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Jackson G. Lu

Publications and source records attributed to Jackson G. Lu.

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Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts

Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritization: we must understand which risks are most severe, who is most vulnerable, and who is most responsible for addressing them. We report results from a three-round Delphi study conducted late 2025 with 272 international AI experts. Experts rated 24 AI risks on harm probability and severity, sector and actor vulnerability, actor responsibility, and overall concern. Experts estimated the five most severe harms in the next 5 years were likely to come from dangerous capabilities, competitive dynamics, weapons & cyberattacks (including CBRNE), power centralization, and false information. In a business-as-usual scenario, experts judged 18 of 24 risks as having a more than 10% probability of catastrophic outcomes (e.g., more than 1 million deaths or more than USD 100B in financial loss) in the next 5 years (2025-2030). In a scenario where pragmatic mitigations are implemented, experts still judged five risks as having a more than 10% probability of catastrophic outcomes: dangerous capabilities, weapons & cyberattacks, environmental harm, inequality & unemployment, and power centralization. All 24 risks were judged as being more than 5% likely to cause catastrophic outcomes. AI users and the general public were judged the most vulnerable to these risks, but experts assigned the highest responsibility for addressing them to general-purpose AI developers and governance actors (including governments, regulators, and standards bodies). Across most risks, experts identified information, finance, and national security as the most vulnerable sectors. These findings can guide AI risk prioritization and clarify expert expectations about who should bear responsibility for mitigation.

cs.CY

Generative AI Use in Entrepreneurship: An Integrative Review and an Empowerment-Entrapment Framework

Despite the growing use of generative artificial intelligence (GenAI) in entrepreneurship, research on its impact remains fragmented. To address this limitation, we provide an integrative, entrepreneur-centered review of how GenAI influences entrepreneurs at each stage of the entrepreneurial process: (1) opportunity recognition and ideation, (2) opportunity evaluation and commitment, (3) resource assembly and mobilization, and (4) venture launch and growth. Based on our review, we propose the Empowerment-Entrapment Framework, which not only catalogs GenAI's benefits and costs throughout the entrepreneurial process, but also identifies potential trade-offs underlying GenAI's double-edged role. For example, GenAI may improve venture idea quality yet produce hallucinations and biases; boost entrepreneurial self-efficacy yet heighten overconfidence; increase functional breadth and self-sufficiency yet reduce prosociality; and enhance productivity yet diminish cognitive engagement, learning, and memory. We also identify features of GenAI that underlie these empowering and entrapping effects. Moreover, we explore boundary conditions that moderate GenAI's effects, including entrepreneurs' expertise, approaches to using GenAI, and personal traits and values. Beyond these theoretical contributions, our review and framework offer practical guidance for entrepreneurs seeking to use GenAI strategically while managing its risks.

cs.CY