Erica Lam

Entangled Responsibilities: Bias, Reliability, and Human-AI Collaboration in Sociotechnical Systems

Past Event / By

Date: November 28, 2025 (Friday) Time: 13:30-15:00 Venue: Rm 10.13, Run Run Shaw Tower, Centennial Campus, HKU Speaker: Dr Erica Mealy, University of the Sunshine Coast Abstract: Artificial intelligence is increasingly embedded within sociotechnical systems—complex networks where human practices, institutional structures, and technological components interact. In these environments, ensuring the reliability of AI is not […]

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South China Morning Post Highlights Discussion on Deepseek’s Implications

News / By

Our recent seminar discussion on Deepseek – Unpacking the Technical, Financial, and Geopolitical Implications, has been published by the South China Morning Post in a feature article.  Read the full article: China’s DeepSeek shows US chip controls have failed as AI race ramps up: HKU scholars A video capturing the essence of our discussion has been

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What Was a Photograph?

Past Event / By

Date: November 22, 2024 (Friday) Speaker: Ms Sarah Jeong, Journalist Chair: Dr Frank Hong, The University of Hong Kong Abstract: Generative AI is now a ubiquitous feature of consumer electronics, incorporated even into smartphone photography. As photographs and other forms of media become trivially easy to doctor, the social consensus around what documentary evidence is

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AI Governance

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Regulatory Frameworks for AI:  Developing ethical guidelines and regulations for the development and deployment of AI systems. International Cooperation and Standards: Fostering international collaboration on AI governance to address global challenges and ensure ethical consistency. Public Engagement and Participation:  Involving the public in discussions and decision-making processes related to AI governance. Corporate Social Responsibility in AI: 

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AI Well-Being

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Defining AI Well-being:  How can we conceptualize and define well-being for artificial entities? What might constitute a flourishing or thriving state for an AI? Subjective Experiences of AI:  Can AI systems have subjective experiences, and if so, what are the ethical implications for their well-being? How can we understand and potentially measure their internal states?

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Model Evaluation

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Developing standardized benchmarks and evaluation metrics for AI systems Investigating techniques for detecting and mitigating biases in AI models Exploring methods for assessing the robustness and generalizability of AI models Studying the limitations and potential failure modes of AI systems Examining the role of human judgment in evaluating and validating AI models

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