I liked reading about this, thank you for taking the time to write about it. I have seen a number of HR AI/ML companies pop up recently and it seems they are particularly prone to the “how biased is the algorithm” question, which I have to imagine applies here as well. ML applications in other employee software systems (like electronic health records) also run into the problem you highlighted about having employees buy into the system. Given that the employees have to input their preferences, does it leave room to game the system, or does collective inaction/lack of buy in mean the product suffers enough to never get off the ground? Will employees be ok with a ML generated assignment? To me it presents a bunch of very interesting organizational challenges and questions!
What a neat topic! I am glad to see this in a more mature phase from what it was a few years ago. I had seen another example of this (https://blog.google/technology/ai/machine-learning-meets-african-agriculture/) when looking for a topic to do (glad I didn’t pick it and overlap with you!). It is especially interesting to read about the value capture portion. Thank you for posting!
What a neat concept! A high school age me would have been totally into this (I used to volunteer for medical trials of various sorts at a local hospital). That does lead me to wonder how high quality and consistent the data users are producing is (or perhaps the only productive users are of a biased/specific demographic). I can get why users of an app like Ovia are good about inputting their data, but I wonder if the intrinsic motivation would last as long for a company like this. Like Strava, maybe it’s biggest sticky point is that it integrates other user health data. Thank you for covering!
I love Strava! Their ability to stitch together a bunch of different data sources is a huge selling point I feel. The points you made around scalability make total sense now that I think about it. It also seems to be to appeal to multiple types of users – casual people trying out fitness tracking, hardcore data driven device users, folks who are competitive, general fitness enthusiasts etc. Thanks for posting, I liked reading this!
This is a cool app! When reading it made me think a lot of Craigslist or Facebook Marketplace. I wonder like you how scalable/global the Karrot could be in other countries if other apps/platforms exist already in some capacity. Maybe the more formalized nature of their app would be a selling point? Anyways, thank you for posting, this was a cool read!
Thanks for highlighting Garmin! Fitness data is a really cool application of the concepts we have been talking about in class and it’s interesting to see how Garmin is positioned relative to their rivals (Apple, Fitbit, etc) for the more intense athletic user. And the data that you talk about fits directly into that positioning! Really cool, thank you again!
This is a really cool company! I have a bit of familiarity with the industry as (I believe?) there is the additional problem beyond the lack of micro data is that the standard maps used for assessing price/risk for areas are very out of date (and often in disagreement with each other). That plus the odd incentive system around providing cheaper insurance to higher risk flood zones. (Again could be wrong about that feel free to correct). Thanks for highlighting them!
I also hadn’t heard about Ibotta! This is a really interesting concept! In my previous work, I did some amount of fiddling with consumer credit card data (purchasing, identifying trends, etc), so this type of application makes total sense to me! It makes me wonder what type of customer uses an app like Ibotta and the value that Ibotta provides to both the consumers and Ibotta’s eventual customer. Thank you for highlighting them, I learned a lot!