ΑΙhub.org
 

AAAI presidential panel – factuality and trustworthiness


by
14 July 2026



share this:

This image shows a pixelated room, it looks like a typical bedroom or office. Most of it is heavily pixelated, but a shelf, table and plant, windows and clock can be recognised. These are all outlined in yellow boxes. Elise Racine / Morning View / Licenced by CC-BY 4.0

The Future of AI Research report, published in March 2025, aims to clearly identify the trajectory of AI research in a structured way. The report was led by outgoing AAAI President Francesca Rossi and covers 17 different AI topics. Members of the report team, and other selected AI practitioners, are taking part in a series of video panel discussions covering selected chapters from the report.

In the sixth discussion in the collection, the three panellists tackle factuality and trustworthiness. Specifically, they cover the following topics:

  • Understanding factuality: why preventing false outputs from large language models remains AI’s toughest problem
  • Beyond accuracy: how trustworthiness encompasses understandability, robustness, and human values—essential for deploying AI in high-stakes environments
  • Practical solutions: explore proven approaches including fine-tuning, retrieval-augmented generation, output verification, and model simplification strategies

Panel Members

  • Oren Etzioni, TrueMedia.org, University of Washington
  • Henry Kautz, University of Virginia at Charlottesville
  • Kush R Varshney, IBM Fellow

Moderator

  • Francesca Rossi, AAAI past president, IBM Fellow and AI Ethics Global Leader


tags: , ,


Lucy Smith is Senior Managing Editor for AIhub.
Lucy Smith is Senior Managing Editor for AIhub.

            AUAI is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

Disappearing lakes and AI are helping scientists map Arctic permafrost thaw in near‑real time

  18 Sep 2026
Researchers created an interactive website to track permafrost thaw across the Arctic.

How much can fair budget-division rules resist manipulation?

The authors write about their award-winning IJCAI-ECAI paper: "Approximate Strategyproofness in Approval-based Budget Division".

AI dives into a sea of data, from plankton to pollution

  16 Sep 2026
“Faster and cheaper monitoring means problems like plankton decline, litter accumulation, oil spills and coral degradation can be picked up and acted on sooner."

Interview with Yash Saxena: how is external knowledge used in AI systems?

  15 Sep 2026
What happens to information from external sources as it moves through an AI system?

When AI art has no author: Study finds generated images often can’t be traced to training data

  14 Sep 2026
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.

AI in nature conservation: powerful tool or dangerous shortcut?

  11 Sep 2026
AI provides opportunity for future biodiversity conservation but introduces risks .

Improving the process for large-scale recommender systems: an interview with Haruka Kiyohara

  10 Sep 2026
Credit-assigned policy gradient for early stage retrieval in two-stage ranking.

The Machine Ethics podcast: Data Collective with E.M. Lewis-Jong

Ben chats to E.M. Lewis-Jong about the promise of AI and making human connection easier, speech recognition and supporting linguistic diversity, making useful technologies that have a purpose, and more.



AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence