The goal of the following paper is to develop a defence for the regulation of algorithmic recommender systems. After demonstrating the harmful effects that algorithmic recommender systems can have, I will build an argument for regulating these systems in order to protect individuals and minority groups from discrimination in social media. In chapter 4, I will address two arguments that act as a foundation against regulating such systems and show that these arguments are not able to present convincing points. In chapter 5, I will bring my argumentation with another pro- regulation argument to an end and finish my paper with a conclusive summary.
Hate, misinformation and political manipulation have grown to become a problem of social scale in the online media of our time. This can have a negative impact outside the digital world. Despite the fact that it can only be the user himself who initially introduces hate and agitation into a discussion, the large operators of social media platforms, namely Facebook, Twitter or Google, are also to be held responsible for the fact that hate can spread on their platforms as quickly as it was not possible in classic media before. Their business model aims to keep users on the platform for a long period of time in order to sell their advertising space as profitably as possible. For this purpose, most of these platform companies use algorithmic recommender systems to push content or products (like YouTube's "Next Videos" or Facebook's "Recommended Groups") which are considered relevant for the user.
Although the platforms, if we follow their argumentation, want to make the user experience with these systems as positive as possible, the practice of providing the user with information that reflects his or her interests leads to the fact that if the user deals with already critical topics these also appear repeatedly in their timeline. This sometimes leads to users being encouraged in their opinion and creates network effects through which users encounter other users with the same opinion or inform themselves about similar topics. In addition to that, personalised advertising whose business model is also based on the same critical algorithmic recommender systems, can cause particular harm in a political context.
Table of Contents
1. Introduction
2. The Harm of Algorithms
2.1. The Problem of Filter Bubbles and Echo Chambers
2.2. Negative Impacts of Algorithmic Suggestions
3. The Harm Argument
4. Arguments Against Regulation
4.1. The Sphere of Economic Freedom
4.2. The Argument of Intellectual Property
5. State Risks – Further Arguments for Restrictions
6. Conclusions
Objectives and Topics
The primary objective of this paper is to construct a theoretical defense for the regulation of algorithmic recommender systems. By analyzing the harmful social and political effects of these technologies, the work seeks to justify state intervention as a necessary measure to protect individuals, minority groups, and the stability of democratic institutions.
- Analysis of algorithmic mechanisms like filter bubbles and echo chambers.
- Examination of the "Harm Argument" in the context of online radicalization.
- Critique of counterarguments based on economic freedom and intellectual property rights.
- Investigation of state risks and political manipulation via fake news and bot networks.
- Justification of regulatory frameworks for digital media platforms.
Excerpt from the Book
2.1. The Problem of Filter Bubbles and Echo Chambers
The algorithms of social media platforms are designed to keep the attention of users on their platform as long as possible. Maximizing the time a user spends on the platform promises immensely higher advertising revenues and thus represents a core factor for the success of the platforms business model. In order to achieve this goal, information collected in the background, such as the user's last search queries or the posts he or she clicked on last, is used to present the user with additional content that the algorithm considers particularly relevant.
Although platform operators see these practices as user-centric and argue that this is an attempt to tailor the service to the individual user, there are some major risks associated with this approach. One of these risks can be the creation of a so-called filter bubble. A filter bubble describes the phenomenon that the algorithms mainly suggest topics that users have already indicated as topics of interest (Zweig et. al 2017: p. 319). As a result, the user has to deal with the same content repeatedly. Let's assume that a user is a fan of a sports club, clicks mostly sports related posts and even prefers the sports reports in the online version of his daily newspaper. According to what is known about the approach of the algorithmic recommender systems, this user would mainly get sports content pushed into his timeline, while other traditional media formats such as newspapers and television would also bring him into contact with other news (Zweig et. al 2017: p. 323). In terms of political topics, there is a danger that a user who deals in detail with an extreme point of view or with theories of conspiracy would, in the course of the algorithmic adjustment to his search behavior, only be shown content that further confirms these extreme views.
Chapter Summaries
1. Introduction: This chapter outlines the rising issue of online misinformation and manipulation and defines the paper's goal of defending the regulation of recommender systems.
2. The Harm of Algorithms: This section details how technical mechanisms like filter bubbles and echo chambers prioritize engagement at the expense of balanced information intake.
2.1. The Problem of Filter Bubbles and Echo Chambers: This sub-chapter explains how user-specific data tracking creates restrictive content environments that reinforce existing viewpoints.
2.2. Negative Impacts of Algorithmic Suggestions: This sub-chapter highlights how algorithmic recommendations can drive users toward extremist groups and discriminatory content.
3. The Harm Argument: This chapter formalizes an argument for regulation based on the moral obligation of the state to prevent harm caused by these algorithms.
4. Arguments Against Regulation: This chapter addresses and counters two major liberal arguments against government intervention: economic freedom and intellectual property rights.
4.1. The Sphere of Economic Freedom: This sub-chapter examines the claim that regulating algorithms infringes upon a company's economic freedom to operate.
4.2. The Argument of Intellectual Property: This sub-chapter discusses the assertion that regulation threatens the proprietary innovation and intellectual property of technology firms.
5. State Risks – Further Arguments for Restrictions: This chapter explores how unregulated algorithms pose broader risks to democracy, citing examples of election manipulation and political instability.
6. Conclusions: The final chapter summarizes the findings, reiterating that the arguments against regulation fail to outweigh the necessity of protecting citizens and democratic order.
Keywords
Algorithms, Recommender Systems, Filter Bubbles, Echo Chambers, Regulation, Political Philosophy, Public Policy, Social Media, Radicalization, Fake News, Economic Freedom, Intellectual Property, Democracy, Manipulation, Online Safety
Frequently Asked Questions
What is the core subject of this publication?
The work examines the political and social dangers posed by algorithmic recommender systems on social media and provides a normative justification for their state regulation.
What are the central thematic fields?
The focus lies on the intersection of digital technology, democratic theory, platform business models, and the ethical responsibility of the state regarding content regulation.
What is the primary research question?
The paper asks whether the documented harmful effects of algorithmic recommender systems provide sufficient moral and empirical grounds to justify state intervention despite counterarguments regarding market freedoms.
Which scientific methodology is applied?
The author uses an argumentative analysis, which includes building logical arguments (claims and conclusions), evaluating counter-positions, and applying philosophical frameworks to contemporary technological problems.
What topics are discussed in the main body?
The main body covers the mechanics of digital radicalization, the "harm argument," critiques of economic and property-based arguments against regulation, and the broader risks to national democratic stability.
Which keywords best describe the paper?
Key terms include Algorithms, Recommender Systems, Filter Bubbles, Regulation, Political Philosophy, and Democracy.
How do "filter bubbles" specifically harm users according to the text?
Filter bubbles isolate users from diverse viewpoints by repeatedly suggesting content that confirms their existing biases, making it difficult for them to form independent opinions and increasing the risk of radicalization.
What counterarguments does the author address?
The author addresses two main liberal arguments: that regulation violates the economic freedom of companies and that it infringes upon their intellectual property rights regarding their proprietary algorithms.
How does the paper link algorithms to the U.S. presidential election?
The paper cites the 2016 election to illustrate how unregulated algorithmic systems and bot networks can facilitate the spread of fake news and political manipulation on a massive scale.
- Citation du texte
- Maximilian Gerring (Auteur), 2022, The Harm of Algorithms. An Argumentative Analysis to Justify the Regulation of Algorithmic Recommender Systems, Munich, GRIN Verlag, https://www.grin.com/document/1214950
