hisham hm

🔗 The algorithm did it!

Earlier today, statistician Kareem Carr posted this interesting tweet, about what people out there mean when they say “algorithm”, which I found to be a good summary:

When people say “algorithms”, they mean at least four different things:

1. the assumptions and description of the model

2. the process of fitting the model to the data

3. the software that implements fitting the model to the data

4. The output of running that software

Unsurprisingly, this elicited a lot of responses from computer scientists, raising the point that this is not what the word algorithm is supposed to mean (you know, a well-defined sequence of steps transforming inputs into outputs, the usual CS definition), including a response from Grady Booch, a key figure in the history of software engineering.

I could see where both of them were coming from. I responed that Carr’s original tweet not was about what programmers mean when we say “algorithms” but what the laypeople mean when they say it or read it in the media. And understanding this distinction is especially important because variations of “the algorithm did it!” is the new favorite excuse of policymakers in companies and governments alike.

Booch responded to me, clarifying that his point is that “even most laypeople don’t think any of those things”, which I agree with. People have a fuzzy definition of what an algorithm is, at best, and I think Carr’s list encompasses rather well the various things that are responsible for the effects that people credit on a vague notion of “algorithm” when people use that term.

Booch also added that “it’s appropriate to establish and socialize the correct meaning of words”, which simultaneously extends the discussion to a wider scope and also focuses it to the heart of the matter about the use of “algorithm” in our current society.

You see, it’s not about holding on to the original meaning of a word. I’m sure a few responses to Carr were of the pedantic variety, “that’s not what the dictionary says!” kind of thing. But that’s short-sighted, taking a prescriptivist rather than descriptivist view of language. Most of us who care about language are past that debate now, and those of us who adhere to the sociolinguistic view of language even celebrate the fact language shifts, adapts and evolves to suit the use of its speakers.

Shriram Krishnamurthi, CS professor at Brown, joined in on the conversation, observing that this shift in the language as a fait accompli:

I’ve been told by a public figure in France (who is herself a world-class computer scientist) — who is sometimes called upon by shows, government, etc. — that those people DO very much use the word this way. As an algorithms researcher it irks her, but that’s how it is.

Basically, we’ve lost control of the world “algorithm”. It has its narrow meaning but it also has a very broad meaning for which we might instead use “software”, “system”, “model”, etc.

Still, I agreed with Booch that this is still a fight worth fighting. But not to preserve our cherished technical meaning of the term, to the dismay of the pedants among our ranks, but because of the observation of the very circumstances that led to this linguistic shift.

The use of “algorithm” as a vague term to mean “computers deciding things” has a clear political intent: shifting blame. Social networks boosting hate speech? Sorry, the recommendation algorithm did it. Racist bias in criminal systems? Sorry, it was the algorithm.

When you think about it, from a linguistic point of view, it is as nonsensical as saying that “my hammer assembled the shelf in my living room”. No, I did, using the hammer. Yet, people are trained to use such constructs all the time: “the pedestrian was hit by a car”. Note the use of passive voice to shift the focus away from the active subject: “a car hit a pedestrian” has a different ring to it, and, while still giving agency to a lifeless object, is one step closer to making you realize that it was the driver who hit the pedestrian, using the car, just like it was I who built the shelf, using the hammer.

This of course leads to the “guns don’t kill people, people kill people” response. Yes, it does, and the exact same questions regarding guns also apply regarding “algorithms” — and here I use the term in the “broader” sense as put forward by Carr and observed by Krishnamurthi. Those “algorithms” — those models, systems, collections of data, programs manipulating this data — wield immense power in our society, even, like guns, resulting in violence, and like guns, deserving scrutiny. And when those in possession of those “algorithms” go under scrutiny, they really don’t like it. One only needs to look at the fallout resulting from the work by Bender, Gebru, McMillan-Major and Mitchell, about the dangers of extremely large language models in machine learning. Some people don’t like hearing the suggestion that maybe overpowered weapons are not a good idea.

By hiding all those issues behind the word “algorithm”, policymakers will always find a friendly computer scientist available to say that yes, an algorithm is a neutral thing, after all, it’s just a sequence of instructions, and they will no doubt profit from this confusion of meanings. And I must clarify that by policymakers I mean those both in public and private sphere, since policies put forward by the private tech giants on their platforms, where we spend so much of our lives, are as effecting on our society as public policies nowadays.

So what do we do? I don’t think it is productive to start well-actually-ing anyone who uses “algorithm” in the broader sense, with a pedantic “Let me interject for a moment — what you mean by algorithm is in reality a…”. But it is productive to spot when this broad term is being used to hide something else. “The algorithm is biased” — What do you mean, the outputs are biased? Why, is the input data biased? The people manipulating that data created a biased process? Who are they? Why did they choose this process and not another? These are better interjections to make.

These broad systems described by Carr above ultimately run on code. There are algorithms inside them, processing those inputs, generating those outputs. The use of “algorithm” to describe the whole may have started as a harmless metonymy (like when saying “White House” to refer to the entire US government), but it has since been proven very useful as a deflection tactic. By using a word that people don’t understand, the message is “computers doing something you don’t understand and shouldn’t worry about”, using “algorithm” handwavily to drift people’s minds away from the policy issues around computation, the same way “cloud” is used with data: “your data? don’t worry, it’s in the cloud”.

Carr is right, these are all things encompassing things that people refer to as “algorithms” nowadays. Krishnamurthi is right, this broad meaning is a reality in modern language. And Booch is right when he says that “words matter; facts matter”.

Holding words to their stricter meanings merely due to our love for the language-as-we-were-taught is a fool’s errand; language changes whether we want it or not. But our duty as technologists is to identify the interplay of the language, our field, and society, how and why they are being used (both the language and our field!). We need to clarify to people what the pieces at play really are when they say “algorithm”. We need to constantly emphasize to the public that there’s no magic behind the curtain, and, most importantly, that all policies are due to human choices.


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