The rise of Truth to Power (T2P) systems
I recently read the excellent book by Zeynep Tufekci, Twitter and Tear Gas; the Power and Fragility of Networked Protest. Although the main theme, the impact of new technologies on protesting against dictatorial regimes, is very interesting and worth discussing, I want to highlight another concept she discussed; surveillance as a business model.
One reason why the field of data science is such an incredible hype is because of the implicit promise that large corporates can understand much more of consumers and their customers. Monitoring people’s every move, corporations can better predict what offer is more likely to succeed. This is the implicit promise of significantly reducing cost of acquisition and creating additional revenue streams through new offerings.
This is not an unproblematic promise.*
With almost all industries interested in using data science for commercial purposes, money is pouring in research that deals with predicting and influencing consumer behavior. In my opinion this has consequences for the current so called fair markets.
* Full disclosure: I have worked several years promoting the use of persuasion profiling, I do take full responsibility for being part of the problem.
“Data science, as an academic field, shouldn’t only cater to corporate agendas.”
Does Data Science Lead to Asymmetric Information?
You could make the case that if only sellers are using advanced algorithms to optimize how they approach buyers this can be seen as information asymmetry, which leads to failing markets.
The rise of data science and the use of algorithms to exploit personal data has added another level to surveillance as a business model. In my opinion it has had an negative impact on markets. And that’s why I wanted to share my concerns.
Data science, as an academic field, shouldn’t only cater to corporate agendas. It should instead try to fix the current information asymmetry. Next to all kinds of important ethical discussions and regulatory issues, data scientists can actually help explain what algorithms are being used and what their effects are.
And even better; data scientists can use their skills to help build systems to restore the information asymmetry. These T2P systems (or at least how I would like to call them) add to another promise that was hyped a long time ago; that the internet would offer more transparency for citizens and consumers.
I am curious to know what your ideas are, and let me know if you are an academic that wants to explore this theme more thoroughly.