Dark Patterns in AI-Enabled Consumer Experiences
PI: Christo Wilson
Associate Professor, Khoury College of Computer SciencesNortheastern University
PI: David Choffnes
Associate Professor, Khoury College of Computer Sciences
Northeastern University
Framework component: Interaction
Artificial Intelligence (AI) techniques have reached a level of maturity where they are being adopted into consumer-facing services and devices. While AI-enabled devices and services may bring benefits to consumers and businesses, they also have the potential to incorporate dark patterns that cause harm. Dark patterns are interface designs that interfere with peoples’ decision-making processes and harm their autonomy. To date, there is very little work that examines the unique potential of AI to worsen existing classes of dark patterns, as well as facilitate entirely new classes of dark patterns that are specific to AI-enabled experiences.
We propose to address these gaps in knowledge by exploring the dark patterns that are present in AI-enabled consumer experiences. We will investigate applications of AI in consumer electronics (i.e., IoT devices), their voice assistants, and any third-party software such as voice-assistant skills or smartphone apps using a mixed-methods approach that combines (1) controlled, laboratory testing coupled with human annotation to identify new classes of dark patterns and construct ground-truth datasets of dark pattern prevalence; and (2) user studies, to understand peoples’ perceptions about AI dark patterns and whether they actually cause harm.
Key Personnel
Johanna Gunawan
Graduate Student, Cybersecurity
Northeastern University