The ethics of artificial intelligence, or AI ethics, is a rapidly growing field, and rightly so. While the range of issues and groups of stakeholders concerned by the field of AI ethics is expanding, with speculation about whether it extends even to the machines themselves, there is a group of sentient beings who are also affected by AI, but are rarely mentioned within the field of AI ethics—the nonhuman animals. This paper seeks to explore the kinds of impact AI has on nonhuman animals, the severity of these impacts, and their moral implications. We hope that this paper will facilitate the development of a new field of philosophical and technical research regarding the impacts of AI on animals, namely, the ethics of AI as it affects nonhuman animals.
Massive efforts are made to reduce biases in both data and algorithms to render AI applications fair. These efforts are propelled by various high-profile cases where biased algorithmic decision-making caused harm to women, people of color, minorities, etc. However, the AI fairness field still succumbs to a blind spot, namely its insensitivity to discrimination against animals. This paper is a critical comment on current fairness research in AI. It is the first to describe the ‘speciesist bias’ and investigate it in several different AI systems by reflecting on the problem via a normative analysis and by probing, in several case studies, image recognition, word embedding, and language models with established methods for bias detection. We claim that animals matter morally and that discriminating against them is unethical. Furthermore, we provide evidence for speciesist biases in all the mentioned areas of AI. We find that speciesist biases are solidified by many mainstream AI applications, especially in the fields of computer vision as well as natural language processing. In both cases, this occurs because the models are trained on datasets in which speciesist patterns prevail. Therefore, AI technologies currently play a significant role in perpetuating and normalizing violence against animals. To change this, AI fairness frameworks must widen their scope and include mitigation measures for speciesist biases. This paper addresses the AI community in this regard and stresses the influence AI systems can have on either increasing or reducing the violence that is inflicted on animals, especially on farmed animals.
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