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Arvind Narayanan Profile picture
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1 Dec, 5 tweets, 3 min read
Job alert: At Princeton we’re hiring emerging scholars who have Bachelor’s degrees for 2-year positions in tech policy. The program combines classes, 1-on-1 mentoring, and work experience with real-world impact. Apply by Jan 10. More details: citp.princeton.edu/programs/citp-…

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This is a brand new program. Emerging scholars are recruited as research specialists: staff, not students. This comes with a salary and full benefits. We see it as a stepping stone to different career paths: a PhD, government, nonprofits, or the private sector.
Who are we? At Princeton’s Center for Information Technology Policy (@PrincetonCITP), our goal is to understand and improve the relationship between technology and society. Our work combines expertise in technology, law, social sciences, and humanities. citp.princeton.edu
We’re delighted that faculty including @ruha9, @jonathanmayer, @orussakovsky, @msalganik, @b_m_stewart, and Mihir Kshirsagar have expressed an interest in being potential mentors. citp.princeton.edu/programs/emerg…
We’re open to applicants with degrees in a broad set of fields. This is an unusual kind of program and we’re happy to answer your questions and help you figure out if it’s right for you. Please email me (arvindn@cs.princeton.edu) or Dr. Tithi Chattopadhyay (tithic@princeton.edu).

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More from @random_walker

Arvind Narayanan Profile picture
27 Nov
One of the most ironic predictions made about research is from mathematician G.H. Hardy’s famous "Apology", written in 1940. He defends pure mathematics (which he called real mathematics) on the grounds that even if it can't be used for good, at least it can't be used for harm.
Number theory later turned out to be a key ingredient of modern cryptography, and relativity is necessary for GPS to work properly. Cryptography and GPS both have commercial applications and not just military ones, which I suspect Hardy would have found even more detestable.
Hardy’s examples weren’t merely unfortunate in retrospect. I think they undercut the core of his argument, which is a call to retreat to the realm of the mind, concerned only with the beauty of knowledge, freed from having to think about the real-world implications of one’s work.
Read 7 tweets
Arvind Narayanan Profile picture
25 Nov
When I was a student I thought professors are people who know lots of stuff. Then they went and made me a professor. After getting over my terror of not knowing stuff, I realized I had it all wrong. Here are a bunch of things that are far more important than how much you know.
- Knowing what you know and what you don’t know.
- Being good at teaching what you know.
- Being comfortable with saying you don’t know.
- Admitting when you realize you got something wrong.
- Effectively communicating uncertainty when necessary.
- Spotting BS.
- Recognizing others with expertise.
- Recognizing that there are different domains of expertise.
- Recognizing that there are different kinds of expertise including lived experience.
- Drawing from others’ expertise without deferring to authority.
Read 5 tweets
Arvind Narayanan Profile picture
21 Oct
Many face recognition datasets have been taken down due to ethical concerns. In ongoing research, we found that this doesn't achieve much. For example, the DukeMTMC dataset of videos was used in 135 papers published *after* it was taken down in June 2019. freedom-to-tinker.com/2020/10/21/fac…
A major challenge comes from derived datasets. In particular, the DukeMTMC-ReID dataset is a popular dataset used for person re-identification and continues to be free for anyone to download. 116 of 135 papers that use DukeMTMC after its takedown actually use a derived dataset.
This is a widespread problem. MS-Celeb was removed due to criticism but lives on through MS1M-IBUG, MS1M-ArcFace, MS1M-RetinaFace… all still public. The original dataset is also available via Academic Torrents. One popular dataset, LFW, has spawned at least 14 derivatives.
Read 6 tweets
Arvind Narayanan Profile picture
5 Oct
At Princeton CITP, we were concerned by media reports that political candidates use psychological tricks in their emails to get supporters to donate. So we collected 250,000 emails from 3,000 senders from the 2020 U.S. election cycle. Here’s what we found. electionemails2020.org
Let me back up: this is a study by @aruneshmathur, Angelina Wang, @c_schwemmer, Maia Hamin, @b_m_stewart, and me. We started last year by buying a list of all candidates running for federal and state elections in the U.S. We also acquired lists of PACs and other orgs.
Next, the key bit for data collection: we created a bot that was able to find these candidates’ websites through search engines, look for email sign up forms, fill them in, and collect the emails in a giant inbox. We verified manually that each step works pretty accurately.
Read 11 tweets
Arvind Narayanan Profile picture
17 Sep
Expertise is important for scholars, but after 5-10 years the benefits of continuing to deepen your expertise are tiny compared to broadening it.

Universities are perfectly set up to prevent breadth of expertise by hiring people for life and putting them into siloed departments.
How have I not heard this before? It's a few weeks too late to use it on the students on PhD orientation day!
The intellectual superiority of depth over breadth is a pervasive fiction in academia that sustains the culture of fetishizing specialization. I tried to fight this culture early in my career, but realized it was like punching a bag of sand.
Read 5 tweets
Arvind Narayanan Profile picture
26 Aug
An amazing benefit of my privilege is being able to say "I didn't understand that. Could you explain it again?" as many times as necessary without having to worry that people will think I'm stupid.
If you didn't understand something I said, please ask me as many times as necessary. In fact, I'm delighted when this happens. As a professor, knowing when something I explained didn't make sense is extremely valuable feedback that helps me do better.
I'm a tenured computer science professor who looks like what many people expect a tenured computer science professor to look like. The follow up I get after someone asks "So what do you do?" is nearly always "Oh, you must be really smart."
Read 4 tweets

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