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Arvind Narayanan Profile picture
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1 Jul, 5 tweets, 3 min read
We shouldn't shrug off dark patterns as simply sleazy sales online, or unethical nudges, or business-as-usual growth hacking. Dark patterns are distinct and powerful because they combine all three in an effort to extract your money, attention, and data. queue.acm.org/detail.cfm?id=… Image
That's from a 2020 paper by @aruneshmathur, @ineffablicious, Mihir Kshirsagar, and me.

PDF version: dl.acm.org/ft_gateway.cfm…
At first growth hacking was about… growth, which was merely annoying for the rest of us. But once a platform has a few billion users it must "monetize those eyeballs". So growth hackers turned to dark patterns, weaponizing nudge research and A/B testing. queue.acm.org/detail.cfm?id=… Image
To understand why dark patterns are here to stay and why that's a problem, we have to see them in the context of the surveillance economy. Image
Concluding thought: regulation is needed, but at the same time, cultural change among designers is possible and necessary. Without such change, regulators will be left playing whack-a-mole with the industry, and that's not going to end well. Image

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More from @random_walker

Arvind Narayanan Profile picture
30 Jun
I study the risks of digital tech, especially privacy. So people are surprised to hear that I’m optimistic about tech’s long term societal impact. But without optimism and the belief that you can create change with research & advocacy, you burn out too soon in this line of work.
9 years ago I was on the academic job market. The majority of professors I met asked why I chose to work on privacy since—as we all know—privacy is dead because of the Internet and it's pointless to fight it. (Computer scientists tend to be technological determinists, who knew?!)
At fist I didn't expect that "why does your research field exist?" would be serious, recurring question. Gradually I came up with a pitch that at least got interviewers to briefly suspend privacy skepticism and hear about my research. (That pitch is a story for another day.)
Read 5 tweets
Arvind Narayanan Profile picture
22 Jun
The news headlines *undersold* this paper. Widely-used machine learning tool for sepsis prediction found to have an AUC of 0.63 (!), adds little to existing clinical practice. Misses two thirds of sepsis cases, overwhelms physicians with false alerts. jamanetwork.com/journals/jamai…
This adds to the growing body of evidence that machine learning isn't good at true prediction tasks as opposed to "prediction" tasks like image classification that are actually perception tasks.
Worse, in prediction tasks it's extremely easy to be overoptimistic about accuracy through careless problem framing. The sepsis paper found that the measured AUC is highly sensitive to how early the prediction is made—it can be accurate, or clinically useful, but not both.
Read 4 tweets
Arvind Narayanan Profile picture
22 Jun
Academia rewards clever papers over real world impact. That makes it less useful. But it also perpetuates privilege—those with less experience of injustice find it easier to play the game, i.e. work on abstruse problems while ignoring topics that address pressing needs.
I have no beef with fundamental research (which isn't motivated by applications). But most scholarship that *claims* to be motivated by societal needs happens with little awareness of what those needs actually are, and no attempt to step outside academia to actually make change.
Like many of academia's problems, this one is structural. Telling individual scholars to do better is unlikely to work when the incentives are all messed up. Here are some thoughts on what might work. I'd love to hear more.
Read 8 tweets
Arvind Narayanan Profile picture
21 Jun
A student who's starting grad school asked me which topics in my field are under-explored. An important question! But not all researchers in a community will agree on the answers. If they did, those topics won't stay under-explored for long. So how to pick problems? [Thread]
It's helpful for researchers to develop a "taste" for problems based on their specific skills, preferences, and hypotheses about systemic biases in the research community that create blind spots. I shared two of my hypotheses with the student, but we must each develop our own.
Hypothesis 1: interdisciplinary topics are under-explored because it requires researchers to leave their comfort zones. But collaboration is a learnable skill, so if one can get better at it and find suitable collaborators, rich and important research directions await.
Read 6 tweets
Arvind Narayanan Profile picture
20 Jun
I often find myself re-reading this short piece about what peer review was like in the 1860s. A reviewer was someone who helped improve a paper through a collegial, interactive process rather than rejecting it with a withering, anonymous comment. physicstoday.scitation.org/do/10.1063/PT.…
The great benefit of the more formalized system we have today is that it is more impartial, and has helped turn science into less of an old boys' network. But it is also clear that something has been lost.
The problem with reducing bias by formalizing the review process is that it pushes the bias to other parts of the publication pipeline where it is less observable and harder to mitigate.
Read 5 tweets
Arvind Narayanan Profile picture
31 May
When a machine learning system uses argmax to select outputs from a probability distribution — and most of them do — it's a clue that it might be biased. That's because argmax selects the "most probable" output, which may amplify tiny data biases into perfectly biased outputs.
Here's an exercise (with solution) I developed for my Fairness in ML course with @ang3linawang's help. It uses a toy model to show how bias amplification like the one in the "Men also like shopping" paper can arise through the use of argmax alone! drive.google.com/file/d/1baK_c4…
This graph is the punchline. α and β are parameters that describe correlations in the input and the graphs show correlations in the (multilabel) output. It should be terrifying from a scientific and engineering perspective even if there are no relevant fairness considerations!
Read 10 tweets

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