Bias & fairness

Logic and optimization can, in combination, make valuable contributions to rule-based AI. Logic is the obvious medium for encoding a rule base and drawing inferences from it, while optimization provides a powerful technology for computing inferences. Their combination has taken on new relevance amid a growing concern for transparency in AI. which is important for reproducibility, explainability, trustworthiness, and fairness. Rule-based AI provides a natural solution to transparency that is beco

Any new rule issued under current FCC Chairman Brendan Car would likely be more limited in scope, panelists said

CDT and the undersigned civil rights and civil society organizations write to you regarding the need for civil rights protections to be the foundation of any comprehensive artificial intelligence (AI) legislation. Specifically, we urge the House Democratic Commission on AI and the Innovation Economy (Commission) to support meaningful safeguards (mandatory bias testing, privacy and data […] The post Civil Rights Letter to the House Democratic AI Commission appeared first on Center for Democracy a

A federal judge struck down one part of the law standing up the program, which a government lawyer has said would allow the program to move forward

Online encyclopedias shape political opinion and, through it, democratic discourse. In late 2025, Grokipedia was released, an encyclopedia written entirely by the LLM Grok. One motivation behind the project was to provide an unbiased alternative to Wikipedia, which has faced accusations of "left-wing" and "liberal" bias. But does an encyclopedia written by an LLM deliver greater neutrality, or does it simply embed a different ideology? We conduct a large-scale political bias study on Grokipedia

Per-subgroup fairness audits of medical image classifiers face a sample-size problem: minority subgroups in held-out test sets have so few samples that the resulting confidence intervals on per-subgroup performance are wider than the bias the audit is meant to detect. We argue that a demographically-conditioned synthetic generator can do both: mitigate bias on the training side and detect bias on the evaluation side. Working on COVID-19 chest CT classification with an end-to-end fine-tuned Stabl

Most existing approaches to AI-Generated Text Detection (AIGTD) treat documents as static objects and base their decisions on aggregate statistics or globally compressed embeddings. However, this perspective overlooks the inherently dynamic nature of autoregressive generation, where content evolves progressively through the latent space. In this paper, we reformulate AIGTD as the problem of distinguishing between latent generation trajectories. Instead of relying on static representations, we mo

Neben mehr Einsatzgebieten für Discriminated Unions bietet die Preview weitere Möglichkeiten zur asynchronen Validierung und Neues für die Prozessverwaltung.

Recent findings suggest that detection models for artificial intelligence (AI) cannot accurately identify AI-generated text and may exhibit bias against certain minority groups. In the present study, anecdotal claims that autistic writers more often have their work flagged as AI-generated are examined empirically. A corpus of approximately 60,000 Reddit posts split into "likely-autistic" and "general-Reddit" subcorpora is used to compare the distribution of probabilities output by the OpenAI GPT

Graph neural networks (GNNs) frequently encounter group fairness issues, often yielding biased predictions against specific demographic groups defined by sensitive attributes such as gender or race. While this challenge has motivated extensive research, most existing solutions rely on the strong assumption that demographics are fully available. To bypass this strict requirement, a few recent studies have attempted to use predicted demographics as proxies to enforce fairness constraints. However,

There is a real risk of cultural bias if AI assumes the whole world ought to pursue the same values as the West.

There is a real risk of cultural bias if AI assumes the whole world ought to pursue the same values as the West.

There is a real risk of cultural bias if AI assumes the whole world ought to pursue the same values as the West.

There is a real risk of cultural bias if AI assumes the whole world ought to pursue the same values as the West.

There is a real risk of cultural bias if AI assumes the whole world ought to pursue the same values as the West.

Machine learning (ML) models deployed in sensitive domains such as healthcare, law enforcement, and finance must satisfy not only utility requirements but also fairness and privacy guarantees. While prior work has largely examined how privacy-preserving techniques affect fairness, the inverse question-how fairness-enhancing algorithms influence privacy leakage-remains underexplored. We present the first comprehensive study of how fairness interventions affect membership inference privacy risks a

This study examines the cross-prompt generalization and first-language (L1) scoring effects of a LoRA-adapted open-weight large language model (Gemma-3-27B-it) applied to automated essay scoring. Using the identical model and inference configuration reported in "AiAWE: An Open-Source LLM Automated Writing Evaluation System Using LoRA-Adapted Instruction-Tuned Models" (Gayed, 2026), which was fine-tuned on 480 argumentative essays from two prompts, we evaluate scoring accuracy on the full TOEFL11

Publication date: Available online 15 July 2026 Source: Computers in Human Behavior Author(s): Rongyi Chen, Qing Xiao, Shike Lin, Jingjia Xiao, Menghan Yin, Yucong Ma, Bingbing Zhang, Hua Zhong

arXiv:2605.13866v2 Announce Type: replace Abstract: Humans increasingly delegate consequential decisions to language models, yet whether these systems reproduce or reshape human patterns of discrimination remains unclear. Here, across 29 models and 177 occupations covering nearly half of U.S. employment, we show that language models incorporate demographics into hiring decisions, advantaging female and Black candidates while penalising disabled candidates, with effect sizes comparable to six mon

Generative AI tools can produce polished academic text on demand, undermining the validity of assessments that treat written submissions as evidence of individual learning. Detection-based countermeasures have demonstrated variable accuracy and equity concerns. This paper does not report empirical outcomes or validation data. It proposes a framework for AI-resilient assessment that shifts evaluation from product quality to demonstrable reasoning, decision-making, and ownership of learning. The f

Background: The rapid expansion of remote patient monitoring (RPM) interventions highlights the need for their comparison and evaluation. Current evaluation frameworks often fail to capture a multistakeholder perspective. Traditional health technology assessment approaches emphasize health and economic outcomes, providing an incomplete picture of RPM’s broader impact. The Sextuple Aim, encompassing health outcomes, costs, patient and provider experience, equity, and sustainability, offers a more

As generative AI (GenAI) becomes increasingly embedded in undergraduate academic writing, how students rely on these tools, rather than simply whether they use them, has become a central question for learning, academic integrity, and educational equity. Existing measures of reliance were developed inductively, focused on discrete problem-solving tasks, and validated mainly with homogeneous samples. This study developed and validated the GenAI Reliance Types Scale (GenAI-RTS), a 20-item instrumen

“AI is transforming how our economy operates, and a wide range of voices are unifying around a shared goal: expanding worker equity ownership,” writes Lynn Forester de Rothschild.

Can a handful of engineers really do the work of an army of consultants? That’s the bet behind Ode with Anthropic — the joint venture dedicated to embedding forward-deployed engineers in enterprise firms, backed by Anthropic, Blackstone, Hellman & Friedman, Goldman Sachs and others. On this episode of TechCrunch’s Equity podcast, Rebecca Bellan sits down with Ode’s leaders Chris Taylor and Eddie Siegel, who founded Fractional AI, […]

In real-world applications, node classification on graphs often faces the challenge of class imbalance, where majority classes dominate training, resulting in biased model performance. Traditional GNNs often struggle in such scenarios, as they tend to overfit to majority classes while underrepresenting minority classes. Existing solutions, which either prioritize nodes based on class size or synthesize new nodes for minority classes, often fall short of effectively addressing this imbalance issu

A startup called American Growth Insurance said today it has raised almost $70 million in committed equity capital to transform the insurance industry. It plans to do so with an aggressive, technology-focused business model that’s quite unlike anything its competitors do. AGI, officially known as AGI Holdings LLC, says it has worked out how to use […] The post AGI raises $70M to buy up and transform insurance firms into AI-native operations appeared first on SiliconANGLE .

For low-data and resource-constrained regimes typical of quantum chemistry, parameter-efficient learning is a key objective. Here, we propose a topology-aligned inductive bias in which the model architecture mirrors the molecular bond graph: atoms map to a fixed register of computational units, and bonds determine which pairs interact through shared learnable parameters. This principle is instantiated in two architectures: a variational quantum circuit (Iso-QGNN), and a parameter-matched classic

Former and current Meta employees are suing the company in a California federal court over AI-driven mass layoffs. Meta allegedly used internal AI systems to generate the layoff lists when it cut 8,000 workers, disproportionately targeting employees with disabilities or on parental leave. The article Meta employees sue over layoffs they say were driven by discriminatory AI selection systems appeared first on The Decoder .

In bandit problems, standard regret-minimizing algorithms treat exploration as an amortized cost, which can expose early participants to unfair ex-ante losses in settings such as clinical trials. Recent work addresses this by evaluating the sequence of per-round expected rewards through the generalized $p$-mean, interpolating between utilitarian welfare ($p=1$), Nash welfare ($p\to0$), and Rawlsian fairness ($p\to-\infty$). Although tight guarantees are known for $p\ge0$, the strictly fair regim

Electric mobility startup E3 Electric.Ai has raised ₹100 Cr (about $10.4 Mn) in a mix of equity and debt as…

Synthetic data generated by large language models plays a central role in the training and alignment process of other AI systems. However, this process also risks inheriting the structural biases of organic corpora and embedding new biases that stem from the design choices underlying the data creation process. This paper examines the systematic biases that emerge when large language models (LLMs) are tasked with generating synthetic personas. We introduce a reproducible, minimally conditioned pi

Meta cut its workforce by 10 percent in May.

The lawsuit filed by current and former Meta employees underscores rising concerns about AI's impact on jobs and people with disabilities in the workforce.

Attention-based KV cache eviction (H2O and its descendants) compresses the memory-constrained state of a long-context model by ranking tokens on accumulated attention mass, treated here as signal energy, and keeping the heaviest. On schema-dense input streams such as nested JSON, this score acts as a non-stationary filter that disproportionately retains noise: a non-content sink role (delimiters or whitespace) carries an order of magnitude more energy than any content role, and structural KEY to

The persuasive power of data visualizations can go awry: for instance, in an explainable AI (XAI) context, visualizations can produce over-trust of predictive models. In this paper, we use a crowdsourced study to show that providing accurate (but superfluous or irrelevant) data in a model explanation can, in fact, result in unjustified trust and other positive beliefs about a model, even when the model is patently discriminatory and unfair. Our results suggest that XAI designers and developers n

A group of 26 former Meta employees is suing the company over claims that it used AI tools to unfairly target workers on leave with layoffs, as reported earlier by Reuters. In the lawsuit, the employees allege Meta determined which workers to dismiss based on performance data collected by a "constellation" of internal AI tools, […]

Indian B2B ecommerce platform Udaan has secured $160 million in a funding round that combines fresh equity from a new investor, additional debt, and the conversion of existing convertible bonds into equity, according to Bloomberg. Lightspeed Venture Partners and M&G Investments are participating in the round. The deal is designed to clean up Udaan’s balance […] This story continues at The Next Web

Publication date: August 2026 Source: Artificial Intelligence, Volume 357 Author(s): Arthur Dantas Mangussi, Ricardo Cardoso Pereira, Miriam Seoane Santos, Ana Carolina Lorena, Mykola Pechenizkiy, Pedro Henriques Abreu

Publication date: December 2026 Source: Computers in Human Behavior, Volume 185 Author(s): Ali Ladak, Janet V.T. Pauketat, Jacy Reese Anthis, Steve Loughnan, Matti Wilks