Smart Software Pitfalls: A List-Tickle
August 26, 2016
Need page views? Why not try a listicle or, as we say here in Harrod’s Creek, a “list-tickle.”
In order to understand the depth of thought behind “13 Ways Machine Learning Can Steer You Wrong,” one must click 13 times. I wonder if the managers responsible for this PowerPoint approach to analysis handed in their college work on 5X8 inch note cards and required that the teacher ask for each individually.
What are the ways machine learning can steer one into a ditch? As Ms. Browning said in a single poem on one sheet of paper, “Let me count the ways.”
- The predictions output by the Fancy Dan system are incorrect. Fancy that.
- One does not know what one does not know. This reminds me of a Donald Henry Rumsfeld koan. I love it when real journalists channel the Rumsfeld line of thinking.
- Algorithms are not in line with reality. Mathematicians and programmers are reality. What could these folks do that does not match the Pabst Blue Ribbon beer crowd at a football game? Answer: Generate useless data unrelated to the game and inebriated fans.
- Biased algorithms. As I pointed out in this week’s HonkinNews, numbers are neutral. Humans, eh, not often.
- Bad hires. There you go. Those LinkedIn expertise graphs can be misleading.
- Cost lots of money. Most information technology projects cost a lot of money even when they are sort of right. When they are sort of wrong, one gets Hewlett Packard-Autonomy like deals.
- False assumptions. My hunch is that this is Number Two wearing lipstick.
- Recommendations unrelated to the business problem at hand. This is essentially Number One with a new pair of thrift store sneakers.
- Click an icon, get an answer. The Greek oracles required supplicants to sleep off a heady mixture of wine and herbs in a stone room. Now one clicks an icon when one is infused with a Starbuck’s tall, no fat latte with caramel syrup.
- GIGO or garbage in, garbage out. Yep, that’s what happens when one cuts the statistics class when the professor talks about data validity.
- Looking for answers the data cannot deliver. See Number Five.
- Wonky outcomes. Hey, the real journalist is now dressing a Chihuahua in discarded ice skating attire.
- “Blind Faith.” Isn’t this a rock and roll band. When someone has been using computing devices since the person was four years old, that person is an expert and darned sure the computer speaks the truth like those Greek oracles.
Was I more informed after clicking 13 times? Nope.
Stephen E Arnold, August 26, 2016
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