arXiv AI
Jun 6
Assessing the Geographic Diversity of AI's Platial Representations in Image Generation
arXiv:2606. 05188v1 Announce Type: cross Abstract: (Gen)AI diversity is not merely an ethical issue.
Geographic Bias and Diversity in AI Evaluation
arXiv:2606. 05187v1 Announce Type: cross Abstract: Among the many challenges hindering the responsible development and deployment of AI, arguably none has faced more intense scrutiny than bias in its various forms.
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arXiv AI
Jun 12
Competition and Diversity in Generative AI
arXiv:2412. 08610v3 Announce Type: replace-cross Abstract: Recent evidence, both in the lab and in the wild, suggests that the use of generative artificial intelligence reduces the diversity of content produced.
arXiv Machine Learning
Jul 31
Epistemic diversity across language models mitigates knowledge collapse
arXiv:2512. 15011v3 Announce Type: replace Abstract: Artificial intelligence (AI) increasingly generates the very content used to train future AI systems.
arXiv AI
Aug 19
Evaluating the Diversity of AI-Generated Content with Diversity Profiles
The paper argues that measuring diversity in AI-generated content using a single scalar score is inherently ambiguous and often misleading. It reviews existing diversity metrics, demonstrates their limitations through axiomatic and empirical analyses, and introduces diversity profiles—curve-valued, condition-aware summaries that evaluate diversity across a range of thresholds, scales, exponents, or orders. These profiles reveal whether comparisons are robust across resolutions or depend on arbitrary parameter choices, offering a more transparent framework for generative AI evaluation.
arXiv Machine Learning
Jun 2
Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities
arXiv:2605. 04127v2 Announce Type: replace Abstract: Model collapse, the degradation in performance that arises when generative models are trained on the outputs of prior models, is an increasing concern as artificially generated content proliferates.
arXiv AI
Jul 1
Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models
arXiv:2410. 12341v4 Announce Type: replace-cross Abstract: As AI-generated content increasingly populates the web, generative AI models are at growing risk of being trained on their own outputs, a process known as AI autophagy.
arXiv AI
Jun 2
"I've Seen How This Goes": Characterizing Diversity via Progressive Conditional Surprise
arXiv:2606. 01811v1 Announce Type: cross Abstract: Measuring the diversity of creative outputs is central to evaluating post-training mode collapse, comparing decoding strategies, and quantifying creative behavior in both AI and human writing.
arXiv AI
Sep 1
Emergent Misalignment Is Not Magical
arXiv:2608.29118v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) on narrowly harmful datasets can lead to misalignment broadly, a phenomenon known as emergent misalignment (EM...
arXiv AI
Sep 23
Biased AI improves human performance but reduces perceived helpfulness
The study tests deliberately biased AI assistants and finds that such bias improves human performance on tasks like misinformation evaluation, financial investment, and graduate education compared to neutral AI. However, participants undervalue biased AI and overvalue neutral AI, even when performance is similar. When two AI biases flank a participant’s perspective, performance gains are maintained while reducing the perceived cost and one‑sided influence.
arXiv AI
Jul 7
Large Language Models Develop Novel Social Biases Through Adaptive Exploration
arXiv:2511. 06148v4 Announce Type: replace-cross Abstract: As large language models (LLMs) are adopted into frameworks that grant them the capacity to make real decisions, it is increasingly important to ensure that they are unbiased.
arXiv Machine Learning
Sep 4
Inferred Generative-Process Diversity Predicts Correlated Failure Across Language Models
The paper introduces a new measure of generative‑process diversity for language models, using Normalised Compression Distance on raw outputs after controlling for permutation effects. Across 38 models, this metric uncovers population structure that semantic similarity misses and predicts lower correlated failures across ten benchmark families, independent of semantic similarity or model capability. The authors argue that higher generative‑process diversity reduces correlated failures in multi‑model systems, offering a practical tool for safety‑relevant applications.
arXiv AI
Jun 26
Benchmarking Open-Weight Foundation Models for Global AI Technical Governance
arXiv:2606. 26099v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in artificial intelligence (AI) governance analysis across national and international organisations.