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Technology ethics is increasingly at the forefront of human-computer interaction scholarship, with increasing visibility not only to end users of technology, but also regulators, technology practitioners, and platforms. The notion of “dark patterns” has emerged as one common framing of technology manipulation, describing instances where psychological or perceptual tricks are used to decrease user agency and autonomy. In this panel, we have assembled a group of highly diverse early-career scholars that have built a transdisciplinary approach to scholarship on dark patterns, engaging with a range of socio-technical approaches and perspectives. Panelists will discuss their methodological approaches, key research questions to be considered in this emerging area of scholarship, and necessary connections between and among disciplinary perspectives to engage with the diverse constituencies that frame the creation, use, and impacts of dark patterns. 

Machine learning datasets have elicited concerns about privacy, bias, and unethical applications, leading to the retraction of prominent datasets such as DukeMTMC, MS-Celeb-1M, and Tiny Images. In response, the machine learning community has called for higher ethical standards in dataset creation. To help inform these efforts, we studied three influential but ethically problematic face and person recognition datasets—Labeled Faces in the Wild (LFW), MS-Celeb-1M, and DukeMTMC— by analyzing nearly 1000 papers that cite them. We found that the creation of derivative datasets and models, broader technological and social change, the lack of clarity of licenses, and dataset management practices can introduce a wide range of ethical concerns. We conclude by suggesting a distributed approach to harm mitigation that considers the entire life cycle of a dataset.