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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 […]
A principled approach for data bias mitigation Read More »
In this interview series, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We sat down with Nitay Alon whose research is at the intersection of cognitive science and AI. We talked about the fascinating topic of Theory of Mind, how this plays out in deceptive environments, multi-agent
AI and Theory of Mind: an interview with Nitay Alon Read More »
In this interview series, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We sat down with Maxime Meyer to chat about his current research, future plans, and how he found the doctoral consortium experience. Could you start with an introduction to yourself, where you’re studying and the
Studying the properties of large language models: an interview with Maxime Meyer Read More »
In this interview series, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We sat down with Prakhar Ganesh to learn about his work on responsible AI, which is focussed on the concept of multiplicity. We found out more about some of the projects he’s been involved in,
Studying multiplicity: an interview with Prakhar Ganesh Read More »
Jamillah Knowles & Digit / Pink Office / Licenced by CC-BY 4.0 Abstract 2025 marked a pivotal shift in AI – from testing to deployment. This happened as generative and agentic systems became essential in key sectors worldwide. This feature highlights the major AI ethics and policy developments of 2025, and concludes with a forward-looking
Top AI ethics and policy issues of 2025 and what to expect in 2026 Read More »
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 explore multi-agent systems and collective decision-making, dive into neurosymbolic Markov models, and find out how robots can acquire skills through interactions with the physical world.
In this interview series, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We caught up with Oliver Chang whose research interests span deep reinforcement learning, autonomous vehicles, and explainable AI. We found out more about some of the projects he’s worked on so far, what drew him
Reinforcement learning applied to autonomous vehicles: an interview with Oliver Chang Read More »
In this interview series, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. Tanmay Ambadkar is researching the reward structure in reinforcement learning, with the goal of providing generalizable solutions that can provide robust guarantees and are easily deployable. We caught up with Tanmay to find out more
Image Credit: Jiajun Wu, Yunzhi Zhang, Hong-Xing Yu, Joy Hsu, Jiayuan Mao. Discovering Hybrid World Representations with Co-Evolving Foundation Models. In Proceedings of the Annual AAAI Conference on Artificial Intelligence, Emerging Trends in AI (ETA) Track, 2026. In the latest issue of AI Matters, a publication of ACM SIGAI, Ella Scallan caught up with Jiajun
Learning to see the physical world: an interview with Jiajun Wu Read More »
In the latest issue of AI Matters, a publication of ACM SIGAI, Ella Scallan caught up with Aishwarya Agrawal to find out more about her research, what most excites her about the future of AI, and advice for early career researchers. You were awarded an Honourable Mention for the 2019 AAAI / ACM SIGAI Doctoral
