AIES2025

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A principled approach for data bias mitigation

Scale and Charts Emojis by OpenMoji (CC BY-SA 4.0) via Streamline. How do you know if your data is fair? And if it isn’t, what can you do about it? Machine learning models are increasingly used to make high-stakes decisions, from predicting who gets a loan to estimating the likelihood that someone will reoffend. But […]

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Governing the rise of interactive AI will require behavioral insights

Clarote & AI4Media / AI Mural / Licenced by CC-BY 4.0 Interactive AI: From tool to companion AI is no longer just a translator or image recognizer. Today, we engage with systems that remember our preferences, proactively manage our calendars, and even provide emotional support. This is interactive AI. Unlike traditional software, these systems are:

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Interview with Anindya Das Antar: Evaluating effectiveness of moderation guardrails in aligning LLM outputs

In their paper presented at AIES 2025, “Do Your Guardrails Even Guard?” Method for Evaluating Effectiveness of Moderation Guardrails in Aligning LLM Outputs with Expert User Expectations, Anindya Das Antar, Xun Huan and Nikola Banovic propose a method to evaluate and select guardrails that best align LLM outputs with domain knowledge from subject-matter experts. Here,

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AIhub monthly digest: October 2025 – energy supply challenges, wearable sensors, and atomic-scale simulations

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we attend AIES and ECAI, learn about policy design for two-sided platforms, discover how to balance speed and physical laws in atomic-scale simulations, and find out

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Congratulations to the #AIES2025 best paper award winners!

The eighth AAAI / ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) is currently taking place in Madrid, Spain, running from 20-22 October. During the opening ceremony, the best papers for this year were announced. The four winners are: AI, Normality, and Oppressive Things Ting-an Lin and Linus Ta-Lun Huang Abstract: While it is

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