Patrick Gage Kelley

Jake Chanenson, Tara Matthews, Sunny Consolvo et al.

Online financial scams represent a long-standing and serious threat for which people seek help. We present a study to understand people's in situ motivations for engaging with scams and the help needs they express before, during, and after encountering a scam. We identify the main emotions scammers exploited (e.g., fear, hope) and characterize how they did so. We examine factors -- such as financial insecurity and legal precarity -- which elevate people's risk of engaging with specific scams and experiencing harm. We indicate when people sought help and describe their help-seeking needs and emotions at different stages of the scam. We discuss how these needs could be met through the design of contextually-specific prevention, diagnostic, mitigation, and recovery interventions.

Patrick Gage Kelley, Steven Rousso-Schindler, Renee Shelby et al.

Generative AI (GenAI) is a powerful technology poised to reshape Trust & Safety. While misuse by attackers is a growing concern, its defensive capacity remains underexplored. This paper examines these effects through a qualitative study with 43 Trust & Safety experts across five domains: child safety, election integrity, hate and harassment, scams, and violent extremism. Our findings characterize a landscape in which GenAI empowers both attackers and defenders. GenAI dramatically increases the scale and speed of attacks, lowering the barrier to entry for creating harmful content, including sophisticated propaganda and deepfakes. Conversely, defenders envision leveraging GenAI to detect and mitigate harmful content at scale, conduct investigations, deploy persuasive counternarratives, improve moderator wellbeing, and offer user support. This work provides a strategic framework for understanding GenAI's impact on Trust & Safety and charts a path for its responsible use in creating safer online environments.

Yujin Potter, Wenbo Guo, Zhun Wang et al.

The impact of frontier AI in cybersecurity is rapidly increasing. In this paper, we comprehensively analyze this trend through three distinct lenses: a quantitative benchmark analysis, a literature review, and an expert survey. We find that while AI is already widely used in attacks, its application in defense remains limited, especially in remediation and deployment. Aligned with these analyses, experts expect AI to continue favoring attackers over defenders, though the gap will gradually narrow. These findings underscore the urgent need to mitigate frontier AI's risks while closely monitoring emerging capabilities. We provide concrete calls-to-action regarding: the construction of new cybersecurity benchmarks, the development of AI agents for defense, the design of provably secure AI agents, the improvement of pre-deployment security testing and transparency, and the strengthening of user-oriented education and defenses. Our paper summary and blog are available at https://rdi.berkeley.edu/frontier-ai-impact-on-cybersecurity/.

Alexandra Klymenko, Stephen Meisenbacher, Patrick Gage Kelley et al.

The proliferation of AI has sparked privacy concerns related to training data, model interfaces, downstream applications, and more. We interviewed 25 AI developers based in Europe to understand which privacy threats they believe pose the greatest risk to users, developers, and businesses and what protective strategies, if any, would help to mitigate them. We find that there is little consensus among AI developers on the relative ranking of privacy risks. These differences stem from salient reasoning patterns that often relate to human rather than purely technical factors. Furthermore, while AI developers are aware of proposed mitigation strategies for addressing these risks, they reported minimal real-world adoption. Our findings highlight both gaps and opportunities for empowering AI developers to better address privacy risks in AI.

Deepak Kumar, Patrick Gage Kelley, Sunny Consolvo et al.

In this work, we demonstrate how existing classifiers for identifying toxic comments online fail to generalize to the diverse concerns of Internet users. We survey 17,280 participants to understand how user expectations for what constitutes toxic content differ across demographics, beliefs, and personal experiences. We find that groups historically at-risk of harassment - such as people who identify as LGBTQ+ or young adults - are more likely to to flag a random comment drawn from Reddit, Twitter, or 4chan as toxic, as are people who have personally experienced harassment in the past. Based on our findings, we show how current one-size-fits-all toxicity classification algorithms, like the Perspective API from Jigsaw, can improve in accuracy by 86% on average through personalized model tuning. Ultimately, we highlight current pitfalls and new design directions that can improve the equity and efficacy of toxic content classifiers for all users.

Patrick Gage Kelley, Yongwei Yang, Courtney Heldreth et al.

As the influence and use of artificial intelligence (AI) have grown and its transformative potential has become more apparent, many questions have been raised regarding the economic, political, social, and ethical implications of its use. Public opinion plays an important role in these discussions, influencing product adoption, commercial development, research funding, and regulation. In this paper we present results of an in-depth survey of public opinion of artificial intelligence conducted with 10,005 respondents spanning eight countries and six continents. We report widespread perception that AI will have significant impact on society, accompanied by strong support for the responsible development and use of AI, and also characterize the public's sentiment towards AI with four key themes (exciting, useful, worrying, and futuristic) whose prevalence distinguishes response to AI in different countries.