Social robots are a class of emerging smart consumer electronics devices that promise sophisticated experiences featuring emotive capabilities, artificial intelligence, conversational interaction, and more. With unique risk factors like emotional attachment, little is known on how social robots communicate these promises to consumers and whether they adequately deliver upon them within their overall product experiences prior to and during user interaction. Animated by a consumer protection lens, this paper systematically investigates manufacturer claims made for four commercially available social robots, evaluating these claims against the provided user experience and consumer reviews. We find that social robots vary widely in the manner and extent to which they communicate intelligent features and the supposed benefits of these features, while consumer perspectives similarly include a wide range of perceptions on robot and AI performance, capabilities, and product frustrations. We conclude by discussing social robots’ unique propensities for consumer risk, and consider implications for regulators, developers, and researchers of social robots.

In recent years, "gig work" platforms have gained popularity as a way for individuals to earn money; as of 2021, 16% of Americans have at some point earned money from such platforms. Despite their popularity and their history of unfair data collection practices and worker safety, little is known about the data collected from workers (and users) by gig platforms and about the privacy dark pattern designs present in their apps. This paper presents an empirical measurement of 16 gig work platforms' data practices in the U.S. We analyze what data is collected by these platforms, and how it is shared and used. Finally, we consider how these practices constitute privacy dark patterns. To that end, we develop a novel combination of methods to address gig-worker-specific challenges in experimentation and data collection, enabling the largest in-depth study of such platforms to date. We find extensive data collection and sharing with 60 third parties—including sharing reversible hashes of worker Social Security Numbers (SSNs)—along with dark patterns that subject workers to greater privacy risk and opportunistically use collected data to nag workers in off-platform messages. We conclude this paper with proposed interdisciplinary mitigations for improving gig worker privacy protections. After we disclosed our SSN-related findings to affected platforms, the platforms confirmed that the issue had been mitigated. This is consistent with our independent audit of the affected platforms. Analysis code and redacted datasets will be made available to those who wish to reproduce our findings.

Internet-of-Things (IoT) devices are ubiquitous, but little attention has been paid to how they may incorporate dark patterns despite consumer protections and privacy concerns arising from their unique access to intimate spaces and always-on capabilities. This paper conducts a systematic investigation of dark patterns in 57 popular, diverse smart home devices. We update manual interaction and annotation methods for the IoT context, then analyze dark pattern frequency across device types, manufacturers, and interaction modalities. We find that dark patterns are pervasive in IoT experiences, but manifest in diverse ways across device traits. Speakers, doorbells, and camera devices contain the most dark patterns, with manufacturers of such devices (Amazon and Google) having the most dark patterns compared to other vendors. We investigate how this distribution impacts the potential for consumer exposure to dark patterns, discuss broader implications for key stakeholders like designers and regulators, and identify opportunities for future dark patterns study.

Dark patterns are user interface elements that can influence a person's behavior against their intentions or best interests. Prior work identified these patterns in websites and mobile apps, but little is known about how the design of platforms might impact dark pattern manifestations and related human vulnerabilities. In this paper, we conduct a comparative study of mobile application, mobile browser, and web browser versions of 105 popular services to investigate variations in dark patterns across modalities. We perform manual tests, identify dark patterns in each service, and examine how they persist or differ by modality. Our findings show that while services can employ some dark patterns equally across modalities, many dark patterns vary between platforms, and that these differences saddle people with inconsistent experiences of autonomy, privacy, and control. We conclude by discussing broader implications for policymakers and practitioners, and provide suggestions for furthering dark patterns research.

In this paper we propose a framework for conceptualizing and demonstrating a good-faith effort when developing autonomous systems. The framework addresses two fundamental problems facing autonomous systems: (1) the disconnect between human-mental models and machine-based sensors and algorithms; and (2) unpredictability in complex systems. We address these problems using a mix of education - explicitly delineating the mapping between human concepts and their machine equivalents in a structured manner - and data sampling with expected ranges as a testing mechanism.

Consumer robots like personal digital assistants, automated cars, robot companions, chore-bots, and personal drones raise common consumer protection issues, such as fraud, privacy, data security, and risks to health, physical safety, and finances. They also raise new consumer protection issues, or at least call into question how existing consumer protection regimes might be applied to such emerging technologies. Yet it is unclear which legal regimes should govern these robots and what consumer protection rules for robots should look like.

The rapid decline in size and cost of networked sensors combined with increased incentives for use including monitoring physical fitness, improving public safety, increasing security, and adding convenience is causing the physical and online worlds to become heavily instrumented. Some welcome such developments, but others seek to retain privacy, often by focusing on countering the sensors themselves. Scholars have begun to consider surveillance countermeasures as a stand-alone area of research. However, a scholarly taxonomy useful for critical analysis and systematic countermeasure development is lacking. In this paper we provide such a taxonomy illustrated with example countermeasures that have been successfully employed.

The management of group context in socially mediating technologies is an important challenge for the design community. To better understand how users manage group context, we explored the practice of multiple profile management in social media. In doing so, we observed creative and opportunistic strategies for group context management. We found that multiple profile maintenance is motivated by four factors: privacy, identity, utility, and propriety. Drawing on these motives, we observe a continuum of boundary regulation behaviors: pseudonymity, practical obscurity, and transparent separation. Based on these findings, we encourage designers of group context management systems to more broadly consider motives and practices of group separations in social media. Group context management systems should be privacy-enhancing, but a singular focus on privacy overlooks a range of other group context management practices.