The Future of Platform Power
Making Middleware Work
Bibliographic Data
| ID | 6287690 |
|---|---|
| Authors | Daphne Keller (corresponding author) |
| Year | 2021 |
| Volume | 32 |
| Issue | 3 |
| Pages | 168-172 |
| Publication date | 2021-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of democracy (JOURNAL) |
| Journal identifiers | ISSN: 1045-5736 • E-ISSN: 1086-3214 |
| Publisher | Project MUSE (PUBLISHER • US) |
| DOI | 10.1353/jod.2021.0043 |
| OpenAlex | W3195048599 |
| Language | EN |
| Citations received | 11 |
Unlike many other proposals to curtail platform power, middleware does not violate the First Amendment of the U.S. Constitution. In the United States, that makes middleware a path forward in a neighborhood full of dead ends. Before we can execute on the middleware vision, however, at least four problems must be solved. First, how technologically feasible is it for competitors to remotely process massive quantities of platform data? Second, how is everyone going to get paid? Third, who will bear the enormous costs of content curation? And fourth, how does middleware address privacy issues regarding users' friends' data? We have to solve these problems to make middleware work—but this is also what makes the concept so promising
Business · Competitor analysis · Computer security · Constitution · Distributed computing · Message oriented middleware · Middleware (distributed applications · Operating system · Path (computing · Political science · Power (physics · Process (computing · Work (physics · World Wide Web · Computer Science · Engineering · Law · Peer-to-Peer Network Technologies · Privacy, Security, and Data Protection · Scientific Computing and Data Management · Software
Recommender Systems
Decentralised content moderation
Recommended Selves
Brazilian Fake News Bill
The moral case for using language model agents for recommendation
The Networked Leviathan
AI and Global Governance
Distrust of Artificial Intelligence
Middleware and the Illusory Promise of End-User Control
Bridging the News and Social Media Divide
Attention, moral skill, and algorithmic recommendation
| Citations per year | 2.75 |
|---|---|
| Citation span | 2022 - 2026 (5) |
| Highly cited | No |