Bias & fairness

President Trump on Friday slammed the European Union for fining Google over allegedly violating its digital competition law, stating the "illegal and highly discriminatory practice" will be probed in a trade investigation. "The European Union is at it again and, as usual, taking direct aim at GREAT American Companies!" Trump wrote on Truth Social. "This...

A central design principle in modern machine learning and artificial intelligence is to align a model's inductive bias with the structure of its input data. For matrix-valued inputs, relevant matrix-level relationships can be characterised through spectral values and spectral subspaces; however, common coordinate-wise rotation-gate data-encoding unitaries used in most quantum machine learning models do not explicitly construct such a matrix-level representation. We introduce Quantum Spectral Mod

The private-equity firm already owns around 60% of the business and is set to buy out founder Casey Wasserman's remaining stake. Source

Despite recent advances, self-supervised learning (SSL) models and Joint-Embedding Predictive Architectures (JEPAs) remain susceptible to learning spurious biases in the dataset. These techniques rely on regularization, which prevents representation collapse by enforcing a global target distribution such as a multivariate Gaussian or a uniform distribution on the sphere. However, these global constraints are insufficient to prevent bias entanglement, as task-irrelevant features can still segrega

ED Scraps Tool to Investigate Discrimination Sara Weissman Fri, 07/24/2026 - 03:00 AM For decades, the Office for Civil Rights investigated whether educational institutions’ policies disparately impacted minority groups in discriminatory ways. Not anymore. Byline(s) Sara Weissman

In this article, we investigate influences of social positionality, designer bias and educational exposure on algorithmic design in healthcare. Against the backdrop of literature on designer bias that points to how it contributes to disproportionate, discriminatory and unethical impacts for racialized and gendered bodies, this study tests this argument with an experiential case study involving Aldebaran’s NAO robot and mechatronics and robotics engineering students at a university in S. E. Ontar

Privacy-preserving machine learning auditing protocols allow auditors to assess models for properties such as accuracy or fairness, without revealing their internals or training data. This makes them especially attractive for auditing models deployed in sensitive domains such as healthcare or finance. For these protocols to be meaningful in real-world audit settings, though, their guarantees must reflect how the model will behave once deployed, rather than merely certifying its behavior during a

Diffusion models typically suffer from error accumulation during iterative sampling, commonly referred to as exposure bias. We reveal systematic frequency-dependent discrepancies between training and inference, which can be interpreted as frequency-dependent SNR error. Crucially, the direction of this mismatch varies across models and timesteps, indicating that fixed correction rules do not generalize. We propose Spectral Alignment (SPA), a lightweight, guidance-based method that calibrates the

Yes, strong laws already exist to protect job-seekers from discrimination. But AI has made things more complicated.

Passive sensing for wellbeing uses smartphones and wearables to continuously collect human behavioral data and applies ML/AI models to infer psychological states and behaviors (e.g., depression, cognitive load). These systems are increasingly adopted in high-stakes settings (e.g., hospitals, universities), yet fairness research remains limited---primarily to post-hoc, identity-based comparisons of model performance. However, passive sensing combines heterogeneous sensing infrastructures, indirec

Passive sensing for wellbeing uses smartphones and wearables to continuously collect human behavioral data and applies ML/AI models to infer psychological states and behaviors (e.g., depression, cognitive load). These systems are increasingly adopted in high-stakes settings (e.g., hospitals, universities), yet fairness research remains limited---primarily to post-hoc, identity-based comparisons of model performance. However, passive sensing combines heterogeneous sensing infrastructures, indirec

Machine unlearning has emerged as a tool for removing personal data from trained models to comply with recent AI regulations. To evaluate unlearning effectiveness in multimodal large language models (MLLMs), prior works fine-tune models on fictitious identities, simulating unlearning requests on subsets of these IDs, which are typically uniformly distributed. However, in realistic scenarios, people from different demographic groups may request to be unlearned at different frequencies, potentiall

Black students have been excluded from high-quality mathematics for generations — with dire outcomes academically and professionally, a California-based math equity group found. Just Equation’s recent report, “Calculated Barriers,” notes that while these students make up 15% of the nation’s public school population, they accounted for only 6% of those enrolled in AP math classes […]

Almost every large language model that reaches a broad audience is quantized: trained in full precision, then compressed for efficiency. This step is assumed harmless and its safety is rarely re-checked. We find its principal side effect is increased bias that standard safety evaluation misses. Holding the model, its training, and the prompts fixed, a quantized model still refuses harmful requests, still avoids over-refusing benign prompts, and still selects the unbiased multiple-choice answer.

arXiv:2607.19389v1 Announce Type: new Abstract: As AI-driven Decision Makers (ADMs) influence our socioeconomic reality, their roles in both enhancing efficiency and amplifying the social biases have drawn attention. In this paper, we revisit the nuances of long-term `fairness' achievable by an ADM, specifically in the context of a credit lending induced wealth process. The literature on long-term fairness mostly (a) considers passive environments, i.e. the outcome of a predictor does not change

arXiv:2607.19586v1 Announce Type: cross Abstract: Despite increasing awareness of cybersecurity risks, users continue to engage in insecure password practices, such as reusing passwords, choosing weak credentials, and neglecting security recommendations. The study explores the behavioral and cognitive factors that influence password decision-making by integrating insights from behavioral economics, particularly hyperbolic discounting, status quo bias, and present bias. We conducted a survey to a

IntroductionQuality control of hatchery production relies on accurate developmental staging of the Pacific white shrimp Litopenaeus vannamei post-larvae (PL), but current methods rely on subjective manual visual evaluation that leads to observer bias and inconsistency.MethodsIn this study, the Hierarchical Isotropic Dense Attention Network (HIDANet) has been introduced, a lightweight convolutional neural network with 0.033M parameters that learns to classify seven post-larval stages (PL5–PL12) i

Darren Aronofsky has showcased the work his new AI-forward studio Primordial Soup can do. Now he wants more money to do it. The company disclosed in a Securities and Exchange Commission filing last week that it was seeking to raise $15 million in cash in exchange for equity in the studio. The filing lists Aronofsky […]

Human judgment is fundamentally prone to error. A promise of AI is that it will rid decisions of bias and ensure a fairer and safer world for all. Yet research unequivocally demonstrates that LLMs exhibit consequential sociocognitive biases. We alert readers that bias in AI (a) is covert and ironically a feature of alignment goals, (b) is not merely a mirror, but an amplifier of human bias, (c) intensifies across model generations, and (d) even transmits bias to humans. Given the potentially sei

Physics-informed learning of partial differential equations (PDEs) has been dominated by multilayer perceptrons (MLPs), whose spectral bias and dense parameterization limit both accuracy and interpretability. Kolmogorov Arnold Networks (KANs) mitigate these limitations because their learnable spline activations are structurally aligned with the piecewise-polynomial bases of classical discretizations. However, the way a PDE is cast into a loss functional is as decisive as the choice of approximat

The private equity firm sold its majority stake in June. The post Blackstone Divested From Spanx appeared first on Above the Law .

AI-based recruitment systems that rely on machine learning models trained on historical CV data, risk perpetuating and amplifying social biases. A key challenge arises in unstructured CV text, where pre-trained language model embeddings may infer sensitive attributes such as gender even after explicit indicators are removed. In this paper, we evaluate nine pre-trained embedding models on the synthetic FairCVdb dataset, analyzing the informativeness of their embeddings for applicant scoring and t

The influence of linguistic bias in online news articles is a growing concern, particularly in the context of shaping public opinion and rising political polarization. While there is a growing body of literature on indicators for misinformation, none have been sufficiently tested to counteract the influence of media bias. Hence, we design six indicators (Bias Bar, Bias Gauge, Bias Highlights, Political Scale, Sentiment Scale, and Trust Score) and test their impact on linguistic bias detection an

This paper examines the integration of artificial intelligence (AI) into cancer screening programmes, focusing on the associated equity challenges and resource allocation implications. While AI technologies promise significant benefits—such as improved diagnostic accuracy, shorter waiting times, reduced reliance on radiographers, and overall productivity gains and cost-effectiveness—current interventions disproportionately favour those already engaged in screening. This neglect of no

This paper describes a global pattern of declining academic freedom, often driven by powerful political interference with core functions of academic communities. It argues that countering threats to academic freedom requires doubling down on ethics, specifically standards of justice and fairness in pursuing knowledge and assigning warrant to beliefs. Using the example of the selection of a Qatari university to host the 2024 World Congress of Bioethics, the authors urge fairness towards diverse g

Critics may label the initiative as 'Privacy sandbox 2.0,' but advocates counter with privacy arguments.

arXiv:2607.18931v1 Announce Type: new Abstract: Web browsers now provide AI-generated news summaries for millions of users. Despite their popularity and influence, we lack a systematic understanding of how these systems transform news before people read it. Through a large-scale audit, we investigate the factual accuracy of browser-based AI summarizers and how they alter the political bias, negative affect, and journalistic writing quality of news. Drawing on 13,777 articles from 15 U.S. news ou

Despite increasing awareness of cybersecurity risks, users continue to engage in insecure password practices, such as reusing passwords, choosing weak credentials, and neglecting security recommendations. The study explores the behavioral and cognitive factors that influence password decision-making by integrating insights from behavioral economics, particularly hyperbolic discounting, status quo bias, and present bias. We conducted a survey to analyze how people create, store and manage their p

Anthea Hartig rejected a White House report accusing the Smithsonian of “ideological capture” and anti‑American bias.

Recent autoregressive video diffusion methods are increasingly built upon Self Forcing, where the student is trained on histories produced by its own rollout rather than ground-truth video contexts. This reduces exposure bias, but the historical key-value cache is still used by future frames only as frozen rollout state. As a result, future losses cannot supervise how earlier generated latents should be written into more useful keys and values for later video-latent generation. We call this the

Federated learning has emerged as a potential solution to privacy concerns associated with using sensitive health data for training predictive models, particularly in personalised cancer care. This research investigates whether federated learning can support the development of robust models for predicting tumour progression in breast cancer patients while addressing four critical deployment pillars: transparency, scalability, security, and fairness. This study evaluates a federated learning fram

Sila, the battery materials company formerly known as Sila Nanotechnologies, has raised $300 million in a private equity round led by Atreides Management and Sutter Hill Ventures to expand its silicon anode manufacturing plant in Moses Lake, Washington. 8VC, Bessemer Venture Partners, Matrix Partners, and T Rowe Price also participated, alongside existing investors including In-Q-Tel, […] This story continues at The Next Web

On Tuesday, a group of the world’s most powerful investors finished buying a company most people have never heard of. The price tag explains why it matters. The largest data-centre deal ever The AI Infrastructure Partnership, MGX, and BlackRock’s Global Infrastructure Partners completed their purchase of all the equity in Aligned Data Centers. The deal […] This story continues at The Next Web

Everyone agrees competition enforcement should be fair. Agreement gets shakier once fairness starts costing agencies time, discretion, or victories. Competition authorities often warn that procedure should not become an obstacle to enforcement. Fair enough. Antitrust investigations can be slow, document-heavy, and vulnerable to delay tactics. A firm with deep pockets may try to turn “process” ... Competition Law’s Fairness Gap: Why Better Procedure Requires Better Incentives The post Competition

Although Large Language Models (LLMs) demonstrate remarkable multilingual fluency, their internal knowledge representations remain disproportionately biased toward high-resource languages. This leads to cross-lingual factual inconsistency, where they shift their empirical answer distributions based solely on the prompt language. We investigate whether these biases can be mitigated at inference time, forcing an English-prompted model to answer as if it were queried in target languages (German, Sp

Objective Structured Clinical Examinations (OSCEs) are the gold standard for assessing clinical competence, yet scoring remains vulnerable to examiner subjectivity, fatigue, and cognitive bias. Standard examiner validation via inter-rater statistics lacks explanatory power regarding the source of errors, as it neither analyzes examiner reasoning nor verifies examiner claims against actual events. Thus, we introduce Quality Action Assurance (QAA), a multimodal framework that verifies examiner cla

We present AutoJourn, a demonstration system for multi-perspective news generation and bias-aware evaluation using large language models (LLMs). The system tackles three core challenges in responsible automated journalism: extracting diverse perspectives from unstructured social media discussions, generating summaries that preserve viewpoint diversity, and detecting or mitigating bias in AI-generated news. The pipeline integrates advanced prompt engineering with optional retrieval augmentation t

Web browsers now provide AI-generated news summaries for millions of users. Despite their popularity and influence, we lack a systematic understanding of how these systems transform news before people read it. Through a large-scale audit, we investigate the factual accuracy of browser-based AI summarizers and how they alter the political bias, negative affect, and journalistic writing quality of news. Drawing on 13,777 articles from 15 U.S. news outlets, we evaluate their 41,331 summaries genera

Artificial intelligence (AI) is increasingly integrated into healthcare to support diagnostics, decision-making, and administrative processes. However, the successful implementation of AI depends not only on technical performance but also on public perceptions of its helpfulness, riskiness, and fairness. This study examines public perceptions of automated decision-making (ADM) in healthcare. Data were drawn from the first wave of an ongoing longitudinal survey panel. The final sample consisted o