-as-a-Service (MLaaS). Still, their black-box deployment exposes them to Model Extraction (ME) attacks, in which adversaries steal intellectual property by querying APIs. Existing defenses suffer from a critical ''Euclidean bias'': they transfer image-based strategies (e.g., random noise) to graphs, ignoring the complex topological dependencies between nodes, which often results in severe utility d", "authors": "Yan Wen, Zhenyi Wang, Heng Huang", "category": "research", "topics": "bias-fairness,copyright-ip", "published_at": "2026-08-11T23:20:15.000Z", "source": "arXiv cs.CR (AI security)", "ethics_ai_record_url": "https://ethics.ai/record/19058" }, { "id": 18806, "url": "https://arxiv.org/abs/2608.11491v1", "title": "The Accuracy Trap: Structural Scarcity Amplifies Relative Inequality in Algorithmic Allocation", "summary": "Algorithmic systems increasingly rank individuals for access to scarce public resources, from child welfare interventions to cancer treatment referrals. The prevailing fairness frame treats disparity as a property of biased data or deficient models, with remedies through calibration and debiasing. Under structural scarcity, where demand exceeds supply by an order of magnitude, allocation becomes a rationing problem, and the statistical properties of ranking diverge sharply from those of classifi", "authors": "Erina Seh-Young Moon, Matthew Tamura, Shion Guha", "category": "research", "topics": "bias-fairness,children-education", "published_at": "2026-08-11T23:04:43.000Z", "source": "arXiv", "ethics_ai_record_url": "https://ethics.ai/record/18806" }, { "id": 18486, "url": "https://www.jmir.org/2026/1/e92842", "title": "Prioritizing Equity in Design and Implementation of Consumer-Facing Digital Resources to Support Engagement and Shared Decision-Making", "summary": "A digitally enabled health system offers the opportunity to address gaps in the implementation of shared decision-making, a collaborative process between health professionals and consumers to decide on the best test, treatment, or management option based on clinical evidence and the consumer’s values and informed preferences. There is increasing design and availability of digital tools online to support shared decision-making. Providing opportunities for all consumers to make shared health care", "authors": "Jenna Smith, Julie Ayre, Carissa Bonner, Danielle Muscat, Heather L Shepherd, Eva Hussain, Husna Amani, Marguerite Tracy, Kristie R Weir, Kathleen McFadden, Kirsten J McCaffery, Jolyn Hersch", "category": "research", "topics": "bias-fairness,healthcare", "published_at": "2026-08-11T21:00:23.000Z", "source": "JMIR (Journal of Medical Internet Research)", "ethics_ai_record_url": "https://ethics.ai/record/18486" }, { "id": 18515, "url": "https://www.cnbc.com/2026/08/11/wall-street-endorsed-jensen-huangs-big-concept-for-ai-what-now.html", "title": "Wall Street just endorsed Jensen Huang's 'big concept' for AI. What now?", "summary": "The first three-plus years of the AI build-out have been funded by record amounts of equity and debt issued by leading tech companies. Nvidia has a new idea.", "authors": null, "category": "news", "topics": "bias-fairness", "published_at": "2026-08-11T14:11:05.000Z", "source": "CNBC Technology", "ethics_ai_record_url": "https://ethics.ai/record/18515" }, { "id": 18696, "url": "https://arxiv.org/abs/2608.10634v1", "title": "IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning", "summary": "Model-based reinforcement learning (MBRL), which learns environment dynamics to generate synthetic experience, is a promising approach to sample-efficient decision making. Numerous methods have been developed to improve dynamics prediction and policy optimization for MBRL through uncertainty estimation, model regularization, and conservative value learning. However, these methods typically treat the transition model and critic as monolithic predictors, overlooking the policy-induced data bias. C", "authors": "Zefeng Liang, Jie Qiao, Ruichu Cai, Weilin Chen, Zhifeng Hao", "category": "research", "topics": "bias-fairness,regulation,environment", "published_at": "2026-08-11T08:20:02.000Z", "source": "arXiv cs.LG", "ethics_ai_record_url": "https://ethics.ai/record/18696" }, { "id": 18669, "url": "https://arxiv.org/abs/2608.10474v1", "title": "Stay or Stray - A Dynamical Systems Viewpoint of Popularity Bias", "summary": "Popularity bias in recommendation systems arises when a majority user class generates disproportionate interaction data, causing the system to increasingly favour it while degrading recommendation quality for niche users. While extensive empirical evidence of popularity bias exists, the dynamics leading to its emergence are not well understood. In this work, we study the coupled evolution of recommender model updates and user engagement through the lens of dynamical systems. We formulate a stoch", "authors": "Sarvesh Shashidhar, Lankireddy Prabhat, Arpit Agarwal, D. Manjunath, Karan Bhukar, Tanmay Khandelwal", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-11T04:41:24.000Z", "source": "arXiv cs.HC", "ethics_ai_record_url": "https://ethics.ai/record/18669" }, { "id": 18178, "url": "https://abovethelaw.com/2026/08/biglaw-flirts-with-private-equity-because-ruining-everything-else-wasnt-enough-see-also", "title": "Biglaw Flirts With Private Equity, Because Ruining Everything Else Wasn’t Enough — See Also", "summary": "Biglaw Wants Private Equity's Money -- Ask Your Vet How That Turned Out: The same 'management services' trick that swallowed your dog's clinic is now knocking on Paul Weiss's door. That's Some Catch, That Catch-22 : The administration may talk like the SPLC case is a vindictive prosecution, but this Trump judge says there's no way to know for sure without discovery. Also, she will not allow discovery . Ballroom Blitzed : D.C. Circuit judges block the Trump bunker ballroom. The Senate Confirmed T", "authors": "Above the Law", "category": "news", "topics": "bias-fairness,finance-investment", "published_at": "2026-08-10T22:56:00.000Z", "source": "Above the Law (legal tech)", "ethics_ai_record_url": "https://ethics.ai/record/18178" }, { "id": 18229, "url": "https://cyberscoop.com/ftc-regulating-ai-ideological-bias", "title": "The FTC wants to regulate AI for ideological bias", "summary": "The commission is mulling whether to begin regulating bias in AI systems. Critics say they’re overstepping their legal authority and infringing on free speech. The post The FTC wants to regulate AI for ideological bias appeared first on CyberScoop .", "authors": "djohnson", "category": "news", "topics": "bias-fairness,regulation", "published_at": "2026-08-10T21:20:50.000Z", "source": "CyberScoop", "ethics_ai_record_url": "https://ethics.ai/record/18229" }, { "id": 18681, "url": "https://arxiv.org/abs/2608.10126v1", "title": "Procedural Fairness Failures in RLHF from Preference Averaging", "summary": "Reinforcement Learning from Human Feedback (RLHF) aggregates heterogeneous preferences into a single reward model, assuming preference homogeneity. When preferences are heterogeneous, this aggregation induces a procedural fairness failure where majority preference groups dominate reward learning while minority preferences are systematically under-represented. This work defines procedural fairness in alignment as preserving distinct preference signals during reward modeling and shows that standar", "authors": "M P V S Gopinadh, Karthik Kamuju, Kummari Avinash, John Joshua, Srinivasa Raju Rudraraju", "category": "research", "topics": "bias-fairness,safety-alignment", "published_at": "2026-08-10T18:38:16.000Z", "source": "arXiv fairness query", "ethics_ai_record_url": "https://ethics.ai/record/18681" }, { "id": 18294, "url": "https://arxiv.org/abs/2608.09899v1", "title": "Fairness in Link Prediction Beyond Demographic Parity: A Reproducibility Study", "summary": "In fair ranked link prediction, demographic parity ($Δ_\\mathrm{DP}$) is a common fairness metric. Yet, Mattos et al. (2025) argue that it fails to detect exposure bias because it ignores where links appear in the ranking. In this study, we reproduce this claim by showing that $Δ_\\mathrm{DP}$ can indicate aggregate parity even when some subgroup-pair links are systematically ranked lower than others. The proposed rank-aware Normalized Discounted KL-divergence (NDKL), however, does detect such dis", "authors": "Valentijn Oldenburg, Floris de Kam, Stef de Wildt, Jarno Nilson Balk", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-10T17:47:19.000Z", "source": "arXiv fairness query", "ethics_ai_record_url": "https://ethics.ai/record/18294" }, { "id": 17999, "url": "https://arxiv.org/abs/2608.09857v1", "title": "Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy", "summary": "Advances in advanced artificial intelligence tools have sparked research in robot autonomy, but the development of such systems has largely focused on execution rather than verifying the feasibility actions planning models propose. Like general-purpose LLMs, robotics planning models carry risks: biased toward user-specified goals, they may suggest actions misaligned with scientific ethics, they may be unsafe due to an inability to \"remember\" prior safety risks, or they may be vulnerable to adver", "authors": "Rohan Bhagra, Mahantesh Halapannavar, Uddhav Bhattarai", "category": "research", "topics": "bias-fairness,safety-alignment,agents-autonomy", "published_at": "2026-08-10T17:15:55.000Z", "source": "arXiv", "ethics_ai_record_url": "https://ethics.ai/record/17999" }, { "id": 18186, "url": "https://abovethelaw.com/2026/08/the-richest-law-firms-are-looking-at-private-equity-cash-because-i-guess-they-dont-have-enough-money", "title": "The Richest Law Firms Are Looking At Private Equity Cash Because I Guess They Don’t Have Enough Money", "summary": "Biglaw is looking at the playbook that killed endless shrimp. The post The Richest Law Firms Are Looking At Private Equity Cash Because I Guess They Don’t Have Enough Money appeared first on Above the Law .", "authors": "Kathryn Rubino", "category": "news", "topics": "bias-fairness,regulation,finance-investment", "published_at": "2026-08-10T17:01:00.000Z", "source": "Above the Law (legal tech)", "ethics_ai_record_url": "https://ethics.ai/record/18186" }, { "id": 18264, "url": "https://arxiv.org/abs/2608.09688v1", "title": "Confusion-Geometry Rebalancing for Long-Tailed Adversarial Training", "summary": "Adversarial training under long tailed distributions suffers from a dual imbalance: the class imbalance skews the training objective toward head classes, and the adversarial inner maximization may further amplify this bias. Existing methods mitigate this issue by correcting class priors or adapting class wise robust supervision, yet they treat each class in isolation and fail to identify which boundaries drive long tailed collapse. We propose a Confusion Geometry Rebalancing method (CGRm) for lo", "authors": "Mengnan Zhao, Geyong Min, Lihe Zhang, Tianhang Zheng, Jie Cui", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-10T14:54:00.000Z", "source": "arXiv cs.AI", "ethics_ai_record_url": "https://ethics.ai/record/18264" }, { "id": 18103, "url": "https://www.fastcompany.com/91585684/exclusive-inkind-414-million-citi-independent-restaurants-financing?partner=rss&utm_source=rss&utm_medium=feed&utm_campaign=rss+fastcompany&utm_content=rss", "title": "Exclusive: InKind lands $414 million led by Citi to dominate how independent restaurants raise money", "summary": "Finance platform InKind has raised more than $1 billion in the past six months to invest in an industry many traditional lenders have long considered too risky: independent restaurants.  Today, InKind announced $414 million in financing in a round led by Citi and Cross River. The latest funding comes just one month after InKind secured $320 million from Liberty Mutual Investments and six months after announcing a $450 million round of debt and equity funding led by Magnetar, an Illinois-bas", "authors": "Kristen Hawley", "category": "news", "topics": "bias-fairness,finance-investment", "published_at": "2026-08-10T13:00:00.000Z", "source": "Fast Company Tech", "ethics_ai_record_url": "https://ethics.ai/record/18103" }, { "id": 18295, "url": "https://arxiv.org/abs/2608.09366v1", "title": "Beyond Binary: Continuous State Optimization with Graph-Structured Objectives", "summary": "Large-scale learning systems often face the challenge of balancing multiple, potentially competing objectives, such as fairness, accuracy, and latency. While recent work has formalized this as an optimization problem over binary states, many real-world control parameters, such as fairness thresholds, diversity mixing rates, or resource budgets, are continuous. In this work, we extend the framework to \\emph{continuous state spaces}. We model the problem as minimizing a sum of linear objectives su", "authors": "Corinna Cortes, Yishay Mansour, Mehryar Mohri", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-10T09:46:28.000Z", "source": "arXiv fairness query", "ethics_ai_record_url": "https://ethics.ai/record/18295" }, { "id": 18024, "url": "https://arxiv.org/abs/2608.09221v1", "title": "FedTVD: Balancing Data Quality and Quantity for Robust Federated Learning", "summary": "Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy. However, FL faces significant challenges due to data heterogeneity, particularly in terms of label distribution skewness and variations in dataset sizes, which can lead to biased model updates and hinder convergence. To address this, we propose FedTVD, a novel FL algorithm that weights client contributions during aggregation by considering both data quality and quantity.", "authors": "Radwan Selo, Majid Kundroo, Taehong Kim", "category": "research", "topics": "bias-fairness,privacy-surveillance", "published_at": "2026-08-10T07:45:25.000Z", "source": "arXiv", "ethics_ai_record_url": "https://ethics.ai/record/18024" }, { "id": 17762, "url": "https://arxiv.org/abs/2608.06908", "title": "Calibrating WEAT Against Anisotropy: ZCA Whitening as a Geometric Pre-Processing Step for Embedding Association Tests", "summary": "arXiv:2608.06908v1 Announce Type: cross Abstract: We propose Zero-phase Component Analysis (ZCA) whitening as a geometric pre-processing step for the Word Embedding Association Test (WEAT). WEAT is a bias measurement method widely used in both computational social science and AI fairness research. It relies on cosine similarity as a measure of semantic association, which assumes that the embedding space is approximately isotropic. However, prior work has reported that many widely used language m", "authors": "Seitaro Ono, Senna Ross, Jun Saiki", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-10T04:00:00.000Z", "source": "arXiv cs.CY", "ethics_ai_record_url": "https://ethics.ai/record/17762" }, { "id": 17763, "url": "https://arxiv.org/abs/2608.06955", "title": "Critical Acclaim Orientation in Large Language Models: Evidence from Film Preference Elicitation", "summary": "arXiv:2608.06955v1 Announce Type: cross Abstract: Large language models (LLMs) are trained on corpora that contain expressions of human judgment about films, books, music, and more. Yet whether LLMs systematically reproduce evaluative hierarchies remains unclear. Prior research on cultural bias in LLMs suggests competing expectations: models may mirror the popularity signals of internet texts, or may reproduce forms of prestige embedded in critical discourse. We probe this question through a stu", "authors": "Jonghyun Jee, Aaron Shaw", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-10T04:00:00.000Z", "source": "arXiv cs.CY", "ethics_ai_record_url": "https://ethics.ai/record/17763" }, { "id": 17769, "url": "https://arxiv.org/abs/2509.16462", "title": "Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs", "summary": "arXiv:2509.16462v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economic disparities. Although prior work has examined intrinsic representational bias and unfair downstream behavior separately, it remains unclear whether mitigating intrinsic bias leads to fairer downstream outcomes. We introduce Fairness-Aware Concept Unlearning (FACU), a model-level mitigation m", "authors": "Mina Arzaghi, Alireza Dehghanpour Farashah, Florian Carichon, Jean-Fran\\c{c}ois Plante, Golnoosh Farnadi", "category": "research", "topics": "bias-fairness", "published_at": "2026-08-10T04:00:00.000Z", "source": "arXiv cs.CY", "ethics_ai_record_url": "https://ethics.ai/record/17769" }, { "id": 18296, "url": "https://arxiv.org/abs/2608.09082v1", "title": "F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting", "summary": "Spatiotemporal prediction on graph-structured data is central to traffic forecasting and environmental monitoring, yet decentralized and heterogeneous data complicate both sequence modeling and collaborative training. We propose F$^2$STNet, a federated forecasting framework that combines truncated graph-Fourier features, a lightweight diagonal state-space temporal encoder, graph convolution, and Fairness-aware Federated Aggregation (FFA). The spectral branch exposes graph-frequency structure, wh", "authors": "Jiayi Zhang, Jinfeng Xu, Hewei Wang, Siyuan Cen, Haidong Huang, Yiyao Zhan, Zheyu Chen, Jinjiang You, Ai Jian, Edith C. H. Ngai", "category": "research", "topics": "bias-fairness,environment", "published_at": "2026-08-10T03:29:15.000Z", "source": "arXiv fairness query", "ethics_ai_record_url": "https://ethics.ai/record/18296" }, { "id": 18346, "url": "https://www.nature.com/articles/d41586-026-02276-z", "title": "This AI tool claims to pick the top 1% of preprints. Should researchers trust it?", "summary": "QED Science says that its metrics reduce bias by assessing papers solely on the basis of their originality and validity.", "authors": null, "category": "research", "topics": "bias-fairness", "published_at": "2026-08-09T17:00:00.000Z", "source": "Nature Machine Intelligence", "ethics_ai_record_url": "https://ethics.ai/record/18346" }, { "id": 17787, "url": "https://techcrunch.com/2026/08/09/historian-jill-lepore-says-the-tech-industry-is-led-by-bad-readers-who-are-undermining-democracy", "title": "Historian Jill Lepore says Silicon Valley misreads science fiction and undermines democracy", "summary": "On the latest episode of Equity, we spoke to Jill Lepore about \"government by machines\" and why Elon Musk is a bad science fiction reader.", "authors": "Anthony Ha", "category": "news", "topics": "bias-fairness", "published_at": "2026-08-09T15:00:00.000Z", "source": "TechCrunch", "ethics_ai_record_url": "https://ethics.ai/record/17787" } ], "attribution": "via ethics.ai" }