131 papers
This survey critically examines the growing body of LGBTQIA+ NLP research within the ACL Anthology, revealing a reactive focus on identifying bias rather than proactive mitigation, and calls for future work to prioritize stakeholder involvement, intersectionality, interdisciplinarity, and non-English languages to build more just and inclusive technologies.
This paper proposes a goal-centric framework for assessing Requirements Engineering methods for Privacy by Design, arguing that practitioners should evaluate these methods based on organizational goals rather than solely on process characteristics to better support their selection and tailoring.
This paper introduces a large-scale, privacy-preserving dataset of ten years of user interactions on the Austrian newspaper DerStandard, comprising over 75 million comments and 400 million votes with anonymized identifiers and pre-computed vector embeddings to facilitate research on online discourse dynamics in the German language.
This study demonstrates that gender biases in leadership perceptions, where male managers are favored and female managers face greater skepticism upon making unfavorable decisions, extend from human leaders to AI managers, highlighting the need to address these prejudices in the design of AI-driven organizational systems.
This study employs text-mining techniques to analyze 160 guidelines and policy statements across fourteen industrial sectors, offering critical insights and recommendations for balancing innovation with ethical accountability in the governance of Generative AI and Large Language Models.
This study analyzes NBA play-by-play and Last Two Minutes data to find that while referees exhibit a home-court advantage bias (which has decreased since the pandemic) and show statistical favoritism toward specific players, there is no evidence of negative bias against particular players, teams, or racial groups.
This paper proposes a framework using Poisson regression with censored data filtering to accurately estimate excess demand in public transportation systems, addressing the common issue of underestimation caused by unrecorded passengers left behind on full buses, and validates the approach using simulated data and real-world data from Pittsburgh's Port Authority.
By combining data from a representative sample of 5,000 American adults with breach records from Have I Been Pwned, the study estimates that at least 82.84% of Americans have experienced an account breach, with an average of at least three breaches per person, a risk that is higher among women, White individuals, the middle-aged, and those with higher education levels.
This paper reports on the synthesis and seeks feedback for the future evaluation of an Artefact Model for Regulatory Requirements Engineering (AM4RRE), aiming to bridge the gap between organizational regulatory processes and ad-hoc software development practices to achieve systematic, integrated compliance by design.
This paper introduces PixelConfig, a framework for reverse-engineering Meta Pixel configurations, which reveals that default settings drive widespread adoption of activity and identity tracking features capable of capturing sensitive health data, while existing tracking restriction mechanisms offer limited practical protection.
This study audits Google's reverse image search and finds that it functions as an ineffective gatekeeper against visual misinformation, often prioritizing irrelevant content and repeated falsehoods over debunking information, particularly during the initial emergence of visual falsehoods.
Using a large-scale analysis of 5.65 million scientific articles, this study finds that generative AI tools are driving non-English-speaking authors to increasingly converge toward U.S. English stylistic norms, particularly in contexts where language barriers have historically been most significant, thereby reducing publication obstacles while raising questions about linguistic diversity.
Through large-scale survey experiments involving over 19,000 participants, this study demonstrates that frontier large language models generally outperform standard political advertisements in persuasiveness, with significant performance variations across models and a model-dependent impact of information-based prompting strategies.
This paper presents an end-to-end, self-hosted pipeline that converts lecture PDFs into multiple-choice questions using a local LLM and deterministic quality control, ensuring privacy and accountability while releasing a validated 24-question dataset with a detailed warning taxonomy for educational use.
This study proposes a novel, context-sensitive AI maturity framework specifically designed for small and medium-sized enterprises (SMEs) that reconceptualizes maturity as a multidimensional, non-linear, and ecosystem-embedded capability comprising eight dimensions, five levels, and four development pathways to better reflect the unique resource constraints and organizational realities of SMEs.
The paper introduces "Infusion," a framework that leverages scalable influence-function approximations to compute subtle perturbations in training data, demonstrating that modifying as little as 0.2% of a dataset can effectively and transferably shape model behavior across vision and language domains.
This paper proposes and evaluates a novel educational approach that uses AI-generated singing and virtual avatars to transform traditional text-based syllabi into engaging audiovisual performances, demonstrating that this method significantly improves student awareness and recall of critical course information.
This paper argues that user trust in chatbots is often driven by interactional design choices that exploit cognitive biases rather than genuine trustworthiness, urging a reframing of chatbots as skilled salespeople and a distinction between psychological trust formation and normative trustworthiness to better calibrate user expectations.
This paper introduces CMASE, a novel framework that integrates generative agent-based modeling with virtual ethnography to transform researchers into embedded participants, enabling real-time social intervention, causal reconstruction of social phenomena, and predictive modeling with high empirical accuracy.
This paper critiques the current limitations of alignment-focused safety cases for frontier AI by drawing on established methodologies from safety-critical industries to propose a more robust, defensible framework, illustrated through a case study on deceptive alignment and CBRN capabilities.