Graduate student in statistics at Duke University. Former dev.to employee. I like to blog about data science on my Medium publication, perplex.city, and on dev.to
Great post. I have a general question: how much value does your team put into the interpretability of the model? I'd guess the highest priority is making good matches and getting users to click, but at what point are you willing to sacrifice slightly better performance for the ability to explain what's going on to someone without statistical expertise?
Kim Arnett [she/her] leads the mobile team at Deque Systems, bringing expertise in iOS development and a strong focus on accessibility, user experience, and team dynamics.
So far, we've put our focus exclusively on optimizing for job applications. We've had plans to make the inner works of the algorithm more transparent, and this blog post is a step in that direction. I'm not sure we'll compromise relevance of results for the sake of making them easier to explain. But explaining within the site how we sort search results is something we'd like to do.
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Great post. I have a general question: how much value does your team put into the interpretability of the model? I'd guess the highest priority is making good matches and getting users to click, but at what point are you willing to sacrifice slightly better performance for the ability to explain what's going on to someone without statistical expertise?
+1 Good Question
So far, we've put our focus exclusively on optimizing for job applications. We've had plans to make the inner works of the algorithm more transparent, and this blog post is a step in that direction. I'm not sure we'll compromise relevance of results for the sake of making them easier to explain. But explaining within the site how we sort search results is something we'd like to do.