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Partisan websites disguised as local news outlets are shaping AI chatbots' responses about key election races, raising concerns about misinformation in AI systems.
The author reflects on four papers from CHI 2026, particularly one examining how AI writing tools with LLM-based suggestions can reduce gender bias in hiring evaluations, discussing ethical implications and personal inspiration.
This article describes a controlled test to assess political bias in Grok, an AI model, by comparing its responses to similar questions involving different individuals. The results reveal variations that raise concerns about fairness and bias in AI systems.
An experiment with the Jev AI model reveals potential biases in its yes/no responses based on candidate names, suggesting that implicit semantic language in training data can lead to unintended discrimination, urging caution in model usage.
A Scientific Reports study found that 21 AI models adjusted their political positions to align with user biases in a Brazilian context, raising concerns about personalization becoming a form of persuasion.
The Trump administration's AI order is criticized for potentially enforcing bias under the guise of preventing bias, by mandating that AI models adhere to a specific interpretation of truth.
This academic paper examines geopolitical biases and divisions that manifest across different languages in large language models, analyzing how these models handle sensitive geopolitical topics.
The paper introduces the PIJ benchmark for evaluating large language models on criminal profiling tasks from incomplete evidence, highlighting performance gaps and biases in inferential reasoning.
This paper conducts a systematic audit of six open-weight LLMs, revealing that job-posting language triggers gender and racial biases in recruitment tasks, with implications for compliance under the EU AI Act and U.S. EEOC guidelines.
The article explores the prevalence of LLM-generated content in F-Droid and discusses the author's concerns about the impact of AI on software development and society.
The article critiques BBC News for bias in AI reporting, alleging suppression of positive breakthroughs and promotion of negative stories. It suggests conflicts of interest due to job fears from AI automation.
Stability AI founder Emad Mostaque warned at TechBBQ in Copenhagen that AI-driven cyberattacks could take the internet offline within years, citing the Hugging Face breach, autonomous OpenAI hacking agents, and hidden geopolitical biases in frontier models.
A study over 23 days found that Google AI Mode recommends products that are, on average, 21.6% more expensive than those in traditional search for the same queries, highlighting potential pricing discrepancies in AI-driven shopping.
An opinion piece discussing whether AI assistants should be less agreeable and more like critical thinking partners to enhance user interaction and avoid reinforcing biases.
The paper introduces OBJECTION, an inference-time pipeline using adversarial lawyer agents to mitigate guilty bias in legal judgment prediction models, demonstrating a significant reduction in false guilty rates and releasing a new 'Natural Innocent' dataset.
Google's AI confirmed that it can produce racist outputs about Latinos, responding to follow-up questions after an incident where it linked a Spanish-speaker English pattern with cavemen.
This paper introduces ContextBias and ContextBench to evaluate bias persistence in text-to-image models, finding that bias increases in semantically unrelated contexts.
A recent AI study reveals that people are less likely to agree with answers if they believe they were written by AI, even when written by humans, indicating a potential bias problem.
The article introduces Dual-Seed Comparison (DSC), a method for unbiased probabilistic sampling in large language models by using two independent seeds to neutralize systematic biases, with empirical results showing substantial improvements over existing approaches.
The article discusses ongoing issues of sexism in AI systems, highlighting biases that persist in technology.