(THIS ARTICLE IS MACHINE TRANSLATED by Google from Norwegian)
Around 1200 before our time, the Shang Dynasty in China developed a factory system to make thousands of large bronze containers, for everyday use and for ritual ceremonies. In this early example of mass production, the casting required detailed planning and coordination of large groups of workers, with each group performing a separate task in the correct order.
A similarly complex process lay behind the famous army of terracotta warriors as Qin Shi Huang, China's first emperor, unveiled 1000 years later. According to the Asian Art Museum in San Francisco, "the statues were created using a mounting system that paved the way for progress in mass production and trade." Some scientists have wondered whether these early forms of work-guiding technologies played an important role in shaping Chinese society. Among other things, they seem to have predisposed people to accept bureaucratic structures, a social philosophy with an emphasis on hierarchy and a belief that there is only one right way to do things.
algorithms
When industrial factories were introduced in England in the 19. century, even staunch critics of capitalism, such as Friedrich Engels, realized that mass production required a centralized authority, regardless of whether the economic system was capitalist or socialist. In it 20. over the century, theorists like Langdon Winner expanded this thinking to include other technologies. He believed that the atomic bomb, for example, should be considered a "deep political object" because the "deadly properties of the bomb require it to be controlled by a centralized, strictly hierarchical command line".
Machine learning algorithms are contrary to the desire for privacy.
Today we can take this thinking even further: Think about the algorithms that enable machines to learn – it is the most important technology in use today, for a variety of purposes. By using real-world examples to mimic human cognitive abilities, these algorithms are already everywhere in the world of work. However, in order to fully benefit from this technology, organizations must redefine human tasks into predictable tasks. It fits better with the strength of these algorithms.
A key feature of machine learning algorithms is that their performance gets better with more data. As a result, the use of these algorithms creates a pressure towards treating information about people as recordable, available data. Like the mass production system, they are "inherently political" because their core functionality requires certain social practices and discourages others. In particular, machine learning algorithms are in direct contradiction to individuals' desire for privacy.
Rating Community
A system based on allowing information about individual community members to be publicly available may seem to be polite to communitarianists such as sociologist Amitai Etzioni, who believes that privacy restrictions are a means of strengthening social norms. But unlike communitarianists, algorithms are indifferent to social norms. All they care about is making better predictions by transforming more and more areas of human life into data sets that can be utilized. Moreover, while the power of a technological imperative turns Western individualists into casual communitarianists, it also makes them more likely to accept a meritocratic culture based on algorithmic judgments. Whether at work, at school, or even in dating apps, we have already become accustomed to being rated by impersonal tools, which then assigns us positions in a hierarchy.
Like the mass production system, the algorithms are "inherently political".
Algorithmic assessment is nothing new at all. A generation ago, for example, Professor Oscar H. Gandy warned that we were about to have a scoring and ranking community, demanding more accountability and opportunities for redress for technology-related errors. However, unlike modern machine learning algorithms, older assessment tools were somewhat understood. They made decisions on the basis of relevant normative and empirical factors. For example, it was no secret that accumulating large credit card debt could damage your credit rating.
In contrast, the new machine learning technology runs deep in large amounts of data to find correlations that are predictable but poorly understood. In the workplace, algorithms can check employees' conversations, lunch habits and how much time they spend at the computer, on the phone or at meetings. With this data, the algorithm develops sophisticated productivity models that far exceed our reasoning-based intuition. In an algorithmic meritocracy, what models demand will be the new standard for what is considered excellent.
communitarianism
Still: Technology is not destiny. We shape it before it shapes us. Business executives and other decision makers can develop and apply the technology they want, based on their institutional needs. It is in our power to protect sensitive parts of privacy, protect people from malicious use of data, and ensure that algorithms balance accurate predictions against other values such as fairness, accuracy and transparency.
But if we follow the natural direction of algorithmic logic, a more meritocratic and communitarian society will be inevitable. And this smooth transformation will have far-reaching implications for our democratic institutions and political structures. As China experts Daniel A. Bell and Zhang Weiwei have pointed out, the most important political alternative to Western liberal democratic traditions will be the communitarian institutions that continue to develop in China.
The communitarian institutions become political alternatives.
In China, collective decisions are not legitimized by the explicit consent of citizens, and people have consistently fewer enforced rights in relation to the government – especially when it comes to surveillance. The role of a common Chinese in politics is largely limited to participating in local elections. The country's leaders are selected through a meritocratic process and see themselves as guarantors of the welfare of the people.
Liberal democracies are unlikely to change completely into such a political system. But if current trends in business and consumer culture continue, we may soon have more in common with China's meritocratic and communitarian traditions than with our own history of individualism and liberal democracy. If we want to change course, we must put our own political imperatives ahead of the imperatives that apply to our technology.
Translated into Norwegian by Lasse Takle.
Project Syndicate, 2016.
www.project-syndicate.org
