We Humans and the Intelligent Machines

Defeating cancer before it develops. Stopping crime before it happens. Getting the dream job without the right connections. Serving justice freed from subconscious prejudices. All of that sounds auspicious, yet the negative narrative is just as impressive: healthcare systems which are no longer based on social solidarity, minority groups which suddenly find themselves disadvantaged, individuals who are completely excluded from the job market. In this scenario, people become playthings, the victims of digitally determined probabilities. Whether promise or peril – the changes will be radical.

In their book “We Humans and the Intelligent Machines”, Jörg Dräger and Ralph Müller-Eiselt illustrate the scope of algorithms and demonstrate their social relevance in more than 40 vivid and surprising case studies. The book is neither dystopia nor utopia. It illustrates the opportunities for a better society without losing sight of the risks of algorithms.

We humans are not perfect: too much information overwhelms us, we make inconsistent decisions and we discriminate unconsciously. As “augmented intelligence”, algorithms help us to compensate for human weaknesses. In many areas this has long been reality, often without us being really aware of it. Yet the line between more equal opportunities and more social inequality is a fine one. Algorithms can also reinforce discrimination and social prejudice. Artificial intelligence and algorithms are only as good as we make them. People formulate the goals of a software, they program its code and use it in a very concrete context.

The authors are certain: 

We can use them to make our society fairer, more efficient and more humane. Achieving this is the political task of our time.

To put algorithms at the service of society, they propose four concrete solutions:

First, a broad societal debate. We as society must discuss where we use algorithms, for what purpose and according to what rules. Not all algorithmic systems are equally relevant to our society. But where intelligent machines determine our lives, there must be a public debate about their goals, their design and their effect.

Second, effective control. Algorithmic decisions must be clearly recognizable as such and be verifiable, comprehensible and contestable in case of doubt by users or independent third parties. This does not require a standardized technical inspection for every single algorithm or even a separate algorithmic law. Instead, the legislator should above all supplement existing laws and strengthen long-established control institutions such as financial supervision or drug control authorities.

Third, diversity and competition. Monopolies are harmful, as everywhere else, when algorithms are used. Only a diversity of algorithmic systems and goals can adequately represent social plurality, avoid discrimination and promote innovation. Essential for this is better access to the fuel of algorithms, the data. Only those who have access to high-quality data can succeed in becoming serious competitors on the market.

Fourth: Algorithmic competency at all levels. Every citizen must be able to assess whether and how the decisions of an algorithm are relevant for him or her. Anyone who commissions, develops and uses software must consider its social consequences and ethical aspects. And we need a public sector that is strong in AI matters, one that regulates intelligently on the one hand and uses algorithms to foster the common good on the other hand. A state agency for algorithmic competencies would help to quickly build up the urgently needed abilities for this throughout the public administration.

The authors do not see Europe’s current backlog as an obstacle, but rather as an opportunity. “We don’t have to repeat the mistakes of others and can go our own European way, which should not see values and competitiveness as opposites. This European path gives the common good a higher priority than in the US and, unlike in China, preserves a high degree of individual freedom.”