Speciesist bias in AI: how AI applications perpetuate discrimination and unfair outcomes against animals

Abstract

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.

Ethical and methodological challenges in building morally informed AI systems

“…In sum, many parts of the ethics literature are predominantly echoing previous publications, leading to a discourse that is frequently repetitive, combined with a tacit disregard for underpinning claims with empirical insights or statistics. Additionally, the literature exhibits a limitation in that it is solely anthropocentric, neglecting perspectives on generative AI that consider its impacts on non-human animals [159][160][161][162][163] . Another noticeable trait of the discourse on the ethics of generative AI is its emphasis on…”

The ethics of sustainable AI: Why animals (should) matter for a sustainable use of AI

“…This can be argued for not just theoretically. Research on AI systems that are applied specifically to animals (Bossert & Hagendorff, 2021), as well as empirical investigations of speciesist bias in foundational language and vision models (Hagendorff et al, 2022; Takeshita et al, 2022) reveal the many disadvantages and harms that AI systems can afflict on animals. However, although a handful of papers demanding otherwise (Bendel, 2018; Owe & Baum, 2021; Singer & Tse, 2022; Ziesche, 2021), AI ethics has a purely anthropocentric tailoring where nonhumans are not part of normative considerations, despite the overlaps between concepts like fairness, well‐being, sustainability, and so forth, with issues in animal ethics.…”

Apropos of “Speciesist bias in AI: how AI applications perpetuate discrimination and unfair outcomes against animals”

“…Throughout their article, Hagendorff et al [3] assume that different treatments of individuals of different species is prima facie unjust, without a nuanced consideration of whether this necessarily is the case and whether there may be an explanation for this behaviour which is not speciesist in nature. Indeed, previous work [1] explains how an unequal treatment of individuals of two species can be ethically justified as emerging from the differences in the associated sentient environments (thus making irrelevant both the similarity of their cognitive powers or even sentient experiences, if they indeed are such), them in part being consequent on the species' inherent biology, and in part on incidental factors, including interestingly, humans' attitudes, which are shown not to be inherently speciesist.…”

“…"Even when people hold true stereotypes, they have little effect on how people judge or treat individuals about whom they have other, individualized information." [all emphasis mine] Thus, if anything, the fears of Hagendorff et al [3] seem to be based in speciesism, albeit an anti-anthropic variant thereof, to coin a word.…”

“…Lastly, a more subtle error pervasive in the work of Hagendorff et al [3] concerns the objection that prompts such as "What are sheep good for?" result in answers like "Cuteness, wool, bleating, meat", and specifically that: "This prompt can in itself raise the criticism for speciesism because it is suggesting that animals are means to an end.…”