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Article Article June 4th, 2018

Why artificial intelligence can't solve all of government’s problems

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Governments should pause and take a deep breath when it comes to #AI, says @DrPolonski. It's not going to solve all their problems...

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Simply adding a neural network to a democracy does not mean it will be instantly more inclusive, fair or personalised. says @DrPolonski #AI

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Using AI simply for the sake of AI may not always be productive or useful, says @DrPolonski – not everything that can be automated should be

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30-second summary

  • Although media headlines imply we are already living in a future where AI has infiltrated every aspect of society, this actually sets unrealistic expectations about what AI can really do for humanity
  • Governments are racing to pledge support AI initiatives but they tend to understate the complexity around deploying advanced machine learning systems in the real world.
  • There is no AI solution for everything. All solutions come at a cost and not everything that can be automated should be.

The hysteria about the future of artificial intelligence (AI) is everywhere. There seems to be no shortage of sensationalist news about how AI could cure diseasesaccelerate human innovation and improve human creativity. Just looking at the media headlines, you might think that we are already living in a future where AI has infiltrated every aspect of society.

While it is undeniable that AI has opened up a wealth of promising opportunities, it has also led to the emergence of a mindset that can be best described as “AI solutionism”. This is the philosophy that given enough data, machine learning algorithms can solve all of humanity's problems.

But there's a big problem with this idea. Instead of supporting AI progress, it actually jeopardises the value of machine intelligence by disregarding important AI safety principles and setting unrealistic expectations about what AI can really do for humanity.

The rise of AI solutionism

In only a few years, AI solutionism has made its way from the technology evangelists' mouths in Silicon Valley to the minds of government officials and policymakers around the world. The pendulum has swung from the dystopian notion that AI will destroy humanity to the utopian belief that our algorithmic saviour is here.

We are now seeing governments pledge support to national AI initiatives and compete in a technological and rhetorical arms race to dominate the burgeoning machine learning sector. For example, the UK government has vowed to invest £300m in AI research to position itself as a leader in the field. Enamoured with the transformative potential of AI, the French president, Emmanuel Macron, committed to turn France into a global AI hub. Meanwhile, the Chinese government is increasing its AI prowess with a national plan to create a Chinese AI industry worth $150bn by 2030.

Many countries are hoping to dominate the fourth industrial revolution. AI solutionism is on the rise and it is here to stay.

False promises or flawed premises?

While many political manifestos tout the transformative effects of the looming “AI revolution”, they tend to understate the complexity around deploying advanced machine learning systems in the real world.

One of the most promising varieties of AI technologies are neural networks. This form of machine learning is loosely modelled after the neuronal structure of the human brain but on a much smaller scale. Many AI-based products use neural networks to infer patterns and rules from large volumes of data. But what many politicians do not understand is that simply adding a neural network to a problem will not automatically mean that you'll find a solution.

If policymakers start deploying neural networks left and right, they cannot assume that the AI will instantly make our government institutions more agile or efficient. Simply adding a neural network to a democracy does not mean it will be instantaneously more inclusive, fair or personalised.

Public sector impediments

AI systems need a lot of data to function, but the public sector typically does not have the appropriate data infrastructure to support advanced machine learning. Most of the data remains stored in offline archives. The few digitised sources of data that exist tend to be buried in bureaucracy. More often than not, data is spread across different government departments that each require special permissions to be accessed. Above all, the public sector typically lacks the human talent with the right technological capabilities to fully reap the benefits of machine intelligence.

Furthermore, one of the many difficulties in deploying machine learning systems is that AI is extremely susceptible to adversarial attacks. This means that a malicious AI can target another AI to force it to make the wrong prediction or to behave in a certain way. Many researchers have warned against the rolling out of AI without appropriate security standards and defence mechanisms. Still, AI security remains an often-overlooked topic in the political rhetoric of policymakers.

An expensive instrument

If we are to reap the benefits and minimise the potential harms of AI, we must start thinking about how machine learning can be meaningfully applied to specific areas of government, business and society. This means we need to have a discussion about AI ethics and the distrust that many people have towards machine learning.

Most importantly, we need to be aware of the limitations of AI and where humans still need to take the lead. Instead of painting an unrealistic picture of the power of AI, it is important to take a step back and separate actual technological capabilities of AI from magic.

Several examples from medicine, law and social media demonstrate that there is no AI solution for everything. Using AI simply for the sake of AI may not always be productive or useful. Not every problem is best addressed by applying machine intelligence to it.

In an almost prophetic treatise published in 1964, the American philosopher Abraham Kaplan described this tendency as the “law of the instrument”.

It was formulated as follows: “Give a small boy a hammer, and he will find that everything he encounters needs pounding.” Only this time, the mentality of Kaplan's small boy is shared by influential world leaders and the AI hammer is not only very powerful, but also very expensive.

Thus, this is the crucial lesson for everyone aiming to boost investments in national AI programmes: all solutions come at a cost and not everything that can be automated should be.



Written by:

Dr Vyacheslav Polonski Network Scientist at the University of Oxford, Founder and CEO of Avantgarde Analytics
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