Providing accurate and actionable advice about phishing emails is challenging. The majority of advice is generic and hard to implement. Phishing emails that pass through filters and land in user inboxes are usually sophisticated and exploit differences between how humans and computers interpret emails. Therefore, users need accurate and relevant guidance to take the right action. This study investigates the effectiveness of guidance based on features extracted from emails, which even in AI-driven systems can sometimes be inaccurate, leading to poor advice. We examined three conditions: control (generic advice), perfect advice, and realistic advice, through an online survey of 489 participants on Prolific, and measured user accuracy and confidence in phishing detection with and without guidance. Our findings indicate that having advice specific to the email is more effective than generic guidance (control). Inaccuracies in the guidance can also impact user decisions and reduce detection accuracy.
https://dl.acm.org/doi/10.1145/3706598.3714267
Globally, small and medium-sized businesses (SMBs) have had to adapt to rapid digital changes, a shift accelerated by the COVID-19 pandemic. In Kenya, this transition has involved a significant move towards digital management tools. While many had already experienced marked digitalization over the last few decades, they completed this work differently from their European and North American counterparts. This study explores how Kenyan SMBs continue to navigate these changes and considers the potential of Generative AI in this context. Applying the concept of socio-tecture—which emphasizes social networks, relational business practices, and employees as knowledge producers—we analyze how these elements influence SMB operations in Nairobi. We highlight how socio-tecture affects business performance and growth, and discuss how an Afro-centric strengths-based approach might offer unique opportunities and challenges with the influx of new technologies like Generative AI.
https://dl.acm.org/doi/10.1145/3706598.3715019
Large Language Models (LLMs) are seemingly infiltrating every domain, and the legal context is no exception. In this paper, we present the results of three experiments (total N = 288) that investigated lay people's willingness to act upon, and their ability to discriminate between, LLM- and lawyer-generated legal advice. In Experiment 1, participants judged their willingness to act on legal advice when the source of the advice was either known or unknown. When the advice source was unknown, participants indicated that they were significantly more willing to act on the LLM-generated advice. The result of the source unknown condition was replicated in Experiment 2. Intriguingly, despite participants indicating higher willingness to act on LLM-generated advice in Experiments 1 and 2, participants discriminated between the LLM- and lawyer-generated texts significantly above chance-level in Experiment 3. Lastly, we discuss potential explanations and risks of our findings, limitations and future work.
https://dl.acm.org/doi/10.1145/3706598.3713470
Generative AI (GAI) technologies are disrupting professional writing, challenging traditional practices. Recent studies explore GAI adoption experiences of creative practitioners, but we know little about how these experiences evolve into established practices and how GAI resistance alters these practices. To address this gap, we conducted 25 semi-structured interviews with writing professionals who adopted and/or resisted GAI. Using the theoretical lens of Job Crafting, we identify four strategies professionals employ to reshape their roles. Writing professionals employed GAI resisting strategies to maximize human potential, reinforce professional identity, carve out a professional niche, and preserve credibility within their networks. In contrast, GAI-enabled strategies allowed writers who embraced GAI to enhance desirable workflows, minimize mundane tasks, and engage in new AI-managerial labor. These strategies amplified their collaborations with GAI while reducing their reliance on other people. We conclude by discussing implications of GAI practices on writers' identity and practices as well as crafting theory.
https://dl.acm.org/doi/10.1145/3706598.3714035
Generative AI could enhance scientific discovery by supporting knowledge workers in science organizations. However, the real-world applications and perceived concerns of generative AI use in these organizations are uncertain. In this paper, we report on a collaborative study with a US national laboratory with employees spanning Science and Operations about their use of generative AI tools. We surveyed 66 employees, interviewed a subset (N=22), and measured early adoption of an internal generative AI interface called Argo lab-wide. We have four findings: (1) Argo usage data shows small but increasing use by Science and Operations employees; Common current and envisioned use cases for generative AI in this context conceptually fall into either a (2) copilot or (3) workflow agent modality; and (4) Concerns include sensitive data security, academic publishing, and job impacts. Based on our findings, we make recommendations for generative AI use in science and other organizations.
https://dl.acm.org/doi/10.1145/3706598.3713827