FATe of Bots: Ethical Considerations of Social Bot Detection

TL;DR

This paper explores the ethical challenges in social media bot detection, focusing on fairness, accountability, and transparency, and offers recommendations for more responsible development and deployment of detection algorithms.

Contribution

It provides a comprehensive analysis of ethical issues in social bot detection through the FATe framework, highlighting dataset biases and user impacts, and suggests future research directions.

Findings

Survey of existing bot detection datasets and their biases

Discussion of user experiences being misclassified as bots

Identification of ethical challenges in algorithm development

Abstract

A growing suite of research illustrates the negative impact of social media bots in amplifying harmful information with widespread social implications. Social bot detection algorithms have been developed to help identify these bot agents efficiently. While such algorithms can help mitigate the harmful effects of social media bots, they operate within complex socio-technical systems that include users and organizations. As such, ethical considerations are key while developing and deploying these bot detection algorithms, especially at scales as massive as social media ecosystems. In this article, we examine the ethical implications for social bot detection systems through three pillars: training datasets, algorithm development, and the use of bot agents. We do so by surveying the training datasets of existing bot detection algorithms, evaluating existing bot detection datasets, and drawing on discussions of user experiences of people being detected as bots. This examination is grounded in the FATe framework, which examines Fairness, Accountability, and Transparency in consideration of tech ethics. We then elaborate on the challenges that researchers face in addressing ethical issues with bot detection and provide recommendations for research directions. We aim for this preliminary discussion to inspire more responsible and equitable approaches towards improving the social media bot detection landscape.