Soul and machine (learning)
Name
11002_2020_9538_ReferencePDF.pdf
Size
573.77 KB
Format
Adobe PDF
Checksum (MD5)
975b1ffe5e8b97cce7f97f2db2903a76
Author(s)
Proserpio, Davide
Hauser, John R
Liu, Xiao
Amano, Tomomichi
Burnap, Alex
Guo, Tong
Lee, Dokyun (
Lewis, Randall
Misra, Kanishka
Schwarz, Eric
Date Issued
August 27, 2020
Publisher
Springer US
Version
Author's final manuscript
Abstract
Abstract
Machine learning is bringing us self-driving cars, medical diagnoses, and language translation, but how can machine learning help marketers improve marketing decisions? Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to analyze rich media content, such as text, images, audio, and video. Examples of current marketing applications include identification of customer needs from online data, accurate prediction of consumer response to advertising, personalized pricing, and product recommendations. But without the human input and insight—the soul—the applications of machine learning are limited. To create competitive or cooperative strategies, to generate creative product designs, to be accurate for “what-if” and “but-for” applications, to devise dynamic policies, to advance knowledge, to protect consumer privacy, and avoid algorithm bias, machine learning needs a soul. The brightest future is based on the synergy of what the machine can do well and what humans do well. We provide examples and predictions for the future.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s11002-020-09538-4
