Algorithm-Based Advertising: Unintended Effects and the Tricky Business of Mitigating Adverse Outcomes

Lambrecht Anja () and Tucker Catherine ()
Additional contact information
Lambrecht Anja: Professor of Marketing, London Business School, England
Tucker Catherine: Professor of Marketing, Massachusetts Institute of Technology, Cambridge, MA, USA

NIM Marketing Intelligence Review, 2021, vol. 13, issue 1, 24-29

Abstract: Some algorithms may have similar discriminatory tendencies to humans. The presented study investigates gender bias in social media advertising in the context of STEM careers. Results suggest that advertising algorithms are not gender-biased as such, but that economic forces in the background might lead to unintended, uneven outcomes. Spillover effects across industries make reaching some consumer segments more likely than others. A gender-neutral strategy is less likely to reach women because women are more likely to react to advertising. Therefore, targeting them is more expensive and economic forces unintentionally favor men. One potential solution could be running separate campaigns for men and women to target both demographic groups equally. However, anti-discrimination legislation in many countries does not allow companies to target employment ads to only one gender. So ironically, laws that are designed to avoid discrimination actually rule out a fairly simple way to correct the bias in online targeting on Facebook and other platforms, illustrating further need for policy guidance in this area.

Keywords: Algorithms; Ad Auctions; Discrimination; Gender; Bias; STEM (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/nimmir-2021-0004 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:gfkmir:v:13:y:2021:i:1:p:24-29:n:4

DOI: 10.2478/nimmir-2021-0004

Access Statistics for this article

NIM Marketing Intelligence Review is currently edited by Christine Kittinger-Rosanelli

More articles in NIM Marketing Intelligence Review from Sciendo
Bibliographic data for series maintained by Peter Golla ().