17 entries
This article examines the friction that arises when users shift from exploratory conversation with AI tools to precise task delegation. Through direct observation of seven people working with AI, the author identifies three interaction styles (collaborative, commanding, over-explainers) and reveals how conversational fluency in AI interfaces creates false expectations of shared understanding, leading users to accept suggestions that drift from their original intent.
Annotation of So, Cheng & Krishna Murthy (2026). CHI EA '26. Proposes a spectrum of LLM interface metaphors from anti- to hyper-anthropomorphism to encourage critical engagement.
Capturing ideas from the Guildford session and shaping them into a talk.
Annotation of Masure (2019). Interface Critique no. 2. Argues against user-centered design as a reductive framing, tracing from Xerox Star through Don Norman to experience design.
China's proposed AI law acknowledges AI-related human vulnerabilities and establishes contextual technical measures to prevent AI harms | Edition #264
A peer-reviewed conference paper from DRS 2022 that examines how metaphors shape designers' understanding of machine learning and AI systems, exploring both where common metaphors mislead and what qualities make metaphors genuinely useful for design thinking.
Collects over 12,000 open-ended metaphor responses from a nationally representative US sample across 12 months, developing a systematic framework to quantify how people conceptualise AI. Finds that Americans increasingly perceive AI as warm, competent, and human-like, with attributions of warmth and human-likeness rising significantly in the year after ChatGPT's release. Demographic variation matters: women, older individuals, and people of colour are more likely to attribute human-like qualities, and these perceptions strongly predict trust and willingness to adopt AI.
Argues that the appeal of large language models is rooted in human interactional and interpretive processes, not in machine intelligence. Tracing a line from divination and ELIZA to present-day LLMs, Dingemanse follows Lucy Suchman in slowing down 'discourses of the smart machines' and shows how fluid output, fine-tuned overconfidence, and interactive design exploit our interpretive infrastructure. Critical AI literacy, the paper argues, must be grounded in a deep understanding of human interaction and sense-making.
Ben Wigler highlights a paper on emotion circuits in LLMs: discovery and control.
Reading notes on cognitive assemblages, anthropocentrism, and the case for extending cognition beyond human consciousness, from bacteria to AI.
A collection of thoughts & ideas on alternative metaphors for human-GenAI-interaction.
DALL-E 3 is still digitally challenged.
You built a conversational interface and then expected people to use it just for search?
Both can access the internet and retrieve recent information. Let's take them for a test drive.
Why does ChatGPT seem to 'know' things it shouldn't? The reason might be much more mundane than you think.
Part 1: you can call me Al. A conversation designer's first encounters with ChatGPT.
Facebook's AI chatbot might just be saying what you want it to say…