How The New York Times Uses Software to Recognize Members of Congress

On that note, they could utilize the box color to match the party affiliation.

(And no one else)

Several comments from the article give me concern

- They seem to think Rekognition is a panacea for their problem, but there are many known issues with Rekognition celebrity detection. Not to mention that the cost-per-request is often highly unfavorable compared with building a higher-accuracy, situation-specific solution with extensions to pre-trained models.

- They say some interns took a “novel approach” by creating a hard coded look-up table for disambiguating similar politician-celebrity pairs. This creates awful tech debt and failure cases. I’m not knocking it too hard because it’s pragmatic, which is a good sign about those interns, but this should be seen as a necessary wart to be improved, not a point of pride.

- As others have pointed out, even considering turnover in Congress, it seems like people who report on Congress for their full time job should recognize them. It truly seems like a silly, wasteful use of resources to solve this with computer vision.

This is all consistent with what I’ve heard from colleagues at NYT data science. As well as people I’ve known in data science bootcamps around New York, like Insight, who heard recruiting pitches.

Their department seems self-aggrandizing, using highly overwrought personalization models and seeming to have 538-envy for how they want their data science work to come off despite 538 exiting, among other important figures like Mike Bostock.

It just comes off as a place that wants to do status signalling to seem like a machine learning or data science thought-leader, but they don’t pay competitively or do what’s needed to retain good people and would rather do patchwork stuff like this with interns than to take the work a little more seriously.

I don’t get the impression it’s a place serious ML practitioners would want to go.

> Most recently, Rachel Shorey found members of Congress at an event hosted by a SuperPAC by trawling through images found on social media and finding matches.

I bet nothing in the technology says "member of Congress" or depends on the target being member of Congress. So anybody can mine social media and collect surveillance data on people. And that is probably already happening.

Someone is woefully ignorant how good facial-recognition surveillance is.

Every profession has things you can look up and things you just have to memorize. 540 people isn't much - can sports journalists recognize 540 athletes? Otherwise you'll be in situations where you don't have an opportunity to look them up (e.g., can't get a photo, no time, etc.), and you'll have many false negatives: If you don't know what they look like, you won't realize it's a member of Congress at the party with the coke.

This is disturbing to hear. How can our congress make the best decisions possible if it can't access and communicate relevant information quickly? The ROI to the United States of simply having a high-bandwidth network at this global powerspot is so obvious that I had just assumed it was the case—so to hear that reporters can't even use a web interface to quickly send images is frightening if true, and perhaps even indicative of a broader issue of our government's inability to effectively execute, partially rooted in its inability to empower itself with the tools necessary to effectively execute.

* Edited at burkaman's prompt to be less sensationalist