This paper discusses and aims to explain the emergence of foresight activities in Swedish government authorities, including environmental scanning and analysis, futures studies and other activities tr

The societal impact of new technologies

In order to meet global challenges such as poverty and climate change, we depend on technological innovations. We have already become increasingly embedded in a technological ecosystem that have significant effects on our everyday lives. But even small mistakes in how new technologies are being introduced, can cause negative impacts on our societies. In this theme we try to understand this process.

in: Technology in Society. 63, 101396 Abstract:Artificial Intelligence (AI) in the form of Deep Learning (DL) technology has diffused in the consumer domain in a unique way as compared to previous gene, i.e., by being added to preexisting technologies that are already in use. We find that DL-algorithms for recommendations or ranking have been infused into all the 15 most popular mobile applications (apps) in the U.S. (as of May 2019). DL-infusion enables fast and vast diffusion. For example, when a DL-system was infused into YouTube, it almost immediately reached a third of the world's population. We argue that existing theories of innovation diffusion and adoption have limited relevance for DL-infusion, because it is a process that is driven by enterprises rather than individuals. We also discuss its social and ethical implications. First, consumers have a limited ability to detect and evaluate an infused technology. DL-infusion may thus help to explain why AI's presence in society has not been challenged by many. Second, the DL-providers are likely to face conflicts of interest, since consumer and supplier goals are not always aligned. Third, infusion is likely to be a particularly important diffusion process for DL-technologies as compared to other innovations, because they need large data sets to function well, which can be drawn from preexisting users. Related, it seems that larger technology companies comparatively benefit more from DL-infusion, because they already have many users. This suggests that the value drawn from DL is likely to follow a Matthew Effect of accumulated advantage online: many preexisting users provide a lot of behavioral data, which bring about better DL-driven features, which attract even more users, etc. Such a self-reinforcing process could limit the possibilities for new companies to compete. This way, the notion of DL-infusion may put light on the power shift that comes with the presence of AI in society.

Welcome to this seminar with David Grusky, Professor of Sociology at Stanford University.The seminar is jointly organized by the Institute for Analytical Sociology and the Institute for Futures Studies.D Thursday, October 6 13:00-15:00 (CET) At the Institute for Futures Studies (Holländargatan 13, Stockholm), or onlineIf qualitative work were to be rebuilt around open science principles of transparency and reproducibility, what types of institutional reforms are needed? It’s not enough to mimic open science movements within the quantitative field by focusing on problems of data archiving and reanalysis. The more fundamental problem is a legal-institutional one: The field has cut off the development of transparent, reproducible, and cumulative qualitative research by betting on a legal-institutional model in which qualitative scholars are incentivized to collect data by giving them ownership rights over them. This neoliberal model of privatized qualitative research has cut off the development of public-use data sets of the sort that have long been available for quantitative data. If a public-use form of qualitative research were supported, it would not only make qualitative research more open (i.e., transparent, reproducible, cumulative) but would also expand its reach by supporting new uses. The American Voices Project – the first nationally-representative open qualitative data set in the US – is a radical test of this hypothesis. It is currently being used to validate (or challenge!) some of the most famous findings coming out of conventional “closed” qualitative research, to serve as an “early warning system” to detect new crises and developments in the U.S., to build new approaches to taking on poverty, the racial wealth gap, and other inequities, and to monitor public opinion in ways far more revealing than conventional forced-choice surveys. The purpose of this talk is to discuss the promise – and pitfalls – of this new open-science form of qualitative research as well as opportunities to institutionalize it across the world. 

Should Scholars Own Data? David Grusky About the American Voices Project

If qualitative work were to be rebuilt around open science principles of transparency and reproducibility, what types of institutional reforms are needed? It’s not enough to mimic open science movemen