The sinful algorithm

In 1318, amidst a public sentiment that was less than enthusiastic about King Edward II, a young clerk from Oxford, John Deydras of Powderham, claimed to be the rightful ruler of England. He spun a long and rather fantastic tale that involved sows biting off the ears of children and other assorted lunacy.1It is now more or less consensus that Deydras was mentally ill and made the whole story up. Whether he himself believed it or not is another question. Edward II took much better to the pretender than his wife, the all-around badass Isabella of France, who was embarrassed by the whole affair, and Edward’s barons, who feared more sedition if they let this one slide. As such, eventually, Deydras was tried for sedition.

Deydras’s defence was that he has been convinced to engage in this charade by his cat, through whom the devil appeared to him.2As an obedient servant to a kitten, I have trouble believing this! That did not meet with much leniency, it did however result in one of the facts that exemplified the degree to which medieval criminal jurisprudence was divorced from reason and reality: besides Deydras, his cat, too, was tried, convicted, sentenced to death and hung, alongside his owner.

Before the fashionable charge of unreasonableness is brought against the Edwardian courts, let it be noted that other times and cultures have fared no better. In the later middle ages, it was fairly customary for urban jurisdictions to remove objects that have been involved in a crime beyond the city limits, giving rise to the term extermination (ex terminare, i.e., [being put] beyond the ends).3Falcón y Tella, Maria J. (2014). Justice and law, 60. Brill Nijhoff, Leiden The Privileges of Ratisbon (1207) allowed the house in which a crime took place or which harboured an outlaw to be razed to the ground – the house itself was as guilty as its owner.4Falcón y Tella, Maria J. and Falcón y Tella, Fernando (2006). Punishment and Culture: a right to punish? Nijhoff, Leiden. And even a culture as civilised and rationalistic as the Greeks fared no better, falling victim to the same surge of unreason. Hyde describes

The Prytaneum was the Hôtel de Ville of Athens as of every Greek town. In it was the common hearth of the city, which represented the unity and vitality of the community. From its perpetual fire, colonists, like the American Indians, would carry sparks to their new homes, as a symbol of fealty to the mother city, and here in very early times the prytanis or chieftain probably dwelt. In the Prytaneum at Athens the statues of Eirene (Peace) and Hestia (Hearth) stood; foreign ambassadors, famous citizens, athletes, and strangers were entertained there at the public expense; the laws of the great law-giver Solon were displayed within it and before his day the chief archon made it his home.
One of the important features of the Prytaneum at Athens were the curious murder trials held in its immediate vicinity. Many Greek writers mention these trials, which appear to have comprehended three kinds of cases. In the first place, if a murderer was unknown or could not be found, he was nevertheless tried at this court. Then inanimate things – such as stones, beams, pliece of iron, etc., – which had caused the death of a man by falling upon him-were put on trial at the Prytaneum, and lastly animals, which had similarly been the cause of death.
Though all these trials were of a ceremonial character, they were carried on with due process of law. Thus, as in all murder trials at Athens, because of the religious feeling back of them that such crimes were against the gods as much as against men, they took place in the open air, that the judges might not be contaminated by the pollution supposed to exhale from the prisoner by sitting under the same roof with him.
[T]he trial of things, was thus stated by Plato:
“And if any lifeless thing deprive a man of life, except in the case of a thunderbolt or other fatal dart sent from the gods – whether a man is killed by lifeless objects falling upon him, or his falling upon them, the nearest of kin shall appoint the nearest neighbour to be a judge and thereby acquit himself and the whole family of guilt. And he shall cast forth the guilty thing beyond the border.”
Thus we see that this case was an outgrowth from, or amplification of the [courts’ jurisdiction trying and punishing criminals in absentia]; for if the murderer could not be found, the thing that was used in the slaying, if it was known, was punished.5Hyde, Walter W. (1916). The Prosecution and Punishment of Animals and Lifeless Things in the Middle Ages and Modern Times. 64 U.Pa.LRev. 696.

Looking at the current wave of fashionable statements about the evils of algorithms have reminded me eerily of the superstitious pre-Renaissance courts, convening in damp chambers to mete out punishments not only on people but also on impersonal objects. The same detachment from reality, from the Prytaneum through Xerxes’s flogging of the Hellespont through hanging cats for being Satan’s conduits, is emerging once again, in the sophisticated terminology of ‘systematized biases’:

Clad in the pseudo-sophistication of a man who bills himself as ‘one of the world’s leading thinkers‘, a wannabe social theorist with an MBA from McGill and a career full of buzzwords (everything is ‘foremost’, ‘agenda-setting’ or otherwise ‘ultimative’!) that now apparently qualifies him to discuss algorithms, Mr Haque makes three statements that have now become commonly accepted dogma among certain circles when discussing algorithms.

  1. Algorithms are means to social control, or at the very least, social influence.
  2. Algorithms are made by a crowd of ‘geeks’, a largely homogenous, socially self-selected group that’s mostly white, male, middle to upper middle class and educated to a Masters level.
  3. ‘Systematic biases’, by which I presume he seeks to allude to the concept of institutional -isms in the absence of an actual propagating institution, mean that these algorithms are reflective of various biases, effectively resulting in (at best) disadvantage and (at worst) actual prejudice and discrimination against groups that do not fit the majority demographic of those who develop code.

Needless to say, leading thinkers and all that, this is absolute, total and complete nonsense. Here’s why.

A geek’s-eye view of algorithms

We live in a world governed by algorithms – and we have ever since men have mastered basic mathematics. The Polynesian sailors navigating based on stars and the architects of Solomon’s Temple were no less using algorithms than modern machine learning techniques or data mining outfits are. Indeed, the very word itself is a transliteration of the name of the 8th century Persian mathematician Al-Khwarazmi.6Albeit what we currently regard as the formal definition of an algorithm is largely informed by the work of Hilbert in the 1920s, Church’s lambda calculus and, eventually, the emergence of Turing machines. And for most of those millennia of unwitting and untroubled use of algorithms, there were few objections.

The problem is that algorithms now play a social role. What you read is determined by algorithms. The ads on a website? Algorithms. Your salary? Ditto. A million other things are algorithmically calculated. This has endowed the concept of algorithms with an air of near-conspiratorial mystery. You totally expect David Icke to jump out of your quicksort code one day.

Whereas, in reality, algorithms are nothing special to ‘us geeks’. They’re ways to do three things:

  1. Execute things in a particular order, sometimes taking the results of previous steps as starting points. This is called sequencing.
  2. Executing things a particular number of times. This is called iteration.
  3. Executing things based on a predicate being true or false. This is conditionality.

From these three building blocks, you can literally reconstruct every single algorithm that has ever been used. There. That’s all the mystery.

So quite probably, what people mean when they rant about ‘algorithms’ is not the concept of algorithms but particular types of algorithm. In particular, social algorithms, content filtering, optimisation and routing algorithms are involved there.

Now, what you need to understand is that geeks care relatively little about the real world ‘edges’ of problems. They’re not doing this out of contempt or not caring, but rather to compartmentalise problems to manageable little bits. It’s easier to solve tiny problems and make sure the solutions can interoperate than creating a single, big solution that eventually never happens.

To put it this way: to us, most things, if not everything, is an interface. And this largely determines what it means when we talk about the performance of an algorithm.

Consider your washing machine: it can be accurately modelled in the following way.

The above models a washing machine. Given supply water and power, if you put in dirty clothes and detergent, you will eventually get clean clothes and grey water. And that is all.
The above models a washing machine. Given supply water and power, if you put in dirty clothes and detergent, you will eventually get clean clothes and grey water. And that is all.

Your washing machine is an algorithm of sorts. It’s got parameters (water, power, dirty clothes) and return values (grey water, clean clothes). Now, as long as your washing machine fulfils a certain specification (sometimes called a promise7I discourage the promise terminology here as I’ve seen it confuzzled with the asynchronous meaning of the word way too often or a contract), according to which it will deliver a given set of predictable outputs to a given set of inputs, all will be well. Sort of.

“Sort of”, because washing machines can break. A defect in an algorithm is defined as ‘betraying the contract’, in other words, the algorithm has gone wrong if it has been given the right supply and yields the wrong result. Your washing machine might, however, fail internally. The motor might die. A sock might get stuck in it. The main control unit might short out.

Now consider the following (extreme simplification of an) algorithm. MD5 is what we call a cryptographic hash function. It takes something – really, anything that can be expressed in binary – and gives a 128-bit hash value. On one hand, it is generally impossible to invert the process (i.e. it is not possible to conclusively deduce what the original message was), while at the same time the same message will always yield the same hash value.

Without really having an understanding of what goes on behind the scenes,8In case you’re interested, RFC1321 explains MD5’s internals in a lot of detail. you can rely on the promise given by MD5. This is so in every corner of the universe. The value of MD5("Hello World!") is 0xed076287532e86365e841e92bfc50d8c in every corner of the universe. It was that value yesterday. It will be that value tomorrow. It will be that value at the heat death of the universe. What we mean when we say that an algorithm is perfect is that it upholds, and will uphold, its promise. Always.

At the same time, there are aspects of MD5 that are not perfect. You see, perfection of an algorithm is quite context-dependent, much as the world’s best, most ‘perfect’ hammer is utterly useless when what you need is a screwdriver. As such, for instance, we know that MD5 has to map every possible bit value of every possible length to a limited number of possible hash values (128 bit worth of values, to be accurate, which equates to 2^128 or approximately 3.4×10^38 distinct values). These seem a lot, but are actually teensy when you consider that they are used to map every possible amount of binary data, of every possible length. As such, it is known that sometimes different things can have the same hash value. This is called a ‘collision’, and it is a necessary feature of all hash algorithms. It is not a ‘fault’ or a ‘shortcoming’ of the algorithm, no more than we regard the non-commutativity of division a ‘shortcoming’.

Which is why it’s up to you, when you’re using an algorithm, to know what it can and cannot do. Algorithms are tools. Unlike the weird perception in Mr Haque’s swirl of incoherence, we do not worship algorithms. We don’t tend to sacrifice small animals to quicksort and you can rest assured we don’t routinely bow to a depiction of binary search trees. No more do we believe in the ‘perfection’ of algorithms than a surgeon believes in the ‘perfection’ of his scalpel or a pilot believes in the ‘perfection’ of their aircraft. Both know their tools have imperfections. They merely rely on the promise that if used with an understanding of its limitations, you can stake your, and others’, lives on it. That’s not tool-worship, that’s what it means to be a tool-using human being.

The Technocratic Spectre

We don’t know the name of the first human who banged two stones together to make fire, and became the archetype for Prometheus, but I’m rather sure he was rewarded by his fellow humans rewarded with very literally tearing out his very literal and very non-regrowing liver. Every progress in the history of humanity had those who not merely feared progress and the new, but immediately saw seven kinds of nefarious scheming behind it. Beyond (often justified!) skepticism and a critical stance towards new inventions and a reserved approach towards progress (all valid positions!), there is always a caste of professional fear-mongerers, who, after painting a spectre of disaster, immediately proffer the solution: which, of course, is giving them control over all things new, for they are endowed with the mythical talents that one requires to be so presumptuous as to claim to be able to decide for others without even hearing their views.

The difference is that most people have become incredibly lazy. The result is that there is now a preference for fear over informed understanding that comes at the price of investing some time in reading up on the technologies that now are playing such a transformative role. How many Facebook users do you think have re-posted the “UCC 1-308 and Rome Statute” nonsense? And how many of them, you reckon, actually know how Facebook uses their data? While much of what they do is proprietary, the Facebook graph algorithms are partly primitives9Building blocks commonly used that are well-known and well-documented and partly open. If you wanted, you could, with a modicum of mathematical and computing knowledge, have a good stab at understanding what is going on. On the other hand, posting bad legalese is easier. Much easier.

And thus, as a result, we have a degree of skepticism towards ‘algorithms’, mostly by people like Mr Haque who do not quite understand what they are talking about and are not actually referring to algorithms but their social use.

And there lieth the Technocratic Spectre. It has always been a fashionable argument against progress, good or ill, that it is some mysterious machination by a scientific-technical elite aimed at the common man’s detriment. There is now a new iteration of this philosophy, and it is quite surprising how the backwards, low-information edges of the far right reach hands to the far left’s paranoid and misinformed segment. At least the Know-Nothings of the right live in an honest admission of ignorance, eschewing the over-blown credentials and inflated egos of their left-wing brethren like Mr Haque. But in ignorance, they both are one another’s match.

The left-wing argument against technological progress is an odd one, for the IT business, especially the part heavy on research and innovation that comes up with algorithms and their applications, is a very diverse and rather liberal sphere. Nor does this argument square too well with the traditional liberal values of upholding civil liberties, first and foremost that of freedom of expression and conscience. Instead, the objective seems to be an ever more expansive campaign, conducted entirely outside parliamentary procedure (basing itself on regulating private services from the inside and a goodly amount of shaming people into doing their will through the kind of agitated campaigning that I have never had the displeasure to see in a democracy), of limiting the expression of ideas to a rather narrowly circumscribed set, with the pretense that some minority groups are marginalised and even endangered by wrongthink.10Needless to say, a multiple-times-over minority in IT, the only people who have marginalised and endangered me were these stalwart defenders of the right never to have to face a controversial opinion.

Their own foray at algorithms has not fared well. One need only look at the misguided efforts of a certain Bay Area developer notorious for telling people to set themselves on fire. Her software, intended to block wrongthink on the weirder-than-weird cultural phenomenon of Gamergate by blocking Twitter users who have followed a small number of acknowledged wrongthinkers, expresses the flaws of this ideology beautifully. Not only is subtleness and a good technical understanding lacking. There is also a distinct shortage of good common sense and, most of all, an understanding of how to use algorithms. While terribly inefficient and horrendously badly written 11Especially for someone who declaims, with pride, her 15-year IT business experience…, the algorithm behind the GGAutoblocker is sound. It does what its creator intended it to do on a certain level: allow you to block everyone who is following controversial personalities. That this was done without an understanding of the social context (e.g. that this is a great way to block the uncommitted and those who wish to be as widely informed as possible, is of course the very point.

The problem is not with “geeks”.

The problem is when “geeks” decide to play social engineering. Whey they suddenly throw down their coding gear and decide they’re going to transform who talks with whom and how information is exchanged. The problem is exactly the opposite: it happens when geeks cease to be geeks.

It happens when Facebook experiments with users’ timelines without their consent. It happens when companies implement policies aimed at a really laudable goal (diversity and inclusion) that leads to statements by employees that should make any sane person shudder (You know who you are, Bay Area). It happens when Twitter decides they are going to experiment with their only asset. This is how it is rewarded.

$TWTR crash
Top tip: when you’ve got effectively no assets to speak of, screwing with your user base can be very fatal very fast.

The problem is not geeks seeing a technical solution to every socio-political issue.

The problem is a certain class of ‘geeks’ seeing a socio-political use to every tool.

Sins of the algorithm

Why algorithms? Because algorithms are infinitely dangerous: because they are, as I noted above, within their area of applicability universally true and correct.

But they’re also resilient. An algorithm feels no shame. An algorithm feels no guilt. You can’t fire it. You can’t tell them to set themselves on fire or, as certain elements have done to me for a single statistical analysis, threaten to rape my wife and/or kill me. An algorithm cannot be guilted into ‘right-think’. And worst of all, algorithms cannot be convincingly presented as having an internal political bias. Quicksort is not Republican. R/B trees are not Democrats. Neural nets can’t decide to be homophobic.

And for people whose sole argumentation lies on the plane of politics, in particular grievance and identity politics, this is a devastating strike. Algorithms are the greased eels unable to be framed for the ideological sins that are used to attack and remove undesirables from political and social discourse. And to those who wish to govern this discourse by fear and intimidation, a bunch of code that steadfastly spits out results and to hell with threats is a scary prospect.

And so, if you cannot invalidate the code, you have to invalidate the maker. Algorithms perpetuate real equality by being by definition unable to exercise the same kind of bias humans do (not that they don’t have their own kind of bias, but the similarity ends with the word – if your algorithm has a racial or ethnic or gender bias, you’re using it wrong). Algorithms are meritocratic, being immune to nepotism and petty politicking. A credit scorer does not care about your social status the way Mr Jones at the bank might privilege the child of his golf partners over a young unmarried ethnic couple. Trading algorithms don’t care whether you’re a severely ill young man playing the markets from hospital.12It was a great distraction. Without human intervention, algorithms have a purity and lack of bias that cannot easily be replicated once humans have touched the darn things.

And so, those whose stock in life is a thorough education in harnessing grievances for their own gain are going after “the geeks”.

Perhaps the most disgusting thing about Mr Haque’s tweet is the contraposition between “geeks” and “regular humans”, with the assumption that “regular humans” know all about algorithms and unlike the blindly algorithm-worshipping geeks, understand how ‘life is more complicated’ and algorithms are full of geeky biases.

For starters, this is hard to take seriously when in the same few tweets, Mr Haque displays a lack of understanding of algorithms that doesn’t befit an Oregon militia hick, never mind somebody who claims spurious credentials as a foremost thinker.

“Regular humans”, whatever they are that geeks aren’t (and really, I’m not one for geek supremacy, but if Mr Haque had spent five minutes among geeks, he’d know the difference is not what, and where, he thinks it is), don’t have some magical understanding of the shortcomings of algorithms. Heck, usually, they don’t have a regular understanding of algorithms, never mind magical. But it sure sounds good when you’re in the game of shaming some of the most productive members of society unless they contribute to the very problem you’re complaining about. For of course ‘geeks’ can atone for their ‘geekdom’ by becoming more of a ‘regular human’, by starting to engage in various ill-fated political forays that end with the problems that sent the blue bird into a dive on Friday.

Little of this is surprising, though. Anyone who has been paying attention could see the warning signs of a forced politicisation of technology, under the guise of making it more equal and diverse. In my experience, diverse teams perform better, yield better results, work a little faster, communicate better and make fewer big mistakes (albeit a little more small ones). In particular, gender-diverse and ethnically diverse teams are much more than the sum of their parts. This is almost universally recognised, and few businesses that have intentionally resisted creating diverse, agile teams have fared well in the long run.13Not that any statement about this matter is not shut down by reference to ludicrous made-up words like ‘mansplaining’. I’m a huge fan of diversity – because it lives up to a meritocratic ideal, one to which I am rather committed after I’ve had to work my way into tech through a pretty arduous journey.

Politicising a workplace, on the other hand, I am less fond of. Quite simply, it’s not our job. It’s not our job, because for what it’s worth, we’re just a bunch of geeks. There are things we’re better at. Building algorithms is one.

But they are now the enemy. And because they cannot be directly attacked, we’ll become targets. With the passion of a zealot, it will be taught that algorithms are not clever mathematical shortcuts but merely geeks’ prejudices expressed in maths.

And that’s a problem. If you look into the history of mathematics, most of it is peppered by people who held one kind of unsavoury view or another. Moore was a virulent racist. Pauli loved loose women. Half the 20th century mathematicians were communists at some point of their career. Haldane thought Stalin was a great man. And I could go on. But I don’t, because it does not matter. Because they took part in the only truly universal human experience: discovery.

But discovery has its enemies and malcontents. The attitude they display, evidenced by Haque’s tweet too, is ultimately eerily reminiscent of the letter that sounded the death knell on the venerable pre-WW II German mathematical tradition. Titled Kunst des Zitierens (The Art of Citing), it was written in 1934 by Ludwig Bieberbach, a vicious anti-Semite and generally unpleasant character, who was obsessed with the idea of a ‘German mathematics’, free of the Hilbertian internationalism, of what he saw as Jewish influence, of the liberalism of the German mathematical community in the inter-war years. He writes:

“Ein Volk, das eingesehen hat, wie fremde Herrschaftsgelüste an seinem Marke nagen, wie Volksfremde daran arbeiten, ihm fremde Art aufzuzwingen, muss Lehrer von einem ihm fremden Typus ablehnen.”


“A people that has recognised how foreign ambitions of power attack its brand, how aliens work on imposing foreign ways on it, has to reject teachers from a type alien to it.”

Algorithms, and the understanding of what they do, protect us from lunatics like Bieberbach. His ‘German mathematics’, suffused with racism and Aryan mysticism, was no less delusional than the idea that a cabal of geeks is imposing a ‘foreign way’ of algorithmically implementing their prejudices, as if geeks actually cared about that stuff.

Every age will produce its Lysenko and its Bieberbach, and every generation has its share of zealots that demand ideological adherence and measure the merit of code and mathematics based on the author’s politics.

Like on Lysenko and Bieberbach, history will have its judgment on them, too.

Head image credits: Max Slevogt, Xerxes at the Hellespont (Allegory on Sea Power). Bildermann 13, Oct. 5, 1916. With thanks to the President and Fellows of Harvard College.

References   [ + ]

1. It is now more or less consensus that Deydras was mentally ill and made the whole story up. Whether he himself believed it or not is another question.
2. As an obedient servant to a kitten, I have trouble believing this!
3. Falcón y Tella, Maria J. (2014). Justice and law, 60. Brill Nijhoff, Leiden
4. Falcón y Tella, Maria J. and Falcón y Tella, Fernando (2006). Punishment and Culture: a right to punish? Nijhoff, Leiden.
5. Hyde, Walter W. (1916). The Prosecution and Punishment of Animals and Lifeless Things in the Middle Ages and Modern Times. 64 U.Pa.LRev. 696.
6. Albeit what we currently regard as the formal definition of an algorithm is largely informed by the work of Hilbert in the 1920s, Church’s lambda calculus and, eventually, the emergence of Turing machines.
7. I discourage the promise terminology here as I’ve seen it confuzzled with the asynchronous meaning of the word way too often
8. In case you’re interested, RFC1321 explains MD5’s internals in a lot of detail.
9. Building blocks commonly used that are well-known and well-documented
10. Needless to say, a multiple-times-over minority in IT, the only people who have marginalised and endangered me were these stalwart defenders of the right never to have to face a controversial opinion.
11. Especially for someone who declaims, with pride, her 15-year IT business experience…
12. It was a great distraction.
13. Not that any statement about this matter is not shut down by reference to ludicrous made-up words like ‘mansplaining’.

2 thoughts on “The sinful algorithm

  1. Wow, such a long reply to a short tweet. I think his statement that there is a general fallacy of those who study algorithms to consider it the embodiment of impartiality without consideration of the context around it. But to end users, there are systematized biases and value judgments that do unintentional and perpetuate social harm. I don’t think that’s very controversial.

    Like, when you think about, for Facebook, how hard the problem of the finding a balance on the trending page of being an echo chamber vs a diverse wall of misinformed clickbait, People are complicated. Whenever people are involved, special social rules apply and it’s hard to codify those rules. And sometimes, makers forget about that. That’s all he’s saying. Seemed pretty apparent to me.

    Going off on a rant like this on a single tweet seems a bit much to me.

    1. Is it? Algorithms run or co-run so many of our social institutions that hostility (rather than mere skepticism) towards them is pretty much questioning some basic allocative, distributive and other life-sustaining mechanisms of society. Most of all, it’s questioning them on the fallacious basis that the people developing an algorithm leave an irrevocable footprint thereon, or that they regularly (or even sometimes!) resolve issues based on what they think or feel about society. This just doesn’t square with experience.

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