The Risky Business of AI Implementations - Unintended Consequences
- Jennifer Jordan
- Sep 25, 2019
- 1 min read
Updated: Sep 28, 2019
Headlines are full of the ways in which our use of data and automated decision-making algorithms have run amok, from blindly optimizing into unintended consequences, to extrapolating for populations not represented in the data, to replicating and even amplifying existing biases:
· YouTube’s algorithm makes it easy for pedophiles to find more videos of children – June 2019
· Human Genomics Research Has A Diversity Problem – March 2019
· Women’s Pain Is Different from Men’s – The Drugs Could be Too – March 2019
· Amazon's controversial facial recognition software can't tell the difference between men and women or recognize dark-skinned females, MIT study finds – January 2019
· Police use of Amazon’s face-recognition service draws privacy warnings – May 2018
· Will Using Artificial Intelligence to Make Loans Trade One Kind of Bias for Another? – March 2017
· Amazon Doesn’t Consider the Race of Its Customers. Should it? – April 2016
· Google’s algorithm shows prestigious jobs to men, not women – July 2015
· Racist Camera! No, I did not blink. I’m just Asian! – May 2009
At the same time the push to regulate this technology is heating up. California’s Consumer Protection Act is set to go into effect, multiple cities and a few states have adopted legislation limiting the use of facial recognition technology, and the “Algorithmic Accountability Act” proposed in the U.S. Congress would require that companies be able to explain the technologies’ decisions to consumers.
This title really hit home. It brought back a memory from a few years ago when my old company decided to automate our customer service ticketing system. The idea was to sort and route issues faster, but the AI they chose was… well, let's just say it had a mind of its own. It started miscategorizing urgent issues as low priority and sending simple requests to departments that had no clue how to handle them https://www.nytimes.com/2009/06/10/business/10poker.html?_r=3 We had a backlog of frustrated customers growing by the hour, and the IT team was scrambling to figure out where things were going wrong. It felt like we were trying to put out fires with a hose that was spraying water in random…
This post really hit home for me! I’ve been searching for a comprehensive look at navigating the complexities of risky business ventures, and your article is precisely what I needed. It’s so refreshing to see such candidness about the potential pitfalls of AI adoption. Honestly, the way you've laid out the challenges makes me feel less alone in my own hesitations https://www.osko.com.au/ I’ve tried to sidestep the idea of engaging with these sorts of uncertain projects, but they have a way of resurfacing, demanding attention. It makes me wonder about the specific compromises one has to make when diving headfirst into a risky business endeavor. A follow-up piece offering a practical roadmap for adopting these kinds of initiatives would be…
This was a really insightful read, and I'm definitely making a note to revisit this next month to mull it over again. I'd also add that I walked away from this feeling significantly smarter about the world of risky business, so cheers for that! The conviction behind your take on the potential pitfalls of AI implementations is so well placed; it truly resonated with me https://aana.com.au/self-regulation/codes-guidelines/wagering-code/ It makes me wonder if the relevance of these kinds of ventures is increasing or decreasing in today's rapidly evolving landscape. I found myself reflecting on how easily unforeseen issues can arise, much like when I tried to automate a simple household task last year, only to create a more complex problem than I…
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