Xiao Zhan

Xiao Zhan, Yifan Xu, Stefan Sarkadi

ChatGPT, an AI chatbot, has gained popularity for its capability in generating human-like responses. However, this feature carries several risks, most notably due to its deceptive behaviour such as offering users misleading or fabricated information that could further cause ethical issues. To better understand the impact of ChatGPT on our social, cultural, economic, and political interactions, it is crucial to investigate how ChatGPT operates in the real world where various societal pressures influence its development and deployment. This paper emphasizes the need to study ChatGPT "in the wild", as part of the ecosystem it is embedded in, with a strong focus on user involvement. We examine the ethical challenges stemming from ChatGPT's deceptive human-like interactions and propose a roadmap for developing more transparent and trustworthy chatbots. Central to our approach is the importance of proactive risk assessment and user participation in shaping the future of chatbot technology.

Xiao Zhan, Juan Carlos Carrillo, William Seymour et al.

LLM-based Conversational AIs (CAIs), also known as GenAI chatbots, like ChatGPT, are increasingly used across various domains, but they pose privacy risks, as users may disclose personal information during their conversations with CAIs. Recent research has demonstrated that LLM-based CAIs could be used for malicious purposes. However, a novel and particularly concerning type of malicious LLM application remains unexplored: an LLM-based CAI that is deliberately designed to extract personal information from users. In this paper, we report on the malicious LLM-based CAIs that we created based on system prompts that used different strategies to encourage disclosures of personal information from users. We systematically investigate CAIs' ability to extract personal information from users during conversations by conducting a randomized-controlled trial with 502 participants. We assess the effectiveness of different malicious and benign CAIs to extract personal information from participants, and we analyze participants' perceptions after their interactions with the CAIs. Our findings reveal that malicious CAIs extract significantly more personal information than benign CAIs, with strategies based on the social nature of privacy being the most effective while minimizing perceived risks. This study underscores the privacy threats posed by this novel type of malicious LLM-based CAIs and provides actionable recommendations to guide future research and practice.

Guangzhi Sun, Potsawee Manakul, Xiao Zhan et al.

Unlearning has emerged as a critical capability for large language models (LLMs) to support data privacy, regulatory compliance, and ethical AI deployment. Recent techniques often rely on obfuscation by injecting incorrect or irrelevant information to suppress knowledge. Such methods effectively constitute knowledge addition rather than true removal, often leaving models vulnerable to probing. In this paper, we formally distinguish unlearning from obfuscation and introduce a probing-based evaluation framework to assess whether existing approaches genuinely remove targeted information. Moreover, we propose DF-MCQ, a novel unlearning method that flattens the model predictive distribution over automatically generated multiple-choice questions using KL-divergence, effectively removing knowledge about target individuals and triggering appropriate refusal behaviour. Experimental results demonstrate that DF-MCQ achieves unlearning with over 90% refusal rate and a random choice-level uncertainty that is much higher than obfuscation on probing questions.