Skip to content

The Value of Algorithms in Artificial Intelligence

Share this article: Facebooktwitterredditlinkedinmail


Algorithms by themselves aren’t valuable. Their ability to solve a particular business problem is. The two most important questions to ask when assessing the value of algorithms in artificial intelligence are: 1) Can they solve the business problem? 2) Do people actually use the right conditions under which to perform their analysis?

Does their use of algorithms make a difference?

For example: Job candidates are often scored by algorithms in order to help human business leaders responsible for making hiring decisions. Sometimes recruiters who receive this machine-generated output don't actually follow the recommendations provided by the algorithms. It’s as if a tree falls alone in the forest and no one is around to hear it. One could argue that it didn't happen, or at least that the algorithms (represented by the tree falling) didn't matter. If someone didn’t hear the algorithm's output or listen to its recommendations, the algorithm by itself didn’t change the business decision at hand. Did the algorithms really do the analysis in the first place?

The value of algorithms in artificial intelligence depends upon their practical use.

Both of my fellow panelists mention the importance of closing the loop, and collecting data at all stages of the customer lifecycle, even when communicating about topics as complex as cancer medication and lifestyle. Doing a retrospective analysis and predicting an outcome is important, but it is simply the first step in ensuring algorithms used in artificial intelligence produce value.

I will give you an example of where over-reliance on machine-generated output--without a human-in-the-loop--fails. At RapportBoost, our team of data scientists performed an analysis on a customer with millions of chats between their live chat sales team and visitors to their website. Our algorithms determined a high correlation between an agent apologizing and a low customer satisfaction score. An unsupervised deep learning algorithm would assume causation and recommend that the live chat sales agent never apologize. Common sense, human experience and an understanding of our clients' online retail business tell us, however, that many apologies took place because a customer was not happy. The customer would have reported a low score anyway. Algorithms are more valuable to artificial intelligence systems when humans can help interpret causation.

When one gathers data and does A/B testing to determine causal imprints instead of a backward looking quick analysis you get really deep and understand people on a much deeper level. This is where value is created by algorithms for use in artificial intelligence.

Learn more about the live chat agent training solutions from the team at RapportBoost.

The excerpt summarized above is from Dr. Michael Housman, Chief Data Scientist and Co-Founder of RapportBoost.AI, discussing the value of algorithms at the JMP Securities Conference panel on AI in February 2017.

Follow us:Facebooktwitterlinkedinrssyoutube
Dr. Michael Housman

About Dr. Michael Housman

Michael has spent his entire career applying state-of-the-art statistical methodologies and econometric techniques to large data-sets in order to drive organizational decision-making and helping companies operate more effectively. Prior to founding RapportBoost.AI, he was the Chief Analytics Officer at Evolv (acquired by Cornerstone OnDemand for $42M in 2015) where he helped architect a machine learning platform capable of mining databases consisting of hundreds of millions of employee records. He was named a 2014 game changer by Workforce magazine for his work. Michael is currently an equity advisor for a half-dozen technology companies based out of the San Francisco bay area: hiQ Labs, Bakround, Interviewed, Performiture, Tenacity, Homebase, and States Title. He was on Tony’s advisory board at Boopsie from 2012 onward. Michael is a noted public speaker and has published his work in a variety of peer-reviewed journals and has had his research profiled by The New York Times, Wall Street Journal, The Economist, and The Atlantic. Dr. Housman received his A.M. and Ph.D. in Applied Economics and Managerial Science from The Wharton School of the University of Pennsylvania and his A.B. from Harvard University.

Leave a Comment

Scroll To Top