Deception by Design

76 Pages Posted: 21 Sep 2020 Last revised: 15 Jun 2021

Date Written: August 12, 2020

Abstract

Big data, ubiquitous tracking, and artificial intelligence are enabling businesses to disseminate rapidly proliferating permutations of digital marketing and sales materials, each of which can be micro-targeted to particular consumers in real time and space. When the algorithms that design and deliver these advertisements, websites, and apps are optimized only for profit and deception is profitable, consumers will be deceived. Yet at the same time that digital deception is becoming inevitable, it is racing toward immunity from liability. The accepted evidentiary methods for proving deception, from direct application of the reasonable person standard to controlled experiments demonstrating the deceptiveness of a defendant’s conduct, are neither practicable nor scientifically valid when applied to vast numbers of unique, micro-targeted communications. This article identifies and explains this emerging threat to the legal regulation of the consumer marketplace and suggests ways in which the law might give businesses sufficient incentive to engage in fair marketing by design.

Keywords: deception, unfair business practices, marketing, algorithms, machine learning, big data, unfair competition, consumer protection, evidence

JEL Classification: O33, O38, K42, K23, M38, M30, L81, L88

Suggested Citation

Willis, Lauren E., Deception by Design (August 12, 2020). Loyola Law School, Los Angeles Legal Studies Research Paper No. 2020-25, 34 Harvard Journal of Law & Technology 115 (2020), Available at SSRN: https://ssrn.com/abstract=3694575

Lauren E. Willis (Contact Author)

Loyola Law School Los Angeles ( email )

919 Albany Street
Los Angeles, CA 90015-1211
United States
213-736-1086 (Phone)
213-380-3769 (Fax)

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