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Arvind Narayanan

31 Dec 18, 14 tweets, 4 min read

In 2018 the blockchain/decentralization story fell apart. For example, a study of 43 use cases found a 0% success rate. theregister.co.uk/2018/11/30/blo…
Let's talk about some mistaken assumptions about decentralization that led to the blockchain hype, and what we can learn from them.

(For context, I think the technology is sound, interesting and important from a CS perspective. That’s why I’ve been researching and teaching it since 2013. bitcoinbook.cs.princeton.edu But I’ve also been speaking/writing about the pitfalls of decentralization for ~10 years.)

Blockchain proponents have a vision of _society_ in which centralized entities are weakened/eliminated. But blockchain tech is a way to build _software_ without centralized servers. Why would the latter enable the former? It’s a leap of logic that’s left unexplained.

There’s a widespread belief in the blockchain world that centralization results from government regulation and/or monopolistic rent-seeking. The truth is more mundane: centralization emerges naturally in a free market due to economies of scale and other efficiencies.

Absence of government intervention doesn’t mean decentralization—often the opposite. For example, after airlines were deregulated in the US in 1978, they quickly transitioned from a point-to-point model to a hub-and-spoke model, which is more centralized. en.wikipedia.org/wiki/Airline_d…

A neat illustration of how centralization arises naturally due to economies of scale is the fact that the evolution of cryptocurrency mining closely parallels gold mining more than a century ago! [By @josephboneau, from our book. bitcoinbook.cs.princeton.edu]

If there were a conspiracy, then blockchains might be a good answer. But, as Steve Jobs realized in the context of network TV, what we’ve got is what people want.

Open platforms can’t win by directly appealing to users on philosophical grounds, or even cost (see Linux on the desktop). Mainstream users have no good reason to directly interact with blockchain technology—or any piece of code—without intermediaries involved.

Openness and decentralization matter to _developers_. To succeed, decentralized platforms must attract developers and foster an ecosystem of services that build on each other and gradually improve in functionality and quality. That’s how the Internet beat Compuserve and AOL.

But this process has to happen organically and will take decades. It can't be rushed with VC/ICO money. What blockchain companies are doing today is as if Internet companies had tried to compete against print newspapers in the 80s. The supporting infrastructure just wasn’t there.

Another area where blockchain projects have faltered is in how they govern themselves. Decentralized doesn’t mean structureless — structureless groups don’t exist. jofreeman.com/joreen/tyranny…
You can reject governments but you can’t opt out of governance.

Smart contracts are really cool, but they're neither smart nor contracts. freedom-to-tinker.com/2017/02/20/sma…
The fact that they are being proposed as replacements for legal contracts with a straight face is emblematic of the category errors that are rampant in the blockchain space.

I'll leave you with pointers to two readings that I've found incredibly helpful. The first is the book Master Switch, a deep historical exploration of decades-long cycles of centralization & decentralization. amazon.com/Master-Switch-…

The second is absolutely everything written by Matt Levine on blockchains, smart contracts, and fintech. The most recent is this piece on Robinhood, and how fintech "innovation" is a great way to rediscover why regulations exist. bloomberg.com/opinion/articl…

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Arvind Narayanan

@random_walker

15 Sep

Machine learning for decision making often results in discriminatory systems. The reason for this isn't specific to ML. It applies to many quantitative models—models that start by assuming a fixed, exogenously specified distribution of decision subjects' characteristics. 🧵

But the distribution of people's attributes (skills, risk levels, preferences…) isn't fixed. In fact, it is usually produced largely by the past effects of the types of decision systems that these models are used to justify. So the logic underlying these models is tautological.

Unless a model can endogenize the risk distribution, it has zero normative content. At best it has some descriptive value to explain observed differences. But these lazy models are so often used to make policy and thus become excellent tools for reinforcing the status quo.

Read 7 tweets

Arvind Narayanan

@random_walker

9 Aug

To better understand the ethics of machine learning datasets, we picked three controversial face recognition / person recognition datasets—DukeMTMC, MS-Celeb-1M, and Labeled Faces in the Wild—and analyzed ~1,000 papers that cite them.
Paper: arxiv.org/pdf/2108.02922…

Thread ⬇️

@aruneshmathur

First, congrats to lead author Kenny Peng who worked on it for over a year. Kenny was a sophomore here at Princeton when he began this project, and he did this in addition to his coursework and several other research projects. The other authors are @aruneshmathur and me.

Finding 1: despite retraction, DukeMTMC and MS-Celeb-1M are available through copies, derivatives, or pre-trained models. They are still widely used in papers. The creators (especially MS) simply took down the websites instead of making the ethical reasons for retraction clear.

Read 13 tweets

Arvind Narayanan

@random_walker

4 Aug

@elucherini4

Interested in the impact of recommender systems on society? @elucherini4, @MatthewDSun, @aawinecoff, and I have software, papers, and a talk to share:
– T-RECS, a simulation tool for studying these questions github.com/elucherini/t-r…
– Accompanying paper arxiv.org/pdf/2107.08959… 🧵

– A short piece on methodological concerns in simulation research arxiv.org/pdf/2107.14333…
– A talk (by me) offering a critical take on research on filter bubbles mediacentral.princeton.edu/media/1_45q6h2…

Here's a blog post that provides an overview and context: freedom-to-tinker.com/2021/08/04/stu…

The key rationale for this work is that phenomena such as algorithmic amplification of misinformation, filter bubbles, or content diversity in recommendations are difficult to study because they arise through repeated interactions between users, items, and the system over time.

Read 4 tweets

Arvind Narayanan

@random_walker

2 Aug

@sayashk

Can machine learning outperform baseline logistic regression for predicting complex social phenomena? Many prominent papers have claimed highly accurate civil war prediction. In a systematic review, @sayashk and I find these claims invalid due to errors. reproducible.cs.princeton.edu

We are not political scientists and the main point of our paper is not about civil war. Rather, we want to sound the alarm about an oncoming wave of reproducibility crises and overoptimism across many scientific fields adopting machine learning methods. We have an ongoing list:

Incidentally, we learned about one of the systematic surveys in the above list because it found pitfalls in a paper coauthored by me. Yup, even researchers whose schtick is skepticism of AI/ML are prone to overoptimism when they use ML methods. Such is the allure of AI.

Read 8 tweets

Arvind Narayanan

@random_walker

19 Jul

In my dream version of the scientific enterprise, everyone who works on X would be required to spend some percentage of their time learning and contributing to the philosophy of X. There is too much focus on the "how" and too little focus on the "why" and the "what are we even".

Junior scholars entering a field naturally tend to ask critical questions as they aren't yet inculcated into the field's dogmas. But the academic treadmill leaves them little time to voice concerns & their lack of status means that even when they do, they aren't taken seriously.

One possible intervention is for journals and conferences to devote some fraction of their pages / slots to self-critical inquiry, and for dissertation committees to make clear that they will value this type of scholarship just as much as "normal" science.

Read 4 tweets

Arvind Narayanan

@random_walker

1 Jul

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=…

@aruneshmathur

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=…

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