Bias & fairness

Paytm will invest up to Rs 100 crore in Paytm Money as AI boosts engagement and monetisation across equity broking, margin trade funding and wealth products, supporting further expansion and growth. The post Why is Paytm doubling down on Paytm Money? appeared first on MEDIANAMA .

Transformers with relative positional encodings often extrapolate to sequences longer than those seen during training, whereas transformers with learned absolute encodings typically do not. This is a robust empirical regularity, and the explanations offered for it so far are chiefly about expressivity, that is, about whether a length-generalizing solution exists. We give an optimization explanation. On a minimal fixed-offset retrieval task that isolates positional selection, the gap is governed

Reinforcement learning (RL) methods for learning-to-rank (LTR) can optimize (almost) any ranking goal, e.g., from precision or discounted cumulative gain to fairness-of-exposure or ranking distillation. However, standard RL is ineffective and computationally costly due to the enormous action space in LTR settings. Existing methods reach computational efficiency through custom gradient computation algorithms, but they are very complex to implement and often clash with auto-differentiation. Conseq

arXiv:2607.16620v1 Announce Type: cross Abstract: Differential privacy (DP) is increasingly deployed to limit membership inference risk in machine-learning systems. Prior work has shown that DP-SGD can widen accuracy disparities across demographic groups, but this framing treats fairness as a purely outcome-side concern. We argue that privacy cost, the information leakage borne by each group, is itself a form of harm, and adopt a compensatory-fairness framework in which a group that involuntaril

arXiv:2607.17311v1 Announce Type: cross Abstract: The problem of fair multi-agent coordination in decentralized settings is one of the most pressing challenges for building efficient collaborative systems. Resource allocation is based on optimized collective arrangements accounting for agents' needs. Such coordination should not only be computationally efficient but also account for fairness, i.e., equitable redistribution of costs incurred by all agents. Recent literature has proposed several a

arXiv:2504.21259v2 Announce Type: replace Abstract: Accurate imputation of race and ethnicity (R&E) is essential for fair lending compliance under ECOA, HMDA, and the Community Reinvestment Act, where up to 15% of mortgage applications carry missing race data and regulated institutions bear responsibility for identifying disparities on those records. Existing proxy methods, including Bayesian Improved Surname Geocoding (BISG), exhibit systematic misclassification biases linked to socioeconomic s

arXiv:2508.13187v4 Announce Type: replace Abstract: Homelessness is a persistent social challenge, impacting millions worldwide. Over 876,000 people experiencing homelessness (PEH) were recorded in the U.S. in 2025. Social bias is a significant barrier to alleviating homelessness, shaping public perception and influencing policymaking. Because online textual media and offline city council discourse both reflect and influence public opinion, they provide valuable signals for identifying and track

arXiv:2510.04748v3 Announce Type: replace Abstract: The prevalence of online hate and abuse is a pressing global problem. While tackling such societal harms is a priority for research across the social sciences, it is a difficult task, in part because of the magnitude of the problem. People's engagement with reporting mechanisms ('flagging') online is an increasingly important part of monitoring and addressing harmful content at scale. However, users may not flag content routinely enough, and wh

arXiv:2607.05454v2 Announce Type: replace-cross Abstract: The journal is a seventeenth-century technology asked to do four modern jobs at once: disseminate results, certify their quality, allocate scholarly attention, and confer career credit. It does none of them well. Pre-publication peer review is slow, only weakly reliable, demonstrably biased toward established authors and institutions, and expensive, while the reviewing effort it consumes is spent largely on work that will never matter. We

Ant International, the affiliate of Chinese fintech giant Ant Group, has raised about US$1.2 billion in a Series A funding round to fuel its global expansion, the company said on Tuesday. Existing backers Ant Group and Alibaba Group Holding joined the equity financing alongside several international institutional investors, whose identities were not disclosed. The fresh capital would be used to accelerate the firm’s international growth and drive innovation in merchant payments, account...

The English-first bias in technology must be addressed.

BY THE PRESIDENT OF THE UNITED STATES OF AMERICA A PROCLAMATION 1. Section 338 of the Tariff Act of 1930 (19 U.S.C. 1338) (section 338) empowers the President to, among other things, impose duties on imports of a foreign country to offset the burden or disadvantage from a foreign country’s discrimination against or unequal imposition […] The post Imposing Additional Duties to Offset Canadian Discrimination Against the Commerce of the United States with Respect to Motor Vehicles appeared first on

BY THE PRESIDENT OF THE UNITED STATES OF AMERICA A PROCLAMATION 1. Section 338 of the Tariff Act of 1930 (19 U.S.C. 1338) (section 338) empowers the President to, among other things, impose duties on imports of a foreign country to offset the burden or disadvantage from a foreign country’s discrimination against or unequal imposition […] The post Imposing Additional Duties to Offset Canadian Discrimination Against the Commerce of the United States with Respect to Alcoholic Beverages appeared fir

BY THE PRESIDENT OF THE UNITED STATES OF AMERICA A PROCLAMATION 1. Section 338 of the Tariff Act of 1930 (19 U.S.C. 1338) (section 338) empowers the President to, among other things, impose duties on imports of a foreign country to offset the burden or disadvantage from a foreign country’s discrimination against or unequal imposition […] The post Imposing Additional Duties to Offset Canadian Discrimination Against the Commerce of the United States with Respect to Dairy appeared first on The Whit

To a private equity firm.

Background: Black communities face disproportionate burdens of health disparities such as chronic disease, maternal morbidity, and barriers to accessing quality care. Digital health technologies (DHTs) are increasingly promoted as tools to reduce health disparities through access to care. However, the extent to which equity-oriented design approaches have been applied to address the needs of Black communities remains unclear. Objective: This scoping review aims to examine (1) current literature

Robots in cluttered indoor spaces often fail not because they cannot generate collision-free paths, but because a fixed safety margin is mis-calibrated: conservative margins cause detours and timeouts, while permissive margins lead to near-boundary shortcuts under perception bias. Diffusion-based planners propose diverse trajectory candidates from egocentric RGB-D, yet reliable selection remains the bottleneck. We propose a context-conditioned safety critic that learns an adaptive clearance pref

Fair clustering aims to make cluster assignments independent of sensitive attributes, but this goal becomes challenging when multiple sensitive attributes jointly define many subgroups. In such settings, directly extending existing fair clustering algorithms is computationally expensive or numerically unstable, especially when the number of subgroups grows exponentially and some subgroups contain only a few instances. To address these challenges, we define a subgroup-fairness gap for clustering

Modern LLMs are alarmingly susceptible to surprisingly simple immaterial changes of input prompts: a casual hint, an incorrectly labeled few-shot example, or a fake prior assistant turn often flips an originally correct answer. We study where this susceptibility, spanning sycophancy and related cue-induced biases, lives inside the model. Across five model families and seven BCT bias types, we extract a per-bias direction from hidden states and triangulate it through three measures: probing, leav

About half of voters believe it isn't appropriate for the U.S. government to own stakes in companies, a new CNBC poll found.

Concept erasure aims to remove semantic concepts from a trained generative model and is increasingly important for responsible AI deployment. However, verifying whether a model has robustly removed targeted concepts remains a critical challenge. Existing evaluation methods are typically pre-defined and static, failing to expose vulnerabilities under diverse natural-language probes and challenging conditions. Moreover, manually designed evaluation strategies can be biased and difficult to scale.

Concept erasure aims to remove semantic concepts from a trained generative model and is increasingly important for responsible AI deployment. However, verifying whether a model has robustly removed targeted concepts remains a critical challenge. Existing evaluation methods are typically pre-defined and static, failing to expose vulnerabilities under diverse natural-language probes and challenging conditions. Moreover, manually designed evaluation strategies can be biased and difficult to scale.

Financial statements (FS) such as Balance Sheet (BS), Income Statement (IS) and Cash-flow Statement (CS) summarize the annual financial performance of a company. FS are widely used for evaluating corporate governance, credit appraisal, risk analysis, validate taxation, make investment decisions etc. Financial auditing is a complex and knowledge-intensive discipline whose one important aim is ensuring integrity, accuracy, fairness and absence of material misstatement in the published FS. Given th

This is an invited commentary on the Psychometrika focus article "Fairness Issues and Evaluation in Psychometrics and AI/ML: What Can We Learn from Each Field?" by Ying Cheng (2026, doi:10.1017/psy.2026.10110). Cheng offers a systematic comparison between long-standing test fairness and modern algorithmic fairness. Her mapping of the entire testing workflow onto the AI/ML fairness paradigm, rather than only the final selection stage, is a crucial contribution to interdisciplinary fairness resear

Inside the ‘Culture of Fear’ at One American-Chinese University Emma Whitford Mon, 07/20/2026 - 03:00 AM Faculty at Wenzhou-Kean have accused their employer of obfuscatory employment agreements, wrongful termination, discrimination, retaliation and lack of shared governance. University officials dispute the charges. Byline(s) Emma Whitford

Australia’s first AI traffic-light trial could cut delays. It also raises hard questions about fairness, safety and who gets priority on public roads.

On the latest episode of Equity, we debate whether Apple's lawsuit will cast over OpenAi's much-discussed plans to get into hardware and go public.

The rise in 30-year Treasury yields above 5.0% carries a warning ⁠for equity valuations. Just a glance at a chart shows yields have spent little time ​above that barrier in the past two decades, and ...

The problem of fair multi-agent coordination in decentralized settings is one of the most pressing challenges for building efficient collaborative systems. Resource allocation is based on optimized collective arrangements accounting for agents' needs. Such coordination should not only be computationally efficient but also account for fairness, i.e., equitable redistribution of costs incurred by all agents. Recent literature has proposed several algorithms that efficiently determine optimal plan

Differential privacy (DP) is increasingly deployed to limit membership inference risk in machine-learning systems. Prior work has shown that DP-SGD can widen accuracy disparities across demographic groups, but this framing treats fairness as a purely outcome-side concern. We argue that privacy cost, the information leakage borne by each group, is itself a form of harm, and adopt a compensatory-fairness framework in which a group that involuntarily bears greater privacy exposure is owed proportio

While Mars rover operators plan drives across hazardous Martian terrain and diagnose unexpected faults, the necessary information is distributed across separate systems and often reconstructed through manual correlation and memory. To address this challenge, we partnered with Mars rover operators at the NASA Jet Propulsion Laboratory to introduce Hindsight, a visual analytics system that unifies previously disparate rover drive data into a single workspace for search, comparison, and investigati

When Ubisoft's Watch Dogs 2 was released in 2016, it was a headtrip for those of us working on digital-rights issues in the Bay Area. During the day, I'd fight tech-authoritarianism from EFF's San Francisco offices and then, at night, I'd fight tech-authoritarianism in an uncanny simulation of San Francisco from my home gaming console. Watch Dogs 2 is an open-world video game that follows a hacktivist collective called Dedsec as they take on surveillance tech and discriminatory AI systems that a

Over the past decade, responsible AI (RAI) has produced a substantial body of practice for identifying and mitigating the risks AI poses in high-stakes settings. Yet this work has not produced a market that rewards trustworthiness. Firms that invest seriously in safety, fairness, and oversight cannot consistently prove to consumers, regulators, and shareholders that their systems go beyond the bare minimum of compliance. What is missing is a way for society to recognize or compare the difference

Geospatial datasets support applications from urban planning to climate modeling, yet consistent assessment of FAIR compliance is difficult. Existing evaluators use different rubrics and evidence sources and may fail on JavaScript-rendered pages or repository-specific identifiers. For 50 datasets from 10 repositories, the standard deviation of normalized scores across available tools averages 15.0 percentage points and reaches 30.3 for one dataset. Because these outputs are not equivalent measur

Artificial Intelligence can produce biased outputs. In part, this is because unrepresentative data is used to train, validate and test AI. To remedy skewed datasets and train fairer AI, many call for more comprehensive and systematic data production and processing about diverse people’s bodies and lives, including disabled people. Yet this response rests on a number of assumptions. Drawing on disability data, I argue that we should be cautious about these assumptions when regulating AI. The post

As Text-to-Image (T2I) systems rapidly advance, evaluating the cultural authenticity of synthesized content has become increasingly important for fair and trustworthy generative AI. Existing T2I evaluation metrics and multimodal judges often rely on visual-semantic representations that underrepresent implicit cultural norms, leading to biased preference judgments and the omission of fine-grained cultural cues. In addition, visual question answering (VQA)-based evaluators typically depend on auto

Policymakers around the world face the question of how to use artificial intelligence in general, and large language models in particular, to improve the policymaking process. Used well, large language models can strengthen the collection, interpretation and synthesis of policy-relevant information and the drafting of policy-relevant output. Yet the use of large language models in policymaking is associated with risks. Output that is plausible but not necessarily correct, bias resulting from unr

arXiv:2607.14301v1 Announce Type: cross Abstract: 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 Rel

arXiv:2607.14605v1 Announce Type: cross Abstract: 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

arXiv:2607.15146v1 Announce Type: cross Abstract: 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