A few of our thoughts on this great read from the MIT Technology Review: "Here’s a checklist for assessing the quality and validity of a company’s machine-learning product."
Last week, Karen Hao published this article on the MIT Technology Review: "Five questions you can use to cut through AI hype - Here’s a checklist for assessing the quality and validity of a company’s machine-learning product."
This is a 667-word, 5-minute read, and packs in some terrific insight.Among them, we love these two must-ask questions, in particular:
"1. What is the problem it’s trying to solve?" (Sub-text: If the A.I. company/solution cannot pinpoint a precise problem that it is solving, you may be looking at an overly broad platform, and not an actual solution... our CEO recently expanded on the important—and, sometimes, painful—difference on our blog here.5. Should the company be using machine learning to solve this problem? (Two layers of sub-text here: (a) Is the company actually employing machine learning, or just reciting buzzword bingo?; and (b) is the company overcomplicating the solution, unnecessarily? Point b is crucial—as a healthcare leader, you're looking for outcomes... whether the solution drives that outcome with A.I. or not should be irrelevant. LSS, don't be blinded by hype.
And Hao's closing paragraph is spot-on:
"In my opinion, a company with a quality machine-learning product should check off all the boxes: it should be tackling a problem fit for machine learning, have robust data acquisition and auditing processes, have highly accurate algorithms or a plan to improve them, and be grappling head-on with ethical questions. Oftentimes, companies pass the first four tests but not the last. For me, that is a major red flag. It demonstrates that the company isn’t thinking holistically about how its technology can affect people’s lives and has a high chance of pulling a Facebook later down the line. If you’re an executive looking for machine-learning solutions for your firm, this should warn you against partnering with a particular vendor."
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