Product details

Product details
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Subject category: Marketing
Published by: Harvard Business Publishing
Originally published in: 2020
Version: 25 July 2022
Revision date: 2-Aug-2022

Abstract

Supplement to the A case. This collection of exercises aims to teach students about 1) Targeting Policies; and 2) Algorithmic bias in marketing-implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B), (C), (D) Introduce algorithmic bias. The exercises are designed such that the issues of algorithmic bias and discrimination would emerge inductively, 'surprising' the students in the act of recommending a strategy that, inadvertently, is discriminating against customers who belong to minority groups. This is achieved via the combination of hands-on exercises, where students would make decisions based on data analyses and visualization, and in-class discussions, where students would defend their proposed strategies, discover the (discriminating) implications of those actions, and discuss possible solutions.

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Abstract

Supplement to the A case. This collection of exercises aims to teach students about 1) Targeting Policies; and 2) Algorithmic bias in marketing-implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B), (C), (D) Introduce algorithmic bias. The exercises are designed such that the issues of algorithmic bias and discrimination would emerge inductively, 'surprising' the students in the act of recommending a strategy that, inadvertently, is discriminating against customers who belong to minority groups. This is achieved via the combination of hands-on exercises, where students would make decisions based on data analyses and visualization, and in-class discussions, where students would defend their proposed strategies, discover the (discriminating) implications of those actions, and discuss possible solutions.

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