71 citations · 291 across the 40 of their papers we have counts for
44 papers
Agentic Economies for Autonomous Scientific Discovery
Nenad Tomasev, Matija Franklin, Atoosa Kasirzadeh +4
Recent advances in agentic Artificial Intelligence (AI) systems have marked a shift in AI for Science: moving away from the use of individual AI systems for narrow task execution,…
greCAPTCHA: Assessing Understanding as Evidence of Research Authorship Under Generative AI
Justin Payan, Bálint Gyevnár, Atoosa Kasirzadeh +1
Conferences, journals, funders, schools, and universities are struggling with a surge of potentially AI-generated submissions from ostensibly human authors, who may not have exerci…
AI in Science: Early Insights
Mihai Codreanu, Alex Imas, Juan Mateos-Garcia +15
Scientific progress is a key driver of economic growth and prosperity. There is great excitement - but also concerns - about the impacts of AI on science, but so far little data. W…
A Roadmap to Impactful Pluralistic Alignment Research
Elinor Poole-Dayan, Jillian Fisher, Atoosa Kasirzadeh +3
Much research on responsible AI aims to change the behavior of AI systems that billions of people use. Pluralistic value alignment---the goal of building AI systems that represent…
Distributed Denial of Science: How Indirect Data Poisoning of AI Systems Can Industrialize Scientific Fraud
Bálint Gyevnár, Atoosa Kasirzadeh, Nihar B. Shah
Scientific fraud is the instrument of doubt that malicious entities can use to establish controversy in science. Historically, it required the resources of a company: deep pockets,…
The Case for Globally Beneficial Technology
Iason Gabriel, Atoosa Kasirzadeh
To whom do the fruits of advanced technological innovation belong? To their inventors, to the organizations and individuals involved in making such discoveries possible, or to stil…
AI Coding Agents Can Reproduce Social Science Findings
Meysam Alizadeh, Mohsen Mosleh, Fabrizio Gilardi +2
Recent anecdotal evidence suggests that AI coding agents can reproduce published findings when provided with original data and code; yet systematic evaluation across social science…
Bridging the Gap in the Responsible AI Divides
Bálint Gyevnár, Atoosa Kasirzadeh
Tensions between AI Safety (AIS) and AI Ethics (AIE) have created what we term a "responsible AI divide." This divide has been persistent, and has dominated the public and policy d…
International AI Safety Report 2026
Yoshua Bengio, Stephen Clare, Carina Prunkl +89
The International AI Safety Report 2026 synthesises the current scientific evidence on the capabilities, emerging risks, and safety of general-purpose AI systems. The report series…
Legal Alignment for Safe and Ethical AI
Noam Kolt, Nicholas Caputo, Jack Boeglin +14
Alignment of artificial intelligence (AI) encompasses the normative problem of specifying how AI systems should act and the technical problem of ensuring AI systems comply with tho…
Full-Stack Alignment: Co-Aligning AI and Institutions with Thick Models of Value
Joe Edelman, Tan Zhi-Xuan, Ryan Lowe +30
Beneficial societal outcomes cannot be guaranteed by aligning individual AI systems with the intentions of their operators or users. Even an AI system that is perfectly aligned to…
EVALUESTEER: Measuring Reward Model Steerability Towards Values and Preferences
Kshitish Ghate, Andy Liu, Devansh Jain +5
As large language models (LLMs) are deployed globally, creating pluralistic systems that can accommodate the diverse preferences and values of users worldwide becomes essential. We…
Generative Value Conflicts Reveal LLM Priorities
Andy Liu, Kshitish Ghate, Mona Diab +3
Past work seeks to align large language model (LLM)-based assistants with a target set of values, but such assistants are frequently forced to make tradeoffs between values when de…
AI, Digital Platforms, and the New Systemic Risk
Philipp Hacker, Lilian Edwards, Atoosa Kasirzadeh
As artificial intelligence (AI) becomes increasingly embedded in digital, social, and institutional infrastructures, and AI and platforms are merged into hybrid structures, systemi…
The More You Automate, the Less You See: Hidden Pitfalls of AI Scientist Systems
Ziming Luo, Atoosa Kasirzadeh, Nihar B. Shah
AI scientist systems, capable of autonomously executing the full research workflow from hypothesis generation and experimentation to paper writing, hold significant potential for a…
Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants
Zeyu Tang, Alex John London, Atoosa Kasirzadeh +4
Algorithmic fairness research has largely framed unfairness as discrimination along sensitive attributes. However, this approach limits visibility into unfairness as structural inj…
Characterizing AI Agents for Alignment and Governance
Atoosa Kasirzadeh, Iason Gabriel
The creation of effective governance mechanisms for AI agents requires a deeper understanding of their core properties and how these properties relate to questions surrounding the…
Multi-Agent Risks from Advanced AI
Lewis Hammond, Alan Chan, Jesse Clifton +41
The rapid development of advanced AI agents and the imminent deployment of many instances of these agents will give rise to multi-agent systems of unprecedented complexity. These s…
AI Safety for Everyone
Balint Gyevnar, Atoosa Kasirzadeh
Recent discussions and research in AI safety have increasingly emphasized the deep connection between AI safety and existential risk from advanced AI systems, suggesting that work…
The Only Way is Ethics: A Guide to Ethical Research with Large Language Models
Eddie L. Ungless, Nikolas Vitsakis, Zeerak Talat +5
There is a significant body of work looking at the ethical considerations of large language models (LLMs): critiquing tools to measure performance and harms; proposing toolkits to…
A Taxonomy of Systemic Risks from General-Purpose AI
Risto Uuk, Carlos Ignacio Gutierrez, Daniel Guppy +5
Through a systematic review of academic literature, we propose a taxonomy of systemic risks associated with artificial intelligence (AI), in particular general-purpose AI. Followin…
Democratic AI is Possible. The Democracy Levels Framework Shows How It Might Work
Aviv Ovadya, Kyle Redman, Luke Thorburn +11
This position paper argues that effectively "democratizing AI" requires democratic governance and alignment of AI, and that this is particularly valuable for decisions with systemi…
Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models
Eddie L. Ungless, Nikolas Vitsakis, Zeerak Talat +5
This whitepaper offers an overview of the ethical considerations surrounding research into or with large language models (LLMs). As LLMs become more integrated into widely used app…
Measurement challenges in AI catastrophic risk governance and safety frameworks
Atoosa Kasirzadeh
Safety frameworks represent a significant development in AI governance: they are the first type of publicly shared catastrophic risk management framework developed by major AI comp…