Tuesday, November 26, 2019

Papers and posting

If you haven't seen it, Adrian Colyer's excellent blog has great reviews and summaries of recent papers. Back when this blog started in 2004, there weren't many people summarizing research papers. Many more are now, which is part of why I post less now. Adrian's blog is excellent and similar to what I used to do, but I think better in many ways. You can also follow Adrian Colyer on Twitter.

While I'm talking about summarizing papers, I want to highlight two lines of work that had an impact on me in the last few years and that I think deserve much more attention. Both argue we, as in all of us in tech, are doing something important wrong.

The first argues that our metrics usually are off, specifically way too focused on short-term goals like immediate revenue. This is the work started by the fantastic Focus on the Long-term out of Google and continuing from there (including [1] [2]). Because much of what we all do is an optimization process -- ML, deep learning, recommendations, A/B testing, search, and advertising -- having the targets wrong means we are optimizing for the wrong thing.

Optimizing for the wrong thing is ubiquitous in our industry. It may, for example, cause almost everyone to show too many ads and too many low quality ads. If everyone has their metrics subtly wrong, everything we make, and especially everything in the ML community, may be aiming for the wrong target.

The second is Kate Starbird's work on disinformation campaigns. Across many recent papers, Kate argues that the traditional classifier approach to spam, trolls, and shills has been failing. Adversaries can create many accounts and enlist real humans in their disinformation effort. Knocking a few accounts away does nothing; it is like shooting bullets into a wave. Instead, it is important to look at the goals of disinformation campaigns and make them more expensive to achieve. Because shills impact so many things we do -- training data for ML and deep learning, social media, reviews, recommendations, A/B testing, search, advertising -- our failure to deal with shills means the assumptions all of these systems have about the data all being equally good are wrong, and the quality of all these systems is reduced.

Solutions are hard. I'm afraid Kate's advice on solutions is limited. But I would say solutions include whitelisting (using only experts, verified real people, or accounts that are expensive to create), recognizing likely disinformation as it starts to propagate and slowing it, and countering likely disinformation with accurate information where it appears. Those replace outdated whack-a-mole account classifiers and work across multiple accounts to counter modern disinformation campaigns. Manipulation and shilling from sophisticated adversaries is ubiquitous in our industry. Until we fix this, many of our systems produce lower quality results.

Finally, I am posting a lot less here now, so let me point to other resources for anyone who liked this blog. I still post frequently on Twitter; you can follow me there. On AI/ML, there's a lot of great writing by a lot of people, far too many to list, but I can at least list my favorites, which are Fran├žois Chollet and Ben Hamner on Twitter. On economics and econometrics, which I enjoy for adding breadth to AI/ML, my favorites are economists Noah Smith and Dina Pomeranz on Twitter.