Tag Archives: Franz Kafka

HAL 9000. Credit: OpenClipart-Vectors/pixabay

The tragic hero of ‘2001: A Space Odyssey’

This is something I wrote for April 10 but forgot to schedule for publication. Publishing it now…

Since news of the Cambridge Analytica scandal broke last month, many of us have expressed apprehension – often on Facebook itself – that the social networking platform has transformed since its juvenile beginnings into an ugly monster.

Such moral panic is flawed and we ought to know that by now. After all, it’s been 50 years since 2001: A Space Odyssey was released, and a 100 since Frankenstein – both cultural assets that have withstood the proverbial test of time only because they managed to strike some deep, mostly unknown chord about the human condition, a note that continues to resonate with the passions of a world that likes to believe it has disrupted the course of history itself.

Gary Greenberg, a mental health professional and author, recently wrote that the similarities between Viktor Frankenstein’s monster and Facebook were unmistakable except on one count: the absence of a conscience was a bug in the monster, and remains a feature in Facebook. As a result, he wrote, “an invention whose genius lies in its programmed inability to sort the true from the false, opinion from fact, evil from good … is bound to be a remorseless, lumbering beast, one that does nothing other than … aggregate and distribute, and then to stand back and collect the fees.”

However, it is 2001‘s HAL 9000 that continues to be an allegory of choice in many ways, not least because it’s an artificial intelligence the likes of which we’re yet to confront in 2018 but have learnt to constantly anticipate. In the film, HAL serves as the onboard computer for an interplanetary spaceship carrying a crew of astronauts to a point near Jupiter, where a mysterious black monolith of alien origin has been spotted. Only HAL knows the real nature of the mission, which in Kafkaesque fashion is never revealed.

Within the logic-rules-all-until-it-doesn’t narrative canon that science fiction writers have abused for decades, HAL is not remarkable. But take him out into space, make sure he knows more than the humans he’s guiding and give him the ability to physically interfere in people’s lives – and you have not a villain waylaid by complicated Boolean algebra but a reflection of human hubris.

2001 was the cosmic extrapolation of Kubrick’s previous production, the madcap romp Dr Strangelove. While the two films differ significantly in the levels of moroseness on display as humankind confronts a threat to its existence, they’re both meditations on how humanity often leads itself towards disaster while believing it’s fixing itself and the world. In fact, in both films, the threat was weapons of mass destruction (WMDs). Kubrick intended for the Star Child in 2001‘s closing scenes to unleash nuclear holocaust on Earth – but he changed his mind later and chose to keep the ending open.

This is where HAL has been able to step in, in our public consciousness, as a caution against our over-optimism towards artificial intelligence and reminding us that WMDs can take different forms. Using the tools and methods of ‘Big Data’ and machine learning, machines have defeated human players at chess and go, solved problems in computer science and helped diagnose some diseases better. There is a long way to go for HAL-like artificial general intelligence, assuming that is even possible.

But in the meantime, we come across examples every week that these machines are nothing like what popular science fiction has taught us to expect. We have found that their algorithms often inherit the biases of their makers, and that their makers often don’t realise this until the issue is called out – or they do but slip it in anyway.

According to (the modified) Tesler’s theorem, “AI is whatever hasn’t been done yet”. When overlaid on optimism of the Silicon Valley variety, AI in our imagination suddenly becomes able to do what we have never been able to ourselves, even as we assume humans will still be in control. We forget that for AI to be truly AI, its intelligence should be indistinguishable from that of a human’s – a.k.a. the Turing test. In this situation, why do we expect AI to behave differently than we do?

We shouldn’t, and this is what HAL teaches us. His iconic descent into madness in 2001 reminds us that AI can go wonderfully right but it’s likelier to go wonderfully wrong if only because of the outcomes that we are not, and have never been, anticipating as a species. In fact, it has been argued that HAL never went mad but only appeared to do so because of the untenability of human expectations. That 2001 was the story of his tragedy.

This is also what makes 2001 all the more memorable: its refusal to abandon the human perspective – noted for its amusing tendency to be tripped up by human will and agency – even as Kubrick and Arthur C. Clarke looked towards the stars for humankind’s salvation.

In the film’s opening scenes, a bunch of apes briefly interacts with a monolith just like the one near Jupiter and quickly develops the ability to use commonplace objects as tools and weapons. The rest is history, so the story suddenly jumps four million years ahead and then 18 months more. As the Tool song goes, “Silly monkeys, give them thumbs, they make a club and beat their brother down.”

In much the same way, HAL recalls the origins of mainstream AI research as it happened in the late 1950s at the Massachusetts Institute of Technology (MIT), Boston. At the time, the linguist and not-yet-activist Noam Chomsky had reimagined the inner workings of the human brain as those of a computer (specifically, as a “Language Acquisition Device”). According to anthropologist Chris Knight, this ‘act’ inspired cognitive scientist Marvin Minsky to wonder if the mind, in the form of software, could be separated from the body, the hardware.

Minsky would later say, “The most important thing about each person is the data, and the programs in the data that are in the brain”. This is chillingly evocative of what Facebook has achieved in 2018: to paraphrase Greenberg, it has enabled data-driven politics by digitising and monetising “a trove of intimate detail about billions of people”.

Minsky founded the AI Lab at MIT in 1959. Less than a decade later, he joined the production team of 2001 as a consultant to design and execute the character called HAL. As much as we’re fond of celebrating the prophetic power of 2001, perhaps the film was able to herald the 21st century as well as it has because we inherited it from many of the men who shaped the 20th, and Kubrick and Clarke simply mapped their visions onto the stars.

Featured image: HAL 9000. Credit: OpenClipart-Vectors/pixabay.

From Orwell to Kafka, Markov to Doctorow: Understanding Big Data through metaphors

On March 20, I attended a short talk by Malavika Jayaram, a fellow at the Berkman Center for Internet & Society, titled ‘What we talk about when we talk about Big Data’ at the T.A.J. Residency in Bengaluru. It was something of an initiation into the social and political contexts of Big Data and its usage, and the important ethical conundrums assailing these contexts.

Even if it was a little slow during the first 15 minutes, Jayaram’s talk progressed rapidly later on as she quickly piled criticism after criticism upon the concept’s foundation, which was quickly being revealed to be immature. Perhaps those familiar with Jayaram’s past research did (or didn’t) find the contents of her talk to contain more nuances than she’s let on before, but to me it revealed an array of perspectives I’ve remained balefully ignorant of.

The first in line was about the metaphors used to describe Big Data – and how our use of metaphors at all betrays our inability to comprehend Big Data in its entirety. Jayaram quoted at length but loosely from an essay by Sara M. Watson, her colleague at Berkman, titled Data is the new “____”. It describes how the dominant metaphors are industrial, dealing with the data itself as if it were a natural resource and the process of analyzing it as if it were being mined or refined.

Data as a natural resource suggests that it has great value to be mined and refined but that it must be handled by experts and large-scale industrial processes. Data as a byproduct describes the transactional traces of digital interactions but suggests it is also wasteful, pollutive, and may not be meaningful without processing. Data has also been described as a fungible resource, as an asset class, suggesting that it can be traded, stored, and protected in a data vault. One programmatic advertising professional related to me that he thinks “data is the steel of the digital economy,” an image that avoids the negative connotations of oil while at the same time expressing concern about monopolizing forces of firms Google and Facebook.

Not Orwellian but Kafkaesque

There are two casualties of this perspective. The first is the people behind the data – those whose features, actions, choices, etc. have become numbers – are forgotten even as the data they have given “birth” to becomes more important and valuable. The second casualty is the constant reminder that data is valuable, and large amounts of data more so, condemning it to a life where it can’t hope to be stagnant for long.

The dehumanization of Big Data, according to Jayaram, extends beyond analysts forgetting the data belongs to faces and names and unto the restriction of personal ownership. The people the data represents often don’t have access to it. This implies an existential anxiety quite unlike found in George Orwell’s 1984 and more like the one in Franz Kafka’s The Trial. In Jayaram’s words,

You are in prison awaiting your trial. Suddenly you find out the trial has been postponed and you have no idea why or how. There seem to be people who know things that you never will. You don’t know what you can do to encourage their decisions to keep the trial permanently postponed. You don’t know what it was about you and you have no way of changing your behavior accordingly.

In 2013, American attorney John Whitehead popularized this comparison in an article titled Kafka’s America. Whitehead argues that the sentiments of Josef K., the protagonist of The Trial, are increasingly becoming the sentiments of a common American.

Josef K’s plight, one of bureaucratic lunacy and an inability to discover the identity of his accusers, is increasingly an American reality. We now live in a society in which a person can be accused of any number of crimes without knowing what exactly he has done. He might be apprehended in the middle of the night by a roving band of SWAT police. He might find himself on a no-fly list, unable to travel for reasons undisclosed. He might have his phones or internet tapped based upon a secret order handed down by a secret court, with no recourse to discover why he was targeted. Indeed, this is Kafka’s nightmare, and it is slowly becoming America’s reality.

Kafka-biographer Reiner Stach summed up these activities as well as the steadily unraveling realism of Kafka’s book as proof of “the extent to which power relies on the complicity of its victims” – and the ‘evil’ mechanism used to achieve this state is a concern that Jayaram places among the prime contemporary problems threatening civil liberties.

If your hard drive’s not in space…

There is an added complication. If the use of Big Data was predominantly suspect, it would have been easier to build consensus against its abuse. However, that isn’t the case: Big Data is more often than not used in ways that don’t harm our personal liberties, and the misfortune is that their collective beneficence as yet has been no match for the collective harm some of its misuses have achieved. Could this be because the potential for its misuse is almost everywhere?

Yes. An often overlooked facet of using Big Data is the idea that the responsible use of Big Data is not a black-and-white deal. Facebook is not all evil and academic ethnographers are not all benign. Zuckerberg’s social network may collect and store large amounts of information that it nefariously trades with advertisers – and may even comply with the NSA’s “requests” – but there is a systematicity, an orderliness, with which the data is being passed around. The complex’s existence alone presents a problem, no doubt, but that there is a complex at all makes it easier to attempt to fix the problem than if the orderliness were absent.

And this orderliness is often absent among academicians, scholars, journalists, etc., who may not think data is a dollar note but at the same time are processing prodigious amounts of it without being as careful as is necessary about how they are logging, storing and sharing it. Jayaram rightly believes that even if information is collected for benevolent purposes, the moment it becomes data it loses its memory and stays on on the Internet as data; that if we are to be responsible data-scientists, being benevolent alone will be inadequate.

To drive the point home, she recalled a comment someone had made to her during a data workshop.

The Utopian way to secure data is to shoot your hard drive into space.

Every other recourse will only fall short.

Consent is not enough

This memoryless, Markovian character of the data-economy demands a redefinition of consent as well. The question “What is consent?” is dependent on what a person is consenting to. However, almost nobody knows how the data will be used, what for, or over what time-frames. Like a variable flowing through different parts of a computer, data can pass through a variety of contexts to each of which it provides value of varying quality. So, the same question of contextual integrity should retrospectively apply to the process of consent-giving as well: What are we consenting to when we’re consenting to something?

And when both the party asking for consent and the party asked for consent can’t know all the ways in which the data will be used, the typical way-out has been to seek consent that protects one against harm – either by ensuring that one’s civil liberties are safeguarded or by explicitly prohibiting choices that will impinge upon, again, one’s civil liberties. This has also been increasingly done in a one-size-fits-all manner that the average citizen doesn’t have the bargaining power to modify.

However, it’s become obvious by now that just protecting these liberties isn’t enough to ensure that data and consent are both promised a contextual integrity.

Why not? Because the statutes that enshrine many of these liberties is yet to be refashioned for the Internet age. In India, at least, the six fundamental rights are to equality, to freedom, against exploitation, to freedom of religion, cultural and educational rights, and to constitutional remedies. Between them, the promise of protecting against the misuse of not one’s person but one’s data is tenuous (although a recent document from the Telecom Regulatory Authority of India could soon fix this).

The Little Brothers

Anyway, an immediate consequence of this typical way-out has been that one needs to be harmed to get remedy, at a time when it remains difficult to define when one’s privacy has been harmed. And since privacy has been an enabler of human rights, even unobtrusive acts of tagging and monitoring that don’t violate the law can force compliance among the people. This is what hacker Andrew Huang talks about in his afterword to Cory Doctorow’s novel Little Brother (2008),

[In] January 2007, … Boston police found suspected explosive devices and shut down the city for a day. These devices turned out to be nothing more than circuit boards with flashing LEDs, promoting a show for the Cartoon Network. The artists who placed this urban graffiti were taken in as suspected terrorists and ultimately charged with felony; the network producers had to shell out a $2 million settlement, and the head of the Cartoon Network resigned over the fallout.

Huang’s example further weakens the Big Brother metaphor by implicating not one malevolent central authority but an epidemic, Kafkaesque paranoia that has “empowered” a multitude of Little Brothers all convinced that God is only in the detail.

While Watson’s essay (Data is the new “____”) is explicit about the power of metaphors to shape public thought, Doctorow’s book and Huang’s afterword take the next logical step in that direction and highlight the clear and present danger for what it is.

It’s not the abuse of power by one head of state but the evolution of statewide machines that (exhibit the potential to) exploit the unpreparedness of the times to coerce and compel, using as their fuel the mountainous entity – sometimes as Gargantuan as to be formless, and sometimes equally absurd – called Big Data (I exaggerate – Jayaram was more measured in her assessments – but not much).

And even if Whitehead and Stach only draw parallels between The Trial and American society, the relevant, singular “flaw” of that society exists elsewhere in the world, too: the more we surveil others, the more we’ll be surveilled ourselves, and the longer we choose to stay ignorant of what’s happening to our data, the more our complicity in its misuse. It is a bitter pill to swallow.

Featured image credit: DARPA