arXiv AI

It's Complicated: On the Design and Evaluation of AI-Powered AAC Interfaces

arXiv:2606. 24854v1 Announce Type: cross Abstract: Artificial intelligence (AI) can enhance what people who use augmentative and alternative communication (AAC) are able to do with their systems.

arXiv Computation and Language
Sep 15

A Cross Community Agenda for Speech AI

arXiv:2609.13168v1 Announce Type: cross Abstract: Speech AI, any AI system that recognizes, transforms, or generates speech, is built and evaluated across two communities with only a small overlap: t...

By Maria Teleki, Kimi Wenzel, Anna Seo Gyeong Choi, Tobias Weinberg, Shree Harsha Bokkahalli Satish, Stephanny Sanchez, Belu Ticona, Ariadna Sanchez, Yash Sonkar, Aarti Mathur, Christoph Minixhofer, Abraham Glasser, Raja Kushalnagar, James Caverlee, Minha Lee, Shaomei Wu, Alyssa Hillary Zisk, \'Eva Sz\'ekely, Dylan Gaines, Angelika Seeschaaf Veres, Seray Ibrahim, Nicholas Cummins, Allison Koenecke
arXiv AI
Aug 17

AI Evaluation Should Work With Humans

arXiv:2608. 13577v1 Announce Type: new Abstract: This position paper argues that the dominant paradigm of AI evaluation (which focuses on superhuman autonomous performance and so implicitly targets the goal of replacing humans) is guiding AI development in the wrong direction.

By Jan Kulveit, Gavin Leech, Tom\'a\v{s} Gaven\v{c}iak, Raymond Douglas
arXiv AI
Aug 13

On Benchmarking Human-Like Intelligence in Machines

arXiv:2502. 20502v2 Announce Type: replace Abstract: Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as approximate models of human cognition.

By Lance Ying, Katherine M. Collins, Lionel Wong, Ilia Sucholutsky, Ryan Liu, Adrian Weller, Tianmin Shu, Thomas L. Griffiths, Joshua B. Tenenbaum
arXiv AI
Aug 11

Towards an Argumentative Foundation for Evaluative AI

arXiv:2608. 07473v1 Announce Type: new Abstract: Evaluative AI (EAI) has been recently proposed as a way to support human decision-making, not by producing a single recommendation, but by presenting competing hypotheses together with evidence for and against each.

By Xiang Yin, Tim Miller, Nico Potyka, Antonio Rago, Francesca Toni
arXiv AI
Jul 24

HARP: The Human--AI Research Platform

arXiv:2607. 20773v1 Announce Type: cross Abstract: Large language models (LLMs) have shifted human--computer interaction from `traditional'' interface journeys toward more conversational exchanges.

By Zeshu Zhu, Natalie Friedman, Kevin Weatherwax, Emily Eiben