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1 1 (November 4, 2022)

handle is hein.crs/govejhr0001 and id is 1 raw text is: Congr ssiona I inforrningi Lhe leia 'C November 4, 2022 What Hides in the Shadows: Deceptive Design of Dark Patterns Many consumers have encountered dark patterns online, but may not recognize their name or harmful impacts. A Federal Trade Commission (FTC) staff report describes dark patterns as design practices that trick or manipulate users into making choices they would not otherwise have made and that may cause harm. Examples include (1) subscriptions that, despite all efforts, seem impossible to cancel; (2) terms and conditions hidden at the bottom of webpages in tiny fonts; and (3) buttons with confusing phrasing that result in an accidental agreement or purchase (see Figure 1). Figure 1. Example of a Dark Pattern Source: CRS, adapted from Bryce Durbin, TechCrunch. Dark patterns are becoming increasingly pervasive online, which has raised consumer protection, privacy, and competition concerns in Congress. Overview of Dark Patterns Dark patterns deployed online can influence consumer behavior and decisionmaking through psychological, visual, emotional, or other tactics. Because dark patterns are often opaque and subtle, consumers may never realize the influence on their online behavior. This has led some scholars to raise concerns related to consumer autonomy, welfare, and protection. Dark patterns vary in appearance and prevalence across different industries, sites, apps, services, and contexts, so no uniform definition exists. Dark patterns may also harm competition. Some scholars argue dark patterns are anticompetitive since they erode consumer welfare and consumer choice. For example, dark patterns may inhibit consumers from switching to other market competitors or act to decrease price transparency by limiting price comparison through bundling items or different price metrics (e.g., products are grouped together and sold as a single unit, or products use different metrics such as price per unit compared to price per ounce). Dark patterns may also influence consumer purchasing decisions (e.g., false limited-time messages or countdown timers to purchase an item) or influence users to reveal personal information. They may also make it difficult for consumers to exercise agency over their online privacy (e.g., by requiring cumbersome procedures to opt out of data collection). Research has found that dark patterns disproportionately affect lower-income individuals and individuals with lower levels of educational attainment. A 2019 study found that dark patterns were present on 11% of popular e-commerce websites. Dark patterns are even more common in mobile apps: a 2020 study identified dark patterns on 95% of free Android apps in the U.S. Google Play Store. The growing prevalence of dark patterns may raise additional consumer protection concerns, especially as mobile e-commerce currently accounts for more than 70% of total e-commerce sales globally. Types of Dark Patterns The following represent a selection of common dark patterns: Preselection: Default selections that benefit the company (e.g., cookie consent banners that preselect to opt in to cookie tracking) Nagging: Repeated requests for certain consumer actions or denying the consumer's ability to permanently accept or decline (e.g., websites with disruptive pop-ups that continuously ask permission to send notifications) Hidden Information: Hiding important information from consumers (e.g., in lengthy terms of service or in small font) Subverting Privacy: Inducing consumers to provide more of their data than intended (e.g., online platforms that require users to provide information to gain access, or privacy settings that are difficult to utilize) Dark patterns may also contribute to the gamification of certain online services and addiction to online platforms. Gamification refers to the use of game-like design elements and rewards systems that may give rise to impulsive decisions, often found in financial trading and educational apps. Inducing consumers to watch the next recommended video through an auto-play feature that loads new content without user action or agreement may be another example of a dark pattern. This is of particular concern for children when shown age-inappropriate content. Advances in artificial intelligence, machine learning, and data collection and analysis techniques coupled with the use of dark patterns have raised additional concerns. Some scholars argue that companies' real-time experimentation, machine learning models, and A/B testing (which shows consumers two different versions of a user interface to allow comparison of the results) may enable and incentivize new micro-targeted dark patterns or algorithms optimized to induce specific online behavior.

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