Measuring ‘engagement’ on social platforms is always going to be a proxy for an actual concept of value; a user engaging with something doesn’t mean they value it. This paper closes that gap, connecting engagement behaviors to value through a Bayesian network. The authors implement their approach on Twitter.
Hi, I'm Tushar.Thank you for visiting my website. Here, I post my thoughts on data science and HCI; summaries of papers I've read and talks I've watched; and reflections on other things that I read.
Talking to Judah in our reading club helped me to crystallize some of my thoughts about Ali Alkhatib & Michael Bernstein’s Street-Level Algorithms paper. This post explores these.
Street-level bureaucrats are the people making routine decisions for institutions—administrators, police, professors, and more. This work introduces street-level algorithms as an idea for algorithms that are tasked with filling the same role.
397 words what I read,
Two thoughtful articles this week: on how machine learning is going real time, and the problems with machine learning in medicine.
Fairness in machine learning is typically concerned with ideas like discrimination, disparate impact or treatment, or protected classes. This paper describes how the definitions being used in ML aren’t always compatible with definitions in the legal system.
1667 words general,
My year in review post. 2020 was a terrible year, all things considered, but I found opportunity to rekindle my reading habit and write consistently. Inside are reflections on personal and career growth, the development of my research skills, and some of the best things I read this year.
I did some cleanup of the categories and tag listings. This short post discusses why and how!
582 words what I read,
Happy new year! This week’s edition is short, featuring a post on Apple’s new M1 chips and why they’re so fast, and another on boundaries within engineering orgs, especially “social good” ones.