Category Archives: related projects

Virtual Competition – The Promise and Perils of the Algorithm-Driven Economy

Shoppers with Internet access and a bargain-hunting impulse can find a universe of products at their fingertips. In this thought-provoking exposé, Ariel Ezrachi and Maurice Stuckeinvite us to take a harder look at today’s app-assisted paradise of digital shopping. While consumers reap many benefits from online purchasing, the sophisticated algorithms and data-crunching that make browsing so convenient are also changing the nature of market competition, and not always for the better.

Computers colluding is one danger. Although long-standing laws prevent companies from fixing prices, data-driven algorithms can now quickly monitor competitors’ prices and adjust their own prices accordingly. So what is seemingly beneficial—increased price transparency—ironically can end up harming consumers. A second danger is behavioral discrimination. Here, companies track and profile consumers to get them to buy goods at the highest price they are willing to pay. The rise of super-platforms and their “frenemy” relationship with independent app developers raises a third danger. By controlling key platforms (such as the operating system of smartphones), data-driven monopolies dictate the flow of personal data and determine who gets to exploit potential buyers.

Virtual Competition raises timely questions. To what extent does the “invisible hand” still hold sway? In markets continually manipulated by bots and algorithms, is competitive pricing an illusion? Can our current laws protect consumers? The changing market reality is already shifting power into the hands of the few. Ezrachi and Stucke explore the resulting risks to competition, our democratic ideals, and our economic and overall well-being.

.

PolitEcho

What is PolitEcho?PolitEcho shows you the political biases of your Facebook friends and news feed. The app assigns each of your friends a score based on our prediction of their political leanings then displays a graph of your friend list. Then it calculates the political bias in the content of your news feed and compares it with the bias of your friends list to highlight possible differences between the two.

Source: PolitEcho

Algorithmic selection on the Internet – Media Change & Innovation – IPMZ – University of Zurich

Publications

  • Just, Natascha / Latzer, Michael (2016): Governance by Algorithms: Reality Construction by Algorithmic Selection on the Internet. In: Media, Culture & Society [accepted manuscript, forthcoming online before print]. [pdf]
  • Dörr, Konstantin / Hollnbuchner, Katharina (2016): Ethical Challenges of Algorithmic Journalism. In: Digital Journalism [accepted manuscript; forthcoming online before print]. [pdf]
  • Latzer, Michael / Hollnbuchner, Katharina / Just, Natascha / Saurwein, Florian (2016): The economics of algorithmic selection on the Internet. In: Bauer, J. and Latzer, M. (Eds), Handbook on the Economics of the Internet. Cheltenham, Northampton: Edward Elgar, 395-425. [pdf]
  • Saurwein, Florian / Just, Natascha / Latzer, Michael (2015): Governance of algorithms: options and limitations. In: info, Vol. 17 (6), 35-49. [pdf]
  • Dörr, Konstantin (2015): Mapping the field of Algorithmic Journalism. In: Digital Journalism [online before print]. [pdf]

 

 

Artificial Moderation: A Reading List – The Coral Project

A lot of research has been carried out around using data analysis to identify different aspects of online behavior. Before I detail some of it below, I should add a note of caution: all analytics are only as good as how they are utilized in decision making by end users.The complex interplay between computational tools and human actors in sociotechnical systems (such as online communities) means that great technology and analytics can still fall flat if the community policies aren’t “right”. Engagement e

Source: Artificial Moderation: A Reading List – The Coral Project

CrowdRec

Fusion of Active Information for Next Generation Recommender Systems

The CrowdRec project pursues three objectives:

  • Stream Recommendation: real-time combination of information from collection, context, user interaction and user community to generate social smartfeeds for large-scale social networks;
  • Crowd Engagement: creating symbiosis between users and content that activates users to contribute;
  • Deployment and Validation: developing and testing for release of reference implementations and large-scale user trials.

For the reference framework containing implementations of algorithms that have been developed within the CrowdRec project,

Source: CrowdRec