2 citations · 2 across the 9 of their papers we have counts for
9 papers
Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems
Parand A. Alamdari, Toryn Q. Klassen, Sheila A. McIlraith
We examine one particular dimension of AI governance: how to monitor and audit AI-enabled products and services throughout the AI development lifecycle, from pre-deployment testing…
Ground-Compose-Reinforce: Grounding Language in Agentic Behaviours using Limited Data
Andrew C. Li, Toryn Q. Klassen, Andrew Wang +2
Grounding language in perception and action is a key challenge when building situated agents that can interact with humans, or other agents, via language. In the past, addressing t…
Pluralistic Alignment Over Time
Toryn Q. Klassen, Parand A. Alamdari, Sheila A. McIlraith
If an AI system makes decisions over time, how should we evaluate how aligned it is with a group of stakeholders (who may have conflicting values and preferences)? In this position…
Being Considerate as a Pathway Towards Pluralistic Alignment for Agentic AI
Parand A. Alamdari, Toryn Q. Klassen, Rodrigo Toro Icarte +1
Pluralistic alignment is concerned with ensuring that an AI system's objectives and behaviors are in harmony with the diversity of human values and perspectives. In this paper we s…
Policy Aggregation
Parand A. Alamdari, Soroush Ebadian, Ariel D. Procaccia
We consider the challenge of AI value alignment with multiple individuals that have different reward functions and optimal policies in an underlying Markov decision process. We for…
Jump Starting Bandits with LLM-Generated Prior Knowledge
Parand A. Alamdari, Yanshuai Cao, Kevin H. Wilson
We present substantial evidence demonstrating the benefits of integrating Large Language Models (LLMs) with a Contextual Multi-Armed Bandit framework. Contextual bandits have been…
Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making
Parand A. Alamdari, Toryn Q. Klassen, Elliot Creager +1
Fair decision making has largely been studied with respect to a single decision. Here we investigate the notion of fairness in the context of sequential decision making where multi…
Be Considerate: Objectives, Side Effects, and Deciding How to Act
Parand Alizadeh Alamdari, Toryn Q. Klassen, Rodrigo Toro Icarte +1
Recent work in AI safety has highlighted that in sequential decision making, objectives are often underspecified or incomplete. This gives discretion to the acting agent to realize…
Formal Methods with a Touch of Magic
Parand Alizadeh Alamdari, Guy Avni, Thomas A. Henzinger +1
Machine learning and formal methods have complimentary benefits and drawbacks. In this work, we address the controller-design problem with a combination of techniques from both fie…