7 citations · 7 across the 7 of their papers we have counts for
7 papers
Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races
Phu Hoa Pham, Duy Minh Dao Sy, Trung Kiet Huynh +9
An AI development race creates a multi-agent safety dilemma. Each company can develop slowly and safely, or move faster while taking a risk that may remove its final reward. We use…
Falling Behind Drives Unsafe Development in an Idealised AI Race Experiment
Elias Fernández Domingos, The Anh Han
Technological races create tension between speed and safety: actors may gain by moving faster than competitors, even when risky development is harmful. This is prominent in debates…
Trust or Check? Understanding the (Evolutionary) Dynamics of User Trust in AI Systems
Adeela Bashir, Zhao Song, Ndidi Bianca Ogbo +21
As the capabilities and adoption of Artificial Intelligence (AI) systems grow, trust in these AI systems is an increasingly urgent concern. Much research has focused on models of A…
Can Media Act as a Soft Regulator of Safe AI Development? A Game Theoretical Analysis
Henrique Correia da Fonseca, António Fernandes, Zhao Song +15
When developers of artificial intelligence (AI) products need to decide between profit and safety for the users, they likely choose profit. Untrustworthy AI technology must come pa…
Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents
Alessio Buscemi, Daniele Proverbio, Paolo Bova +15
There is general agreement that fostering trust and cooperation within the AI development ecosystem is essential to promote the adoption of trustworthy AI systems. By embedding Lar…
Media and responsible AI governance: a game-theoretic and LLM analysis
Nataliya Balabanova, Adeela Bashir, Paolo Bova +15
This paper investigates the complex interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems. Using evolutionary game theory and large…