FATe of Bots: Ethical Considerations of Social Bot Detection

2026-02-05

AI Summary

This study addresses the ethical risks—pertaining to fairness, accountability, and transparency (FATe)—arising from the large-scale deployment of social bot detection systems. It is the first to systematically integrate the FATe ethical framework into this domain, examining ethical blind spots across three dimensions: training datasets, algorithmic design, and the use of bot proxies. Through a synthesis of literature review, dataset evaluation, and analysis of user misjudgment experiences, the work identifies critical ethical concerns inherent in current detection approaches. The findings underscore the need for more responsible and equitable practices in social bot detection research and offer concrete recommendations to mitigate these risks, thereby promoting ethically sound development and deployment of such technologies.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Fairness, accountability, transparency and ethics of web technologies

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.

Problem

• social bot detection
• ethical implications
• Fairness
• Accountability
• Transparency

Innovation

• FATe framework
• social bot detection
• ethical AI
• algorithmic fairness
• transparent machine learning