AINeutralarXiv – CS AI · Jun 97/10
🧠Researchers propose a Human-Centered Benchmarking Framework that evaluates driver monitoring AI models across accuracy, explainability, efficiency, and robustness—rather than accuracy alone. Testing four lightweight architectures on eye-state classification reveals that while models perform similarly on clean data, each excels in different dimensions, and critically, the top-ranked model fails under sensor noise by misclassifying closed eyes as open, a safety-critical vulnerability.
AIBearisharXiv – CS AI · Jun 236/10
🧠A academic paper explores the intersection of digital humanism and evolutionary design, examining how technical systems should be designed with human-centered values. The research identifies synergies between these concepts while highlighting tensions around autonomy, genuine versus simulated subjectivity, and how market-driven specialization undermines open technology development.
AINeutralarXiv – CS AI · Jun 196/10
🧠Researchers introduce GLARE, an LLM-based interactive system that translates natural language questions into SQL queries to make global explanations from AI vision models more accessible and usable. The system bridges the gap between complex, static explanation artifacts and human-centered interpretability by enabling users to ask targeted questions about model behavior without needing technical expertise.
AINeutralarXiv – CS AI · Jun 16/10
🧠Researchers propose a persona-based evaluation framework that replaces traditional monolithic AI benchmarking with diverse synthetic cognitive profiles to better capture cultural and demographic variability in human judgment. While generative models can instantiate these personas consistently, the study reveals systematic degradation in persona coherence over time, suggesting static alignment approaches are insufficient and dynamic regulatory mechanisms are needed.
AINeutralarXiv – CS AI · May 296/10
🧠The BEAMS Initiative establishes benchmarks to evaluate AI tools for modeling and simulation, ensuring they complement human expertise rather than replace it. Testing reveals that current AI-enabled modeling tools excel at discussion and qualitative tasks but struggle with causal reasoning and quantitative error correction, with performance varying significantly across different LLM implementations.
AIBullisharXiv – CS AI · May 296/10
🧠Researchers introduce E3AD, an emotion-aware vision-language-action model that enhances autonomous driving systems by interpreting passenger emotional states alongside driving commands. The framework combines semantic understanding with emotion detection (Valence-Arousal-Dominance model) and dual-pathway spatial reasoning to improve both trajectory planning and human-vehicle comfort alignment.
AINeutralarXiv – CS AI · May 16/10
🧠Researchers propose PecMan, a human-AI framework designed to optimize fairness, accuracy, and clinical workflow integration simultaneously in medical image analysis. The framework addresses the gap between high-performing AI diagnostic systems and their limited real-world adoption by balancing performance across diverse patient populations while respecting clinician workload constraints.
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AINeutralarXiv – CS AI · Apr 146/10
🧠Researchers introduce an interactive workflow combining Sparse Autoencoders (SAE) and activation steering to make AI explainability actionable for practitioners. Through expert interviews with debugging tasks on CLIP, the study reveals that activation steering enables hypothesis testing and intervention-based debugging, though practitioners emphasize trust in observed model behavior over explanation plausibility and identify risks like ripple effects and limited generalization.
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AINeutralarXiv – CS AI · Apr 146/10
🧠Researchers argue that Large Language Models lack explicit empathy mechanisms, systematically failing to preserve human perspectives, affect, and context despite strong benchmark performance. The paper identifies four recurring empathic failures—sentiment attenuation, granularity mismatch, conflict avoidance, and linguistic distancing—and proposes empathy-aware objectives as essential components of LLM development.
AINeutralarXiv – CS AI · Mar 126/10
🧠Researchers have developed the System Hallucination Scale (SHS), a human-centered tool for evaluating hallucination behavior in large language models. The instrument showed strong statistical validity in testing with 210 participants and provides a practical method for assessing AI model reliability from a user perspective.
AINeutralarXiv – CS AI · Mar 94/10
🧠Researchers conducted a qualitative study analyzing Human-in-the-Loop (HITL) themes in AI application development through diary studies and expert interviews. The study identified four key themes around AI governance, iterative refinement, system lifecycle constraints, and human-AI collaboration to guide future HITL framework design.
