Review: Technocratic Bias Risks Eroding the Legitimacy and Efficacy of the Data Revolution

Two recent articles included in the inaugural post of our “Links We Like” series have focused on what may be missing in the current specialized discourse and thinking about the data revolution’s implications and requirements.

First, Evgeny Morozov’s piece, tellingly titled “The rise of data and the death of politics”, provides an in-depth assessment of the far-reaching democratic implications of the possible advent of an “algorithmic regulation”. His starting point is how technology in general—and in particular the advent of the Internet of Things—is fueling a new approach to governance in the US. As Morozov describes, smart sensors and meters ubiquitously found in everyday appliances and devices create real-time data ripe for automated analysis of patterns and trends that can be used to give dietary advice or alert the National Security Agency of suspicious activity. This way of monitoring our actions to automatically implement policy is what Morozov calls “algorithmic regulation”.

But in Morozov’s view, data analysis should not replace other means of designing and implementing policy. “By assuming that the utopian world of infinite feedback loops is so efficient that it transcends politics,” he says, “the proponents of algorithmic regulation fall into the same trap as the technocrats of the past.” His argument is that while such methods can appear apolitical, they pose, in fact, a real threat to democracy: by entrusting the task of monitoring and directing human behavior to algorithms, we would deprive individuals of traditional policy-making means and systems. Thus, such approaches are not apolitical; rather they leave the political decisions to those who design the algorithms. Typically these decisions also aim to deal with symptoms (which can be tracked by sensors and devices) rather than causes (which are often much harder to trace and go beyond the reach of algorithms and datasets).

Morozov’s point is best illustrated by the case of law enforcement tactics. Algorithmic regulation can help identify whether people are paying their taxes in accordance with the law. But this information becomes irrelevant if the tax code contains loopholes that allow certain people to pay less than their fair share of taxes. This gets at the crux of the issue: the question of what constitutes a ‘fair share’ of taxes is fundamentally a political one, and not one that can be resolved by algorithms. To answer it, Morozov argues, we must stick to traditional politics and avoid adopting the technocratic bias that currently dictates our use of data. In Morozov’s words, “algorithmic regulation is perfect for enforcing the austerity agenda while leaving those responsible for the fiscal crisis off the hook.”

In a post on the ICTs for Development blog titled “The Data Revolution Will Fail Without A Praxis Revolution”, Richard Heeks makes a similar argument regarding the use of Big Data in development. Just as algorithmic regulation should not replace political debates about fiscal issues, Heeks stresses that Big Data is no panacea for development: what’s missing in this case, he says, is a “praxis revolution”. Praxis refers to the process through which policy decisions are implemented in the form of actions, and Heeks suggests that data-revolution-for-development proponents have put too much focus on turning data into information, and not enough focus on using that information to decide which actions will lead to desired results.

Heeks argues that the resources spent on data collection and analysis might better be directed towards improving praxis: by this, he means rethinking the way we design data-revolution-for-development projects to focus more on decision-making and concrete action. Like Morozov, Heeks warns against relying on Big Data at the implementation level of the policy-making chain, stressing that the technocratic approach “assumes digital decisions and actions are some apolitical and rational optimum”, “denies the importance of politics and thus neuters political debate”, and “diverts attention from the causes of society’s ills to their effects with the attitude: ‘there’s an app for that’.”

Morozov and Heeks bring up important issues that are too often left aside in discussions about the potential and implications of the data revolution—including the risks posed by a technocratic-technological overreliance on and overconfidence in the power and soundness of data-driven models. There needs to be a greater, richer, public debate to ensure that better data and solid data analytics reinforce, but do not replace, democratic processes.

Note: Haishan Fu, Director of the Data Development Group at the World Bank, and Emmanuel Letouzé, Director of Data-Pop Alliance, will discuss related considerations in a forthcoming blogpost.


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