Back to the ROI for ML

Thank you for tuning in to this audio only podcast presentation. This is week 101 of The Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for The Lindahl Letter is, “Back to the ROI for ML.”

One of the core topics within the ML space that I have studied happens to be return on investment or more to the point an examination of just how well spent money would be in the space. You can certainly spend money on machine learning and artificial intelligence related efforts as a part of a think tank, an independent lab, or a pure research institution. Within corporate spaces pure research and development is one thing, but generally to expend the precious capital resources of an institution, organization, or group you want to know that some type of return will be accumulated from those activities. It’s inherent to the nature of the venture into running a corporation compared to running some other type of organization.

ROI for ML is a topic that deserves consideration. You can evaluate a variety of potential ML use cases to solve problems. Going from being a special product to an operationalized business process that utilizes technology to get things done is going to be dependent on the return on investment associated with the use case. If it costs more to do it, then you could reasonably expect that use case is going to make most responsible actors pump the brakes on the project. However, it appears that a lot of use cases went forward anyway. 

I spent some time trying to find a good accounting of how much money has been spent on ML projects overall and how many of them have actually yielded solid ROI. You probably will not be all that surprised to learn that very few rigorous studies of successful ROI in the ML space exist. If some of those types of studies exist and I just missed them during my search, then by all means feel free to share them in the comments and let me know [1]. It won’t hurt my feelings or anything and I’d actually be a little bit relieved that research on the subject exists. 

Before we conclude here, I do want to share one paper that does seem to be directly addressing this question. It has only been cited by 3 other papers since 2020. I had hoped it would lead me to a cluster of academic research. That was not the case. The search for solid academic research on ML ROI is still ongoing.

That paper was:

Mizgajski, J., Szymczak, A., Morzy, M., Augustyniak, Ł., Szymański, P., & Żelasko, P. (2020). Return on Investment in Machine Learning: Crossing the Chasm between Academia and Business. Foundations of Computing and Decision Sciences, 45(4), 281-304.

Links and thoughts:

“We Talked To A VP At Microsoft – WAN Show December 23, 2022”

Top 5 Tweets of the week:



What’s next for The Lindahl Letter?

  • Week 102: ML pracademics
  • Week 103: Rethinking the future of ML
  • Week 104: That 2nd year of posting recap

I’ll try to keep the what’s next list forward looking with at least five weeks of posts in planning or review. If you enjoyed this content, then please take a moment and share it with a friend. If you are new to The Lindahl Letter, then please consider subscribing. New editions arrive every Friday. Thank you and enjoy the week ahead.

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