The ‘Soft’ Impacts of Emerging Technology

by Fabio Tollon

Getting a handle on the various ways that technology influences us is as important as it is difficult. The media is awash with claims of how this or that technology will either save us or doom us. And in some cases, it does seem as though we have a concrete grasp on the various costs and benefits that a technology provides. We know that CO2 emissions from large-scale animal agriculture are very damaging for the environment, notwithstanding the increases in food production we have seen over the years. However, such a ‘balanced’ perspective usually emerges after some time has passed and the technology has become ‘stable’, in the sense that its uses and effects are relatively well understood. We now understand, better than we did in the 1920s, for example, the disastrous effects of fossil fuels and CO2 emissions. We can see that the technology at some point provided a benefit, but that now the costs outweigh those benefits. For emerging technologies, however, such a ‘cost-benefit’ approach might not be possible in practice.

Take a simple example: imagine a private company is accused of polluting a river due to chemical runoff from a new machine they have installed (unfortunately this probably does not require much imagination and can be achieved by looking outside, depending on where you live). In order to determine whether the company is guilty or not we would investigate the effects of their activities. We could take water samples from the river and attempt to show that the chemicals used in the company’s manufacturing process are indeed present in the water. Further, we could make an argument where we show how there is a causal relationship between the presence of these chemicals and certain detrimental effects that might be observed in the area, such as loss of biodiversity, the pollution of drinking water, or an increase in diseases associated with the chemical in question.

In this example the issue is black and white: we have a guilty party who we can properly hold to account for their irresponsible use of a technology. However, part of the reason we can do this is because it is supposed that we expect the polluters to have known better, that is, there would be certain, foreseeable, consequences that would have followed from their activities. Let us call these kinds of effects of technology ‘hard’ effects. ‘Hard’ effects can be quantified, have a clear harm, and in addition causation can be established. These are not the kind of effects that I am interested in. It is in the ‘soft’ effects of technology where we encounter inherent ambiguity and contestability, and where getting a handle on what is really going on becomes quite a complicated, if not impossible task. ‘Soft’ impacts, by their very nature, are difficult to quantify, do not have clear and noncontroversial harms, and direct causation is difficult to trace.

For example, it has become common to attribute the seeming rise in political polarization to new forms of social media and their governing algorithms (Twitter, Facebook, etc.). This argument seems to make intuitive sense, as social media seems to be characterized by increasingly extreme views and perspectives, and decreases the costs associated with finding other like-minded individuals. But is this really the case?

Alberto Acerbi, for example, has recently argued that we have been seeing a rise in political polarization that extends to well before the introduction of social media, and that offline individuals might in fact be more polarized than those that spend their time online. He references a research study that measured levels of polarization from 1996, and where the researchers found that age was the biggest factor in predicting an increase in polarization. This actually fits well with data from the election of Donald Trump in the US and the Brexit campaign in the UK: older people tended to be more pro-Trump and pro-Brexit, and these are people who we expect would use social media the least. Therefore, we have higher levels of polarization in communities that spend less time on social media platforms. Of course, this does not settle the matter: social media analysis is a new and moving target for research, and the latest data suggests that the elderly might be using social media just as much as younger people, but perhaps for different purposes.

The point of this example is that the effects of the technology do not conform to the three conditions that underlie our ability to discern ‘hard’ effects. The causal picture is messy, and it is sometimes unclear whether the technology is the cause of some effect, or whether it is merely a contingent part of a decades long trend.

This is in part due to the fact that social media might be thought of as a kind of emerging technology: These are nascent technologies whose meaning, use, and impact is still unstable, where we do not yet know what their future consequences might be. As noted above, such technologies cannot be conceptualized in a ‘cost-benefit’ framework (given that we cannot specify these in sufficient detail) and so a ‘promises and perils’ framework might be more productive. Examples of other emerging technologies include (but are of course not limited to) humanoid robots, autonomous military drones, synthetic biology, and virtual reality.

For these kinds of technologies, we are better off framing their potential impacts in terms of promises or perils: The predictions of what a technology may afford are grounded in what they promise us (better health, more ecologically friendly living, a world without work) or their perils (reduced autonomy, AI overlords, threats to democracy). Such an approach is justified because of the nature of promises and perils: they admit of ambiguity and are not an evaluation of what is but of what could be, and so they better capture the uncertainty that comes with imagining the future than a costs and benefits analysis, which might be better suited to evaluating more ‘stable’ technologies.

As emerging technologies are by their nature in a nascent stage of development, detractors and defenders have to speculate (and often embellish) the effects that these technologies might have. Because the meanings of these technologies have not yet ‘stabilized’ and their place in society is not yet fixed, it is often difficult to disaggregate false promises from genuine ones. Decisions about who will fund the development of the technology, who will be able to make use of it, and the cultural context in which it will be embedded all play major roles in whether the technology will be a ‘success’ or not.

To go back to the example of polarization above, what we observe is an inherent ambiguity and contestability, and, perhaps most importantly, part of the effects we are attempting to measure are produced by the agents themselves. Thus, unlike in the case of a factory polluting a river, when it comes to soft impacts users more intimately co-produce the harms (or benefits) that are the object of our study. Such soft impacts, therefore, require an account of how we expect humans to behave when confronted with the technology, and so it is not enough to merely give an explanation as to how the technology might influence the environment. We require further specification about how an intervention might impact the habits, autonomy, and self-understanding that individual users have. This is not an easy task, as difficult questions include whether the ‘target’ of such studies should be agents’ mental states, their ability to use moral reason, or whether they are more virtuous as a result of the technology.

This means that when attempting to evaluate the potential effects of emerging technology we should be especially sensitive to the soft impacts that they may have. Attention to these soft impacts makes salient how a cost-benefit approach cannot fully capture the nuanced effects that emerging technologies can have on human social relations, culture, and politics.