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Should Australia Introduce a Prohibition on Unfair Trading? Responding to Exploitative Business Systems in Person and Online


  • J. M. Paterson

    (The University of Melbourne)

  • E. Bant

    (University of Western Australia)


Australian consumer protection law contains broad and flexible prohibitions on misleading and unconscionable conduct in trade or commerce. Yet concerns have been raised that these prohibitions are unsuitable for responding to predatory business systems. These are businesses that, by design or operation, target consumers experiencing vulnerability to offer costly products ill-suited to their needs. This concern has arisen in response to prominent instances of products of dubious efficacy offered to marginalized communities. It has also arisen from concerns over the increasing potential for data-driven digital marketing to manipulate consumer choice by targeting with fine-grained accuracy consumer vulnerabilities. In response to these concerns, it has been suggested that the Australian Consumer Law should be reformed, by introducing a prohibition on “unfair trading” inspired by the general prohibitions on such conduct in the EU and USA. This paper explores the key considerations relevant in assessing the merits of this proposed statutory “transplant.” Ultimately, the paper is supportive of the proposed reform, while also recognizing its limits.

Suggested Citation

  • J. M. Paterson & E. Bant, 2021. "Should Australia Introduce a Prohibition on Unfair Trading? Responding to Exploitative Business Systems in Person and Online," Journal of Consumer Policy, Springer, vol. 44(1), pages 1-19, March.
  • Handle: RePEc:kap:jcopol:v:44:y:2021:i:1:d:10.1007_s10603-020-09467-9
    DOI: 10.1007/s10603-020-09467-9

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    References listed on IDEAS

    1. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "The Economics of Artificial Intelligence: An Agenda," NBER Books, National Bureau of Economic Research, Inc, number agra-1, January.
    2. Productivity Commission, 2008. "Review of Australia's Consumer Policy Framework," Inquiry Reports, Productivity Commission, Government of Australia, number 45.
    3. Agrawal, Ajay & Gans, Joshua & Goldfarb, Avi (ed.), 2019. "The Economics of Artificial Intelligence," National Bureau of Economic Research Books, University of Chicago Press, number 9780226613338, January.
    4. Chris Willett, 2010. "Fairness and Consumer Decision Making under the Unfair Commercial Practices Directive," Journal of Consumer Policy, Springer, vol. 33(3), pages 247-273, September.
    5. Jeannie Paterson & Gerard Brody, 2015. "“Safety Net” Consumer Protection: Using Prohibitions on Unfair and Unconscionable Conduct to Respond to Predatory Business Models," Journal of Consumer Policy, Springer, vol. 38(3), pages 331-355, September.
    6. Richardson, Megan & Bosua, Rachelle & Clark, Karin & Webb, Jeb & Ahmad, Atif & Maynard, Sean, 2017. "Towards responsive regulation of the Internet of Things: Australian perspectives," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 6(1), pages 1-14.
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