71 citations · 291 across the 40 of their papers we have counts for
9 papers · 1 filter
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…