Dark patterns are design practices that undermine users' ability to make autonomous and informed choices in digital experiences. The EU Digital Services Act (DSA) seeks to protect users from such designs and their effects, with Article 25 prohibiting three autonomy violation types: deception, manipulation and distortion/impairment. Demonstrating such regulatory violations, however, requires design-oriented reasoning necessary to articulate why an observed design practice constitutes a specific autonomy violation type. This paper maps 59 known dark patterns onto the three autonomy violation types from the DSA and identifies eight new design factors which can help determine when a dark pattern violates autonomy. Our mapping of dark patterns to autonomy violations grounds ongoing regulatory debates in design while opening pathways for translational research that reimagines how HCI engages with the governance of design practices.

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.

Understanding the legally relevant factual basis of an event and conveying it through text is a key skill of legal professionals. This skill is important for preparing forms (e.g., insurance claims) or other legal documents (e.g., court claims), but often presents a challenge for laypeople. Current AI approaches aim to bridge this gap, but mostly rely on the user to articulate what has happened in text, which may be challenging for many. Here, we investigate the capability of large language models (LLMs) to understand and summarize events occurring in videos. We ask an LLM to summarize and draft legal letters, based on 120 YouTube videos showing legal issues in various domains. Overall, 71.7% of the summaries were rated as of high or medium quality, which is a promising result, opening the door to a number of applications in e.g. access to justice.

FemTech is an emerging industry offering products, software, and services to support women’s health and well-being. Within FemTech, mobile health applications (mHealth apps) are popular for managing menstruation, fertility, pregnancy, and menopause. Yet, these apps expose users to deceptive and misleading practices, which can be characterized as Dark Patterns in user experience (or dark UX). Dark UX in commercial FemTech mHealth apps is underexplored, leaving a critical gap in understanding how deceptive patterns manifest in intimate health contexts, the harms they cause, and how to address them. We crowd-source and thematically analyze user accounts of dark UX through user reviews from sixteen systematically selected FemTech mHealth apps. User-reported accounts of dark UX in FemTech mHealth apps reveal several problematic design patterns, which emphasize risks for minors and the need for more transparent design of FemTech mHealth apps. Based on our results, we outline recommendations for enhancing ethical UX design and furthering regulatory action in FemTech.

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.