Why Do We Always See New Technology as a Threat?

Once again, we are hearing calls for regulation of a new technology; in this case, large language models and generative algorithms, a response that is rooted in a very basic fear of change.

Draft European legislation and Italy’s decision to ban access to ChatGPT on privacy grounds, are matched in the United States for a public consultation to shape possible regulation of such algorithms; now Beijing , in its usual “command-and-control” style, is forcing algorithm developers to submit security audits certifying that the content generated by their tools is correct, does not use copyrighted materials, is not discriminatory and does not pose a security threat.

The main problem with this kind of regulation is, firstly, that it is usually carried out by politicians who little idea of what they are regulating, and who are driven by models of social alarm based on exactly the same lack of knowledge. In the case of LLMs, we are talking about a discipline, machine learning, which has been progressing for decades at the pace of the availability of data processing technology, and which has simply, on this occasion, been able to multiply its scale to work with language models based on billions of parameters. The result is that everyone who has worked on machine learning so far is amazed at the impact of dumb algorithms that have no idea what they are saying, but to which many attribute the clairvoyance of the Oracle of Delphi.

While underestimating this technology is as misguided as believing that they are intelligent or conscious, we are talking about algorithms that have managed to handle the complexity of human language, and that will therefore have a huge impact on all tasks and occupations that use language either as input (software development, creative tasks, article processing, etc.) or as output (creation of texts of all kinds, conversations, explanations, etc.) That is a lot of tasks, and we will probably not see so much people replaced by algorithms, as one of people who do not know how to handle algorithms by people who do. In addition, we will see perverse uses based on the manipulation of the training process of algorithms, at least until we start to use open source methodologies in their development.

The other important thing about regulation is that it tends to focus on the negative, on our fears. We have an inveterate tendency to consider all change as a threat, which means that regulation tends to focus on avoiding them at any cost. We usually interpret regulation as a control, equivalent to a users’ manual.

Italy’s approach, for example, is based on the assumption that it is better prevent its population from using ChatGPT so as to protect their privacy, rather than thinking about the possible damage caused by delays in the use of algorithms, which could make Italian workers and companies less competitive with respect to other countries where a learning process has taken place at a reasonable pace. Harming the competitiveness of your workers and your companies for fear of a possible and so far unproven abuse of their privacy is, in the times we live in, irresponsible and an abuse of power. At root, it is regulation by fear. If those who regulate are incapable of understanding the potential of a technology and do so on the basis of atavistic existential fears, what hope is there of obtaining a reasonable legal framework?

When we talk about technological regulation, we have to start from a fundamental basis: the regulator has to understand the inevitability of technological progress and, therefore, be proactive, not reactive, even if the wider population is worried about it. The regulator’s job is to take a long-term view, counterposing the fears of the public. While the general public may think that we can control technology by banning its use, for a government to do so is inexcusable.

LLMs and generative algorithms will once again highlight our insistence on regulating what is new, disruptive or that sparks change. Some countries will emerge from this process able to combine reasonable protection their populations with a proactive approach so they don’t remain anchored in the past and lose competitiveness simply because they are afraid of change. For the regulator, a challenge; for innovators, the same old story.

(En español, aquí)

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This post was previously published on MEDIUM.COM.

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