AI Bibliography
AI Bibliography |
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Willis, L. E. (2020). Deception by design. Harvard Journal of Law & Technology, 34(1), 115–190. |
| Resource type: Journal Article BibTeX citation key: Willis2020 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Data Sciences, Decision Theory, Ethics, General, Law Subcategories: Behavioral analytics, Big data, Decision making, Game theory, Human decisionmaking, Machine learning, Psychology of human-AI interaction Creators: Willis Publisher: Collection: Harvard Journal of Law & Technology |
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| Abstract |
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Big data, ubiquitous tracking, and machine learning and other types of artificial intelligence increasingly shape business interactions with consumers. Through algorithms, businesses employ these tools to design advertising, sales portals, returnand cancellation processes, pricing, and even products and services themselves. Ultimately, these algorithms are programmed to optimizeprofit. At the same time, digital interfaces can exploit features of the online environment to manipulate and deceive, a phenomenon so common that the term “dark patterns”has been coined for it.Although dark patterns can be intentionally programmed, today’s machine learning systems can teach themselves to deceive people even when humans have not designed themto do so. One of this Article’s insights is that when deception of consumers is profitable, business communications and conduct designed by algorithms optimized only for profit will inevitably engage in deception.
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