Human advisors increasingly have access to AI recommendations and use them to shape the advice they give clients, often without disclosing their AI use to their clients. Disclosure of AI use involves informing individuals when AI is being used on their behalf by another person, such as disclosing to clients that advisors are using AI to formulate their expert advice. Our study aims to investigate whether and how disclosing advisors' use of AI assistance to their clients influences the advisors' perceived responsibility for decisions and degree of AI reliance. Recruiting financial advisors to perform a personal finance advising simulation that manipulated whether clients would know that advisors used an AI system (disclosed vs. not disclosed), we found that financial advisors felt less responsible for their recommendations when they believed their use of AI assistance would be disclosed rather than undisclosed to clients. We also found that advisors' perceived personal responsibility was higher when their reliance on AI was lower. Advisors' perceived self-competence increased their perceived personal responsibility for investment decisions relative to AI, and their trust in AI decreased their perceived responsibility. We conclude by discussing how our findings can inform disclosure schemes for improved human-AI collaboration in advising.

Remote Patient Monitoring (RPM) devices transmit patients' medical indicators (e.g., blood pressure) from the patient's home testing equipment to their healthcare providers, in order to monitor chronic conditions such as hypertension. AI systems have the potential to enhance access to timely medical advice based on the data that RPM devices produce. In this paper, we report on three studies investigating how the severity of users' medical condition (normal vs. high blood pressure), security risk (low vs. modest vs. high risk), and medical advice source (human doctor vs. AI) influence user perceptions of advisor trustworthiness and willingness to disclose RPM-acquired information. We found that trust mediated the relationship between the advice source and users' willingness to disclose health information: users trust doctors more than AI and are more willing to disclose their RPM-acquired health information to a more trusted advice source. However, we unexpectedly discovered that conditional on trust, users disclose RPM-acquired information more readily to AI than to doctors. We observed that the advice source did not influence perceptions of security and privacy risks. We conclude by discussing how our findings can support the design of RPM applications.

People regularly rely on social support from family, friends, and the public when mitigating security and privacy risks, even if mainstream technologies hardly support these interactions. In this paper, we evaluated Meerkat, a mobile application that allows users to receive support through screenshot capturing, marking, and messaging. In a field experiment (n = 65), we tested how Meerkat helps users face phishing attempts and examined it by receiving help from close social connections and community volunteers. Our findings show that while users could learn from both types of helpers, they were significantly more willing to rely on advice from close connections. We evaluate several criteria for successful support interactions, showing that learning is significantly correlated with specific properties of the support interaction, such as the length of the messages. We conclude the paper by discussing how our findings can be used to design community-based applications.

Older adults face increased safety challenges, such as targeted online fraud and phishing, contributing to the growing technological divide between them and younger adults. Social support from family and friends is often the primary way older adults receive help, but it may also lead to reliance on others. We have conducted an exploratory study to investigate older adults' attitudes and experiences related to mobile social support technologies for mobile safety. We interviewed 18 older adults about their existing support experiences and used the think-aloud method to gather data about a prototype for providing social support during mobile safety challenges. Our findings point to the potential of mobile technology to increase older adults' ability to mitigate mobile safety challenges through active learning from close social connections. We discuss how to support technology can address helpers' intolerance and overcome the challenges of physical distance.

Privacy in Online Social Networks (OSNs) is a dynamic concept, contingent on changes in technology and usage norms. Social influence is a major avenue for adopting online behaviors in general and privacy practices in particular. In this study, we examine how the source of influence affects the perceived behavioral intention to adopt privacy behavior. Our findings are based on a randomized experiment (167 U.S.-based Amazon Mechanical Turk workers) using a custom Facebook application that collects feedback from participants regarding their intention to adopt privacy practices from different types of sources, including authoritative organizations and friends with varying tie strength correlative. Our results show that the source of social influence affects the susceptibility to adopt certain privacy behaviors and that there are different patterns of influence for security and privacy norms. More interestingly, susceptibility is modulated by the privacy perceptions of the user: users with high perceived behavioral control are more susceptible to peer influence. Additionally, we show that the intention to adopt privacy practices is correlated with the intention to further influence other people.

The way users manage access to their information and computers has a tremendous effect on the overall security and privacy of individuals and organizations. Usually, access management is conducted using a choice architecture , a behavioral economics concept that describes the way decisions are framed to users. Studies have consistently shown that the design of choice architectures, mainly the selection of default options, has a strong effect on the final decisions users make by nudging them toward certain behaviors. In this article, we propose a method for optimizing access control choice architectures in online social networks. We empirically evaluate the methodology on Facebook, the world's largest online social network, by measuring how well the default options cover the existing user choices and preferences and toward which outcome the choice architecture nudges users. The evaluation includes two parts: (a) collecting access control decisions made by 266 users of Facebook for a period of 3 months; and (b) surveying 533 participants who were asked to express their preferences regarding default options. We demonstrate how optimal defaults can be algorithmically identified from users’ decisions and preferences, and we measure how existing defaults address users’ preferences compared with the optimal ones. We analyze how access control defaults can better serve existing users, and we discuss how our method can be used to establish a common measuring tool when examining the effects of default options.