49 citations · 63 across the 4 of their papers we have counts for
7 papers · 1 filter
Co-audit: tools to help humans double-check AI-generated content
Andrew D. Gordon, Carina Negreanu, José Cambronero +9
Users are increasingly being warned to check AI-generated content for correctness. Still, as LLMs (and other generative models) generate more complex output, such as summaries, tab…
Exploring Perspectives on the Impact of Artificial Intelligence on the Creativity of Knowledge Work: Beyond Mechanised Plagiarism and Stochastic Parrots
Advait Sarkar
Artificial Intelligence (AI), and in particular generative models, are transformative tools for knowledge work. They problematise notions of creativity, originality, plagiarism, th…
Should Computers Be Easy To Use? Questioning the Doctrine of Simplicity in User Interface Design
Advait Sarkar
That computers should be easy to learn and use is a rarely-questioned tenet of user interface design. But what do we gain from prioritising usability and learnability, and what do…
Enough With "Human-AI Collaboration"
Advait Sarkar
Describing our interaction with Artificial Intelligence (AI) systems as 'collaboration' is well-intentioned, but flawed. Not only is it misleading, but it also takes away the credi…
User Perceptions of Automatic Fake News Detection: Can Algorithms Fight Online Misinformation?
Bruno Tafur, Advait Sarkar
Fake news detection algorithms apply machine learning to various news attributes and their relationships. However, their success is usually evaluated based on how the algorithm per…
"What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models
Michael Xieyang Liu, Advait Sarkar, Carina Negreanu +4
Code-generating large language models translate natural language into code. However, only a small portion of the infinite space of naturalistic utterances is effective at guiding c…