NLP folks are migrating to CHI. And so are ML practices. Just reviewed a paper that bolded their highest scoring intervention — no stats.
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Zana Buçinca
@zbucinca@hci.social
mastodon 4.7.3PhD Candidate @Harvard; Human-AI Interaction
135 Followers
100 Following
16 Posts
Joined November 09, 2022
New study on Lab in the Wild: Given descriptions of people, can you recommend exercises that suit their constraints, goals, preferences, and needs?
Compare yourself to other test takers!
http://ai.labinthewild.org/exercise-recommendation-sets-optimal/?v=v0&PLATFORM=litw
Someone just responded to an email I sent in December 2022.
I am the first person from Kosovo to do a PhD at Harvard, but my most impressive statement remains “I went to school and was friends with Dua Lipa”.
Spending a weekend reading tens of papers in another field, just to add one single sentence to my own: Decades of research in [this field] have found [X, Y, Z].
NLP researchers be like “old school” work and cite a 2023 paper.
AI assistance is impacting our decisions and the quality of our work. But how will this assistance affect us -- our skills, our growth and improvement, enjoyment, collaboration, or our agency in the workplace? The current design of AI assistance does not consider human-centric objectives; we need methods to account for them.
We propose offline RL as an approach for optimizing such human-centric objectives in AI-assisted decision-making.
Link to preprint: http://arxiv.org/abs/2403.05911
There’s more time to submit to TREW!
Due to popular demand, we’ve extended the submission deadline to March 1st.
Replying to
Our results show that AHA! generated sensible harms and that crowds and LLMs together generated significantly more diverse examples of harms than either crowd or LLM alone.
Via semi-structured interviews, we find that responsible AI experts find AHA! practically useful for AI impact assessment and beyond. But they raise concerns about practitioners overrelying on such tools. We discuss design implications for designing tools for AI impact assessment.
Preprint: https://arxiv.org/pdf/2306.03280.pdf
(2/2)
Replying to
Or envisioning futures in which we seek to enhance collaboration and relatedness in the workplace, such dynamic assistance may even at appropriate times advise decision-makers to seek insights from and consult other colleagues for complex decision-making scenarios.
Replying to
Here, we instantiated our approach with human-AI accuracy and human learning as two objectives.
Our results consistently show that people interacting with policies optimized for accuracy achieve significantly better accuracy and even human-AI complementarity in decision-making. Our results further indicate that human learning is more difficult to optimize than accuracy, with participants who interacted with learning-optimized policies showing significant learning improvement only at times.
Replying to
Our research demonstrates (offline) RL to be a promising approach to model dynamics of human-AI decision-making, leading to policies that may optimize various human-centric objectives and provide novel insights about the AI-assisted decision-making space. Our work emphasizes the importance of considering human-centric objectives along with accuracy while also opening up the novel research challenge of devising human-AI interaction techniques and explanations that support such objectives.
By now, I must have heard every joke there could ever exist when CS or HCI people struggle to make a projector work.
“How many CS people does it take …”
“Talk about more HCI research ..”
“Maybe we need an AI …” …