Violence is a significant public health issue. Interventions to reduce violence rely on data about where incidents occur. Cities have historically used incomplete law enforcement crime data, but many are shifting toward data collected from hospital patients via the Cardiff Model to form a more complete understanding of violence. Still, location data is wrought with issues related to completeness, quality, and privacy. For example, if a patient feels that sharing a detailed location may present them with additional risks, such as undesired police involvement or retaliatory violence, they may be unwilling or unable to share. Consequently, survivors of violence who are the most vulnerable may remain the most at risk. We have designed a user interface and mapping algorithm to confront these challenges and conducted an experiment with emergency department patients. The results indicate a significant improvement in location data obtained using the interface compared to the existing screening interview.
https://dl.acm.org/doi/10.1145/3706598.3714136
Non-consensual intimate media (NCIM) presents internet-scale harm to individuals who are depicted. One of the most powerful tools for requesting its removal is the Digital Millennium Copyright Act (DMCA). However, the DMCA was designed to protect copyright holders rather than to address the problem of NCIM. Using a dataset of more than 54,000 DMCA reports and over 85 million infringing URLs spanning over a decade, this paper evaluates the efficacy of the DMCA for NCIM takedown. Results show that for non-commercial requests, while more than half of URLs are deindexed from Google Search within 48 hours, the actual removal of content from website hosts is much slower. The median infringing URL takes more than 45 days to be removed from website hosts, and only 5.39% URLs are removed within the first 48 hours. Additionally, the most frequently reported domains for non-commercial NCIM are smaller websites, not large platforms. We stress the need for new laws that ensure a shorter time to takedown that are enforceable across big and small platforms alike.
https://dl.acm.org/doi/10.1145/3706598.3713334
Two-factor authentication is often recommended for increasing online security, and users often follow this by using their phones. If physical items become unavailable, there is a risk of losing access to the account due to missing authentication requirements. In such cases, users need a backup or help from the service. Previous work found no standardized approach to how services address this issue, assist users, or offer backup options. Until now, it is unclear how users handle backups and account recovery and what their expectations towards service providers are. To shed light on this, we conducted 16 interviews and a survey with 95 participants. We found that most had never considered how to access their accounts if the second factor was lost, and only a few had a backup plan. Instead, users often rely on website support, assuming that personal data will help them regain access. We give recommendations for services.
https://dl.acm.org/doi/10.1145/3706598.3714245
As Large Language Models become integral to decision-making, optimism about their power is tempered with concern over their errors. Users may over-rely on LLM advice that is confidently stated but wrong, or under-rely due to mistrust. Reliance interventions have been developed to help users of LLMs, but they lack rigorous evaluation for appropriate reliance. We benchmark the performance of three relevant interventions by conducting a randomized online experiment with 400 participants attempting two challenging tasks: LSAT logical reasoning and image-based numerical estimation. For each question, participants first answered independently, then received LLM advice modified by one of three reliance interventions and answered the question again. Our findings indicate that while interventions reduce over-reliance, they generally fail to improve appropriate reliance. Furthermore, people became more confident after making wrong reliance decisions in certain contexts, demonstrating poor calibration. Based on our findings, we discuss implications for designing effective reliance interventions in human-LLM collaboration.
https://dl.acm.org/doi/10.1145/3706598.3714097
In light of the increasing vulnerability of citizens against cyberattacks, we conducted three representative surveys with German citizens in 2021 (N=1,093), 2023 (N=1,011), and 2024 (N=1,004) to examine their cyber threat awareness, use of protective security measures, and preferred information channels. While our findings attest large proportions of the German population a high level of cyber threat awareness, many citizens feel inadequately informed about coping with cyberattacks and show little confidence in German security authorities to protect citizens and infrastructures. While age correlated with citizens' awareness and behavior, we only saw minor temporal differences between datasets. Finally, we provide design and policy implications for enhancing citizens' awareness of cyber threats and implementing security measures.
https://dl.acm.org/doi/10.1145/3706598.3713795
Bystander privacy has become a critical concern amidst the widespread activities of video sharing, engaging billions of users daily. Concerns arise when individuals inadvertently appear in public videos without consent. Existing methods for determining bystander permissions require significant adaptation and modifications by videographers and video sharing platforms, potentially limiting their adoption. This study explores leveraging platform censorship capabilities to enforce bystander privacy. We introduce selfFlag, a type of violative media signal designed to trigger automatic content flagging. Bystanders exhibiting such signals, captured in public videos, can be automatically identified and removed by platforms, thereby indirectly enforcing privacy preferences, primarily through the efforts of bystanders themselves. We conduct thorough measurements on current censorship practices, propose music-based triggering content, and develop an auxiliary tool for videographers to produce high-quality content with privacy compliance.
https://dl.acm.org/doi/10.1145/3706598.3713391