This chapter discusses the implications of predictive analytics for consumer privacy and surveys the existing law that could reach predictive analytics in ecommerce. Part II summarizes the prevailing theoretical accounts of privacy. Some leading theorists define privacy as the individual’s ability to control what others know about him or her, while others identify privacy’s instrumental value for promoting personal dignity and autonomy in ways that are important for individual personality, healthy civic discourse, and democratic governance. Part III introduces predictive analytics and illustrates its potential uses in ecommerce. Predictive analytics predicts future behavior based on patterns of past behavior. Predictive analytics has myriad uses in ecommerce, but they can be grouped into four common categories: (1) targeted advertising; (2) price discrimination; (3) customer segmentation and (4) eligibility determinations. Part IV examines how using predictive analytics in ecommerce affects consumer privacy. Predictive analytics can impair privacy as control because consumers cannot know all of the predictive uses to which merchants will put their information, nor can they anticipate all of the future data that merchants will combine with theirs to build predictive models. Predictive analytics can also harm personal autonomy and dignity by relying on secret data to affect consumers’ participation in the marketplace and by institutionalizing latent societal discrimination. Part V examines how existing state and federal law could reach merchants’ use of predictive analytics in ecommerce, including the Federal Trade Commission Act, Children’s Online Privacy Protection Act, sectoral anti-discrimination laws such as the Equal Credit Opportunity Act and the Fair Housing Act, state insurance laws, and public accommodation laws.
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Section K: Consumer Protection
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Section L: Network Neutrality
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Section M: Regulated Industries Online
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Section N: Privacy
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Section O: Domain Names
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Section P: Taxation of Online Transactions
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