Tech and Politics

Some of these challenges are aimed at expanding social representation and strengthening democratic participation. We find examples of this in the use of digital media by social movements, such as Black Lives Matter in the US. Other challenges aim to restrict representation and participation, as the example of the use of digital media by various right-wing populist movements and parties shows. Digital media can therefore contribute to strengthening societies and democracies as well as to weakening them.  In this chapter, we will discuss this role of digital media in politics and society in detail.

Chapters:

00:00 - Introduction
00:19 - Digital media and the challenge to institutions
01:23 - What are institutions and what do they do?
13:30 - Digital media as staging area for challenges to institutions
21:28 - Conclusion

Script:

http://digitalmedia.andreasjungherr.de/challenge.html

Another field heavily impacted by the uses of data and algorithms is journalism. Journalists and editors see themselves facing new opportunities for the measurement of the success and reach of their pieces with audiences by counting clicks on their sites and interaction metrics provided to them by platform companies used by them to steer audiences to news pieces. Algorithms also play a role in the way people are steered toward journalistic content. By the increasing dependency on digital platforms - like Facebook, Instagram, or Google - news media are subject to the algorithms these platforms use to prioritize or de-prioritize journalistic pieces. Accordingly, figuring out the mechanisms behind these algorithms and adapting one's pieces accordingly may come to matter. Finally, and potentially most disruptively, algorithms are increasingly used to produce journalistic pieces automatically, raising fears about the future of journalism as an occupation. Journalism offers a promising window into the field-specific impact and transformations driven by the increasing availability and uses of data and algorithms.

Chapters:

00:00 - Introduction
00:18 - Data and algorithms in journalism
08:36 - The use and perception of metrics in newsrooms
18:27 - Effects of metrics on audiences
22:40 - Uses and effects of data and algorithms in politics and society
25:55 - Conclusion

Script to episode with references and further readings:

http://digitalmedia.andreasjungherr.de/docs/data-algos/data-algos_section4.html

One area in politics where we find pervasive uses of data and algorithms are political campaigns. Campaigners, candidates, and campaign organizations use data to see the electorate, populations of interest, and their volunteers. They use metrics to monitor their progress and measure their success. They sometimes even use algorithms in order to identify who to talk to and how to best approach them. While data and quantification play an increasing role in international campaigns, their use and contribution in campaigns in the USA is most pronounced and merits specific attention if we want to understand the principles of their use and effects.

Chapters:

00:00 - Introduction
00:20 - The uses and effects of data and algorithms in political campaigns
09:16 - How to keep volunteers engaged?
14:31 - How can we learn about the use of data an algorithms in campaigns?
20:39 - Conclusion

Script to episode with references and further readings:

http://digitalmedia.andreasjungherr.de/docs/data-algos/data-algos_section3.html

A computer algorithm provides a computationally executable series of steps with the goal of solving a given task or problem. Algorithms are shaping people's option spaces in various areas of social life. This has given rise to broad concerns that are part of public debate but also of academic research. Concerns focus on the opaqueness of the uses of algorithms, their inner workings, and their effects; the fairness of their outcomes; and fears of unintended consequences once algorithms are rolled out in scale. This makes algorithms, their uses, mechanisms, and effects a prominent topic for social scientists engaged in understanding the use of digital technology in society.

Chapters:

00:00 - Introduction
00:21 - Algorithms
10:57 - Concerns
11:48 - Opaqueness
15:34 - Fairness
19:38 - Unintended consequences
24:26 - Conclusion

Script to episode with references and further readings:

http://digitalmedia.andreasjungherr.de/docs/data-algos/data-algos_section2.html

Data collected with and on digital devices make new phenomena, objects, and behaviors visible to those with access to that data. Digital technology has extended the amount and depth of data on human behavior and social systems. This has been seen by some as a measurement revolution for the social sciences and as providing many new avenues to knowledge as well as new business opportunities. Still, these data riches have to be translated into meaningful measures of phenomena of interest and society. Without meaningful interpretation these new data remain noise machines, potentially even hiding the signal one is looking for in order to gain understanding or improvement.

Chapters:

00:00 - Introduction
00:21 - Data and algorithms in society
04:17 - Data and measurement
08:37 - Big data
14:10 - Data and control
19:46 - Metrics
24:53 - Conclusion

Script to episode with references and further readings:

http://digitalmedia.andreasjungherr.de/docs/data-algos/data-algos_intro.html

Can we still meaningfully speak of equality of rights and representation among people, if AI-based systems discriminate against minorities or the underprivileged? How do the known biases inherent in AI systems translate to democratic politics?  Even more fundamentally, what does equality even mean when AI contributes to massive power imbalances between the companies running and developing AI and everyone else, including the government?  To some commentators AI might also provide an opportunity for autocracies to get a leg up on democracies in the detection and solution to societal and political challenges. Traditionally, democracies were seen to be better at soliciting information about the state of their societies or the effects of interventions compared to autocracies. This information benefit was seen as one reason for democracies being able to outperform autocracies. AI might offset this benefit and allow autocracies to pass democracies by.
Chapters:

00:00 - Introduction
00:29 - Artificial intelligence and equality
08:31 - Artificial intelligence and power shifts between societies
17:53 - Artificial intelligence and democracy: The road ahead
23:38 - Conclusion

Script to episode with references and further readings:

http://digitalmedia.andreasjungherr.de/docs/ai/ai_section3.html

AI raises questions with regard to the integrity of elections as an adjudication process for the conflict between political factions. In the age of the perceived predictability of people's political attitudes and behavior, can there be free and fair elections in which each faction conceivably might rise to power?

Is it still plausible that people are able to make political and societal decisions? For one, are information environments shaped by artificial intelligence based on the preferences of people still adequate to the task of creating informed publics able to form political opinions according to their interests? Going further, are scenarios provided by AI to experts about the future of complex issues not better decision makers than the people following their passions and their interests? How does democratic decision making hold up against this new environment.

Chapters:

00:00 - Introduction
04:46 - Artificial intelligence and elections
14:09 - Artificial intelligence and people's informational autonomy
15:45 - Free expression
19:02 - Access to information
21:28 - Manipulation
23:30 - Expert rule
26:49 - Conclusion

Script to episode with references and further readings:

http://digitalmedia.andreasjungherr.de/docs/ai/ai_section3.html

Going forward, we will be focusing on the uses and effects of narrow AI on politics and democracy. To do so, we first need to figure out what it is exactly that AI changes in these areas. In other words, what is AI good at? What becomes cheaper or easier to do? And, for which types of problems does this work?

Chapters:

00:00 - Introduction
00:56 - Predictions
08:08 - Machine readable
11:30 - Abundant outcomes
15:45 - Stability over time
19:58 - Reinforcing structural inequalities
23:55 - Conclusion

Script to episode with references and further readings:

http://digitalmedia.andreasjungherr.de/docs/ai/ai_section2.html

AI is a largely invisible feature of our daily lives with clear consequences for us as citizens and consumers. Examining the workings and real-world consequences of actually existing artificial intelligence is important. To do so, we first start with a discussion of what artificial intellence is and is not.

Chapters:

00:00 - Introduction
00:21 - Artificial intelligence and democracy
04:52 - What is artificial intelligence?
12:46 - Narrow artificial intelligence versus artificial general intelligence
24:51 - Conclusion

Script to episode with references and further readings:

http://digitalmedia.andreasjungherr.de/docs/ai/ai_intro.html

Once people interact with digital devices (such as smart phones and smart devices) and services (such as Facebook or Twitter), their digitally mediated interactions leave traces on devices and services. Some of those are discarded, some are stored. Some are available only to the device maker or service provider, some are available to researchers. This last category of digital trace data, those that are stored and available to researchers, has spawned a lot of research activity and enthusiasm over a new measurement revolution in the social sciences. But somewhat more than ten years into this "revolution", the limits of digital trace data for social science research are becoming just as clear as their promises. Before we look at studies using digital trace data, it is therefore necessary that we look a little more closely at what they are, what characteristics they share, and how this impacts scientific work with them.

Chapters:

00:00 - Introduction
00:18 - Digital trace data
12:11 - Digital trace data in political science
15:42 - Making sense of online censorship decisions
21:24 - It's attention, not support!
30:03 - Learning about the world with computational social science
33:49 - Conclusion

Script to episode with references and further readings:

http://digitalmedia.andreasjungherr.de/css.html#digital-trace-data-typical-approaches-in-computational-social-science-2