FATe of Bots: Ethical Considerations of Social Bot Detection

• URL: http://arxiv.org/abs/2602.05200v1
• Date: Thu, 05 Feb 2026 01:53:17 GMT
• Title: FATe of Bots: Ethical Considerations of Social Bot Detection
• Authors: Lynnette Hui Xian Ng, Ethan Pan, Michael Miller Yoder, Kathleen M. Carley,
• Abstract summary: We examine the ethical implications for social bot detection systems through three pillars: training datasets, algorithm development, and the use of bot agents.<n>We aim to inspire more responsible and equitable approaches towards improving the social media bot detection landscape.
• Score: 1.8470340645800405
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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