Zhenhui Jack Jiang
Prof. Zhenhui Jack JIANG
创新及资讯管理学
Professor of Innovation and Information Management
Padma and Hari Harilela Professor in Strategic Information Management
Director, AI Evaluation Lab

3917 8351

KK 804

Publications
当AI开始“一本正经地胡说八道”:全球大模型竞逐中的隐性风险

大语言模型如今正日益渗透到知识生产、商业分析、法律谘询与医疗决策等核心领域,一个过去常被视为技术性缺陷嘅问题,正迅速成为全球技术竞赛嘅新焦点:大语言模型嘅“幻觉”( Hallucination )——即模型喺缺乏事实依据时,仍然以极高“信心”畀出流畅、却虚假嘅答案。

华无惧芯战 AI发展赶美

人工智能(AI)日新月异,中国科企之成就令海外投资者对中资股刮目相看,迎来美国更强力的围堵,务求拖慢中国的AI技术发展。香港大学经管学院创新及资讯管理学教授兼夏利莱伉俪基金教授蒋镇辉表示,中国AI大模型的实力已领先全球,跟美国的差距也愈来愈近,华府的晶片管制不能完全解释两国AI技术差距,但接下来的挑战也非光靠砸钱就能弥补。

Privacy Concerns and Data Donations: Do Societal Benefits Matter?

Data donations, where individuals are encouraged to donate their personal information, have the potential to advance medical research and help limit the spread of pandemics, among other benefits. The decision to donate data is fundamentally a privacy decision. In this research, we build on the privacy calculus, a model describing privacy risks and benefits, and examine the impact of privacy concerns on data donation decisions, highlighting the role of societal benefits in privacy decisions. Based on two randomized experiments using the general context of data donation for medical research (experiment 1) and the specific context of data donation for COVID-19 research (experiment 2), we find that individuals who are highly concerned about privacy tend to donate less data (experiments 1 and 2). This effect holds under a variety of conditions and is consistent with prevailing research. However, this effect is contingent on the privacy calculus. When implicit or explicit societal benefits are perceived, particularly in the absence of privacy controls, the association between privacy concerns and data donation decisions is less salient, highlighting the significant role that societal benefits have in privacy decisions. We discuss the theoretical, practical, social, and ethical implications of these findings.

多模态人工智能模型:图像生成能力评测与安全挑战

人工智慧图像生成技术日新月异,推动市场营销、广告设计及艺术创作等领域的创新。港大经管学院创新及资讯管理学教授蒋镇辉早前联同其人工智慧大模型评测团队对22款主流AI模型进行评估,深入剖析不同AI模型在图像生成方面的表现及潜在风险。

Choosing to Discover the Unknown: The Effects of Choice on User Attention to Online Video Advertising

Online video platforms face the challenge of balancing the needs of their users with those of their advertisers. Although users typically prefer to have less intrusive ads, advertisers aim to effectively catch user attention. This paper investigates how the provision of ad choice affects the effectiveness of video advertising. We argue that allowing users to choose an ad to view may trigger a “conjecture-formation-and-confirmation” process that motivates users to pay more attention to the selected ad. Two online experiments and four laboratory experiments are conducted to test the theorized underlying mechanism of the ad choice effect. Study 1 finds when users are unfamiliar (versus familiar) with the content of ad options (i.e., they need to make conjectures about ad content), ad choice is more likely to increase user attention to the chosen ad. Study 2 and Study 3 show that the impact of ad choice on user attention is more likely to be positive when users are enabled to make conjectures about ad content, such as when choice options provide more relevant information about ad content. Study 4a and Study 4b provide more direct support for the underlying mechanism by showing that the ad choice effect is attenuated when users cannot form conjectures about ad content at the choice stage. Study 5 further demonstrates that the positive effect of ad choice is robust across different ad settings. Taken together, these studies show ad choice is more likely to boost the effectiveness of video advertising when the “conjecture-formation-and-confirmation” process is triggered.

中美大语言模型竞逐:全球视角下的机遇与挑战

自 ChatGPT 问世以来,大语言模型LLM(Large Language Model)迅速成为全球科技竞赛的焦点。LLM将被应用到越来越多领域,成为AI发展的新蓝海。目前,美国在基础技术开发和创新应用上具有明显优势,其模型往往在技术前沿上表现出色;而中国的模型更强调针对本土语言环境的优化和实际应用的适应性。如何在两国的竞争中找到平衡点并推动合作,将成为未来几年的发展关键。

The Impacts of Internet Monitoring on Employees’ Cyberloafing and Organizational Citizenship Behavior: A Longitudinal Field Quasi-Experiment

Many organizations have adopted internet monitoring to regulate employees’ cyberloafing behavior. Although one might intuitively assume that internet monitoring can be effective in reducing cyberloafing, there is a lack of research examining why the effect can occur and whether it can be sustained. Furthermore, little research has investigated whether internet monitoring can concurrently induce any side effects in employee behavior. In this paper, we conducted a longitudinal field quasi-experiment to examine the impacts of internet monitoring on employees’ cyberloafing and organizational citizenship behavior (OCB). Our results show that internet monitoring did reduce employees’ cyberloafing by augmenting employees’ perceived sanction concerns and information privacy concerns related to cyberloafing. The results also show that internet monitoring could produce the side effect of reducing employees’ OCB. Interestingly, when examining the longitudinal effects of internet monitoring four months after its implementation, we found that the effect of internet monitoring on cyberloafing was not sustained, but the effect on OCB toward organizations still persisted. Our study advances the literature on deterrence theory by empirically investigating both the intended and side effects of deterrence and how the effects change over time. It also has important broader implications for practitioners who design and implement information systems to regulate employee noncompliance behavior.

大模型企业加速抢占市场

截至7月底,国内共推出超300个大模型。经过大模型数量之争的上半场,下半场中国大模型该如何押宝?“持续稳定的政策支持、庞大的算力规模和广阔的应用场景是中国独特的竞争优势和巨大的发展潜力。”蒋镇辉教授认为,大模型将从数量到更重视应用端发展。

港大评测报告:英文语境国产AI模型多处劣势 文心一言中文通用能力跑输ChatGPT

生成式人工智能(AI)工具发展日新月异,香港大学经济及工商管理学院今日(12日)发表一项人工智能大语言模型评测综合排行榜,通过港大深圳研究院建立评分系统,比较十多款大 模型表现,显示由中国科企百度开发的「文心一言」,在中文语境下综合得分最高,但在「通用语言能力」却跑输「ChatGPT4-Turbo」,而大部分国产模型在英文 语境下表现均处于「稍微劣势」。