Many books on artificial intelligence (AI) illuminate the pathologies of AI. These pathologies are created by artificial intelligence or better as artificial “intelligment” since AI may not be that intelligent.

In addition, using the term “machine learning might be more appropriate than artificial intelligence as a recent book on AI – Democracy for the People illustrates.

Among both the many known and not so known cases of pathological AI – one might like to mention a case of child abuse. 

In this case, machine learning was used to send the right officers to the right house for the right reasons. This could – once the decision-making power is handed over to a machine – go horribly wrong once it is transferred from people to a machine.

With the warning of replacing people with a machine, the aim of the book is to explore how to make democracy work in the coming age of machine learning. 

By doing that, one might like to focus on the political characters of machine learning. Having overcome the ideological fallacies of techno-determinism, this could even lead to a political theory on machine learning. 

On politics of machine learning, machine learning offers two fundamental promises:

1. the promise of efficiency – artificial intelligence or machine learning will make everything more efficient; and 
2. the promise of fairness – artificial intelligence or machine learning will, magically, lead to a more fairness in business, administration, education, and society,

Basically, efficiency is the worn-out wet dream of management. It is one of the most favourite legitimising ideologies of management – we are efficient and make everything far more efficient, and therefore, we have a right to exist. Undeterred, the apostles of management would even promise to make “love” more efficient.   

Meanwhile, it is used to justify virtually anything management does – from corporate environmental vandalism to the everyday business criminality of companies and beyond. 

Management presents efficiency as an eternal quest behind to which all workers must obey. Meanwhile, fairness is an utterly human – if not philosophical – concept.

Fairness – in philosophical terms, or otherwise – can hardly be transferred to a machine. In short, machine learning will incur many problems on both issues: efficiency and fairness.

Rather than the false promise of fairness, machine learning systems reflect and, in some cases, not just enhance but also crank up historic inequalities. 

For one, the models used in machine learning often involve trade-offs between complexity, accuracy, and error rates. This can, intentionally, or otherwise, worsen inequalities.

Worryingly, machine learning both amplifies and obscures the power of the institutions that design and use it. 

Machine learning not only structures communication, but it also controls communication. It controls communication so that what is seen and, often more importantly, what is “not” seen supports corporations and capitalism. 

In other words, machine learning is inextricably linked to communication, domination, capitalism, and control.

To smokescreen these realities, the politics of machine learning is often buried in technical details. Yet, machine learning is about society – not mathematics. It can even become a Weapon of Math Destruction.

Beyond that, machine learning is here to turbo-charge and to put it rather politely “checkered” history of systematic racism in, for example, the US criminal justice system. On this, machine learning does three things: 

1. machine learning creates a pernicious feedback loop that reinforces stereotyping, bigotry, racism, poverty, and inequality; 
2. machine learning justifies more monitoring, more constabularies and more policing even though after 400⁺ years of prisons and the unabated continuation of crime, perhaps prisons are not the answer to crime;
3. machine learning – and this might be the worst part – is a political tool that projects the imprint of injustice into the future – it cements an unjust world.

Beyond all that still lies the profit motive of capitalism. Facebook, for example, uses machine learning to power its advertising system that distributes ads to its 2.9 billion users. 

This is not just the very point where billions of dollars come in, but it also testifies to the economic and political power of Facebook’s monopoly. 

Worse, it also explains why Mark Zuckerberg is treated like the president of a country at international meetings. Zuckerberg can reach more people than any president of any country.

While the latter is truly impressive, the key to all this lies in the first part. All in all, companies find machine learning extremely useful because it accurately predicts something genuinely useful for making a profit. 

In that undertaking, the aforementioned injustice and racism are mere – and often rather welcomed – by-products. On the latter, AI and machine learning tries to get away using the following rhetorical tool: we can’t write an algorithm that’s going to solve racism. 

True, but that is exactly what is so often done. Such algorithms are written in a way so that they – accidentally or deliberately – worsen racism. In other words, data is not really neutral. In fact, it’s the opposite of neutral.

On the equally dangerous side of the ideologies that accompany AI, it is the technology in the hands of corporate interests that rests at the centre of the systems. 

This shapes who sees what, when, and why, Facebook and Google mould the minds of billions of citizens and shape the public spheres of democracy across the world.

The raw power of both monopolies – Facebook for social networking and Google for structuring information – can be seen in the fact that over 70% of all internet traffic goes through sites owned by Facebook and Google. 

To spice up the power of Facebook even more, Facebook’s most important system is the newsfeed. Its algorithms select what consumers see and perhaps more importantly what they do not see.

It remains imperative to understand that machine learning models prioritise “some” (read: corporate) interests and values over others (read: democracy).

It is profits over people, climate change denial over global warming, corporate interest over trade union interests, Donald Trump over Kamala Harris , etc.

In other words, if people see too much lying, racism, pornography, abuse and the ultimate abuser: Donald Trump, it is because Facebook has built a ranking system that distributes and amplifies them.

The same applies to Google. What makes Google unique is its PageRank. This is an algorithm that ranks the relevance of websites to a query. 

It encodes a kind of value judgement. Worse, Google exercises control by defining concepts like equality.

To keep their dominance concealed, Facebook and Google hide their power behind anodyne technobabble. This is designed to obscure the politics of its machine learning systems.

This gives both Facebook and Google the infrastructural power to structure our public sphere. Worse, this power is unilateral, subject to neither meaningful economic competition nor effective democratic oversight. 

Just like Google, Facebook is the problem, not the solution as it weaponizes us against ourselves. 

On the old question of what is to be done?, one might suggest that corporations like Google and Facebook, and many others should, if not being replaced by a cooperative, be subjected to public oversight and democratic governance. 

It is rather unsurprising to see that well-meaning, liberal and Harvard-trained authors like Simons suddenly discover that competition has been conspicuously absent and that corporations have a monopoly position. 

Didn’t Karl Marx tell us about this in his Das Kapital about 160 years ago? Shock and horror! Google and Facebook are monopolies. Both are just another examples of what the former Harvard Business Review editor Magretta once said,

business executives are society’s leading champions of free markets and competition, words that, for them, evoke a worldview and value system that rewards good ideas and hard work, and that fosters innovation and meritocracy. Truth be told, the competition every manager longs for is a lot closer to Microsoft’s end of the spectrum than it is to the dairy farmers. All the talks about the virtues of competition notwithstanding, the aim of business strategy is to move an enterprise away from perfect competition and in the direction of monopoly.

Denying the obvious, many good liberals argue that to regulate Facebook and Google is precisely what we should do. 

In other words, regulate capitalism and corporations and all will be fine. For that, one surprisingly good suggestion is to advocate that Google and Facebook – and many other IT corporations – should be regulated like a public utility – like water, the postal service, the sewage, public waterways, etc. 

This is the idea of what the author calls, ”Regulating for Democracy”. In other words, a democratic form of state regulation is within the realm of achievable possibilities. One might even formulate a detailed plan on how this can be achieved.

In the end, democracy cannot be automated – not by artificial intelligence nor by machine learning nor by any other automation system. 

Beyond that, transferring Internet corporations into public utilities would also mean that such a move will require reforming the administrative state and changing how we think about policy making itself.

The idea of transferring Internet corporations into public utilities or at least treat them as such under a state issued regulatory framework is sensible. 

After all, they hold monopolistic power. Google and Facebook can spread misinformation (accidentally) and disinformation (deliberately). 

Under the ideological justification of networking millions – if not billions of people – the behemoth-like advertising giant Facebook feeds users with advertising and news its algorithm deems relevant or not. 

Both corporations – Facebook and Google – more or less decide what, when, and why we see or read things – as well as what we do not see. Worse, they also have the power to hide things from the general public. This alone demands regulation and democratic oversight.

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