Maai & AI

Tagged: ai-transparency

8 entries

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Bot or not: Can people tell the difference between stories written by a human or by an AI system? | Judgment and Decision Making | Cambridge Core

This empirical study examines whether people can reliably distinguish between stories written by humans versus generated by AI systems. The research investigates the cognitive and perceptual barriers that affect our ability to detect machine-generated text in narrative contexts, with implications for trust, transparency, and how we evaluate authenticity in written communication.
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State of RAG & GenAI

This article argues that Retrieval Augmented Generation (RAG) has become a strategic imperative for enterprises in 2026, addressing critical challenges like LLM hallucinations, outdated outputs, and high retraining costs. RAG bridges the gap between large language models and organizational knowledge by retrieving verified, real-time data at the moment of generation, ensuring outputs are accurate, compliant, and trustworthy without requiring constant model retraining.
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Don't show me your AI. It is rude!

Marek Tuszynski (Tactical Tech) argues that the synthetic intimacy people develop with generative AI tools (an 'ELIZA effect') makes critical conversation about AI almost impossible: dependency forecloses critique. The piece curates 30 resources mapping AI's labor exploitation, environmental costs, military use, and concentration of power, framing AI critique as a moral and political project rather than a technical one.