Bias & fairness

Publication date: August 2026 Source: Technology in Society, Volume 87 Author(s): Lifeng Lai, Ted Brader

Publication date: August 2026 Source: Technology in Society, Volume 87 Author(s): Pedro C. Magalhães, Sveinung Arnesen, Christoph Kern, Pascal D. Koenig, Daniel S. Schiff, Tom R. Tyler

Publication date: Available online 10 July 2026 Source: Computers in Human Behavior Author(s): Dongyu Liu, Xinyu Zhang, Boxiang Li, Christian Montag, Jon D. Elhai, Haibo Yang

Publication date: August 2026 Source: Technology in Society, Volume 87 Author(s): Hamide Özyürek, Karahan Kara, Galip Cihan Yalçın, Zeynep Baysal, Ufuk Türen, Vladimir Simic, Mustafa Polat, Dragan Pamucar

Publication date: August 2026 Source: Technology in Society, Volume 87 Author(s): Leilei Jiang, Jie Cao, Guixiang Zhu, Yuyao Wang

Publication date: December 2026 Source: Computers and Education: Artificial Intelligence, Volume 11 Author(s): Robin Schmucker, Steven Moore

Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Qian Liu, Louise Archer, Meghna Nag Chowdhuri, Esme Freedman, Jennifer DeWitt

Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Lin Li, Namrata Srivatava, Jia Rong, Quanlong Guan, Dragan Gašević, Guanliang Chen

Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Lilach Alon, Dorit Hadar Shoval, Inbar Levkovich

The Sixth ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization [Munich] [Nov 5, 2026 - Nov 7, 2026]

The Sixth ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization [Munich] [Nov 5, 2026 - Nov 7, 2026]

The Sixth ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization [Munich] [Nov 5, 2026 - Nov 7, 2026]

The Sixth ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization [Munich] [Nov 5, 2026 - Nov 7, 2026]

Call for CRAFT proposals - Critiquing and Rethinking Accountability, Fairness, and Transparency - ACM FAccT 2026 [Montreal] [Jun 25, 2026 - Jun 28, 2026]

Transformer language models are increasingly used as software components, yet biased outputs remain difficult to localize and repair inside the model. Existing fairness testing and repair methods largely operate at the input-output or retraining level, while recent work suggests that bias-related behavior can concentrate in a small set of attention heads. This paper studies whether attention heads can be localized and repaired through a targeted inference-time intervention. We introduce ROBIN, a

This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition. For multi-task learning with simultaneous prediction of valence, arousal, facial expressions, and action units on s-Aff-Wild2 dataset, we use frozen lightweight facial extractors, MT-EmotiDDAMFN and MT-EmotiEffNet-B0, with separate heads and systematic post-processing: temporal Gaussian smoothing, per-class expression bias, AffectNet blending, per-AU threshold tuning, and weighted backbone

Multiple-choice benchmarks that rank candidate completions by conditional log-probability suffer from a length bias: because log-probabilities sum over tokens, longer answers tend to be penalized relative to shorter ones in practice. A common mitigation is to normalize scores by completion length, but we show empirically that this heuristic frequently over-corrects, introducing a bias toward longer answers instead. We first analyze these scoring rules, characterizing when standard and length-nor

As Large Language Models (LLMs) are increasingly deployed as autonomous agents in high-stakes domains, understanding contextual factors that may modulate their decision-making becomes critical. While LLMs are trained to perceive and resonate with users' emotions, it remains unclear whether induced emotion can influence their sequential decision-making. We investigate this question using the Iowa Gambling Task (IGT), a classic psychological paradigm for studying decision-making under uncertainty,

Plus how the IAB is seeking to modernize how digital video inventory is defined, bought and measured.

arXiv:2607.11228v1 Announce Type: new Abstract: While Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities, they remain highly susceptible to embedded social biases. Existing bias evaluation protocols predominantly rely on static datasets, which provide only a superficial assessment, as their fixed test cases cannot adaptively evolve to measure the true depth and limits of model vulnerabilities. We introduce DeepBias, an adaptive framework for the in-depth probing of social b

arXiv:2607.11314v1 Announce Type: new Abstract: The promise of AI literacy ``for all'' confronts a structural challenge embedded in how nations organise secondary computer science education. In most systems, a general-track subject -- Digital Literacy, ICT, TIC, or SNT -- bears the weight of universal AI literacy, while a specialist Informatics course serves STEM pathways separately. Yet the content and depth of the general track are shaped by governance decisions made largely with reference to

arXiv:2607.10317v1 Announce Type: cross Abstract: Algorithmic decision systems in financial services often rely on data proxies that inadvertently encode structural inequalities. This paper introduces a hierarchical human-AI triage model for Point of Sale fraud detection in the Nigerian FinTech sector. Adopting a We Are All Equal worldview, we address the challenge of discrimination laundering, wherein the system misinterprets infrastructure related aleatoric noise such as rural network timeouts

arXiv:2607.10523v1 Announce Type: cross Abstract: Data narratives increasingly shape public understanding, but their failures are rarely just isolated factual errors or deceptive charts. Instead, they emerge through a broader meaning-making process in which quantitative evidence is transformed into claims, representations, and arguments. While prior work has examined these failures across disparate fields (e.g., statistics, visualization, and fact-checking), the community lacks a holistic lens t

arXiv:2607.01254v2 Announce Type: replace Abstract: Benchmarks are the primary instruments through which AI capability is measured, compared, and governed. This paper argues that the validity of frontier AI benchmarks is a function of the quality of human judgment embedded in their construction, and that this quality is structurally scarce in ways that standard scaling narratives obscure. As foundation models approach ceiling performance on existing evaluation suites, discriminating signal conce

arXiv:2606.28186v2 Announce Type: replace-cross Abstract: Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction. Existing methods often depend on costly human calibration or item-level textual representations, providing limited evidence about the cognitive processes that make items difficult. We argue that difficulty should be viewed not only as a property of item text, but also as an observable consequenc

FIFA's semi-automated refereeing system promises neutrality but brims with bias. It's widely despised yet embraced by elites. It is, in other words, a lot like AI.

Existing studies of LLM-as-judge scoring bias work predominantly at the input-output level: they perturb inputs, measure score deltas, and propose prompt-level mitigations. We argue that the same biases admit a representation-level account in the judge's hidden state, complementary to the input-output view and operationally useful in ways it does not afford. We report three findings, across seven judges, seven bias types, and nine benchmarks. Geometry: baseline judging inputs occupy a tight acti

The promise of AI literacy ``for all'' confronts a structural challenge embedded in how nations organise secondary computer science education. In most systems, a general-track subject -- Digital Literacy, ICT, TIC, or SNT -- bears the weight of universal AI literacy, while a specialist Informatics course serves STEM pathways separately. Yet the content and depth of the general track are shaped by governance decisions made largely with reference to the specialist one. This paper presents a compar

The widespread adoption of social media platforms has transformed online communication by enabling users to exchange information and opinions instantly. However, these platforms have also facilitated the dissemination of abusive and hateful content, posing major social, psychological, and ethical challenges. Hate speech can incite discrimination, harassment, and violence against individuals or communities based on attributes such as ethnicity, religion, gender, nationality, or political affiliat

Open-vocabulary dense perception (OVDP) aims to localize objects unseen during training by leveraging textual knowledge. Despite the remarkable progress of recent CLIP-based approaches, we identify a critical limitation: synonym-induced grounding inconsistency, where semantically equivalent expressions yield disparate spatial attention patterns. This inconsistency undermines the robustness and performance of existing methods in real-world OVDP applications. To address this issue, we propose SynC

IntroductionGenerative artificial intelligence is increasingly used in higher education, yet its educational value depends not only on technological access but also on how its use is pedagogically regulated. This study examined how teacher-regulated generative AI support is associated with university students' perceived learning gains in higher education. It further tested whether student agency mediates this association and whether perceived fairness conditions the strength of the association b

We introduce NAILS (Normative Alignment of Recommender Systems via Internal Label Shift), a simple and scalable method for aligning recommendation outputs with target distributions over item-level attributes, such as categories. Recommender systems optimized solely for user engagement often fail to satisfy broader normative objectives, including fairness, diversity, and editorial values. NAILS modifies the user-conditional item distribution to induce a specified marginal distribution over attrib

Data narratives increasingly shape public understanding, but their failures are rarely just isolated factual errors or deceptive charts. Instead, they emerge through a broader meaning-making process in which quantitative evidence is transformed into claims, representations, and arguments. While prior work has examined these failures across disparate fields (e.g., statistics, visualization, and fact-checking), the community lacks a holistic lens to explain how these issues arise, propagate, and c

Large Language Models (LLMs) are increasingly used for code smell detection tasks due to their ability to interpret program semantics. However, their reliability in this context remains poorly explored, particularly under varying prompt conditions where model predictions may be influenced by external cues rather than code characteristics. One such limitation is sycophancy bias, where models tend to align their outputs with user-provided assumptions instead of performing objective analysis. In th

Influence maximization in social networks has received increasing attention, particularly in applications where fairness among demographic groups is an important concern. However, many existing approaches either overlook group-level disparities or primarily optimize influence spread without explicitly modeling fairness-related trade-offs. In this paper, we propose a group-aware multi-objective evolutionary framework that decomposes seed sets into group-specific sub-solutions. Each demographic gr

Pricing food products to balance profitability with consumer welfare is a central challenge for retailers. Dynamic pricing is widely used to maximize revenue, yet most pricing models optimize business objectives while overlooking consumer fairness. This paper studies the risk of consumer exploitation under dynamic food pricing in Canada and proposes a methodology that embeds fairness constraints directly into retail sales forecasting. We model total retail trade sales with a log--log Autoregress

Audio deepfake detection models determine whether speech is genuine or artificially generated, but high overall accuracy can mask substantial performance disparities across demographic groups. In this work, we investigate gender bias in audio deepfake detection using the ASVspoof5 dataset. We use ASVspoof5 under a controlled custom split designed to isolate gender-composition effects. We train attack-specific models on nine training sets with different gender compositions, ranging from female-on

Last weekend I was in Erfurt, the place where the authoritarian-populist AfD party held its annual federal convention. On Saturday I got up at the crack of dawn to help block the AfD delegates from reaching the assembly hall. Was I supposed to do that? As managing director of a legal-scholarly discourse platform should I not have remained neutral? The demand to forbid oneself from discriminating between non-banned parties loyal to the constitution and non-banned parties hostile to it is question

Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isolation and along single demographic axes. This limits practical guidance for selecting fairness strategies, where disparities may arise across intersectional subgroups and across multiple stages of the modeling lifecycle. This work presents FairSelect, a toolkit for systematically evaluating fairness mitigation strategies applied individually and in