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Massive efforts are made to reduce biases in both data and algorithms inorder to render AI applications fair. These efforts are propelled by varioushigh-profile cases where biased algorithmic decision-making caused harm towomen, people of color, minorities, etc. However, the AI fairness field stillsuccumbs to a blind spot, namely its insensitivity to discrimination againstanimals. This paper is the first to describe the 'speciesist bias' andinvestigate it in several different AI systems. Speciesist biases are learnedand solidified by AI applications when they are trained on datasets in whichspeciesist patterns prevail. These patterns can be found in image recognitionsystems, large language models, and recommender systems. Therefore, AItechnologies currently play a significant role in perpetuating and normalizingviolence against animals. This can only be changed when AI fairness frameworkswiden their scope and include mitigation measures for speciesist biases. Thispaper addresses the AI community in this regard and stresses the influence AIsystems can have on either increasing or reducing the violence that isinflicted on animals, and especially on farmed animals.

Speciesist bias in AI -- How AI applications perpetuate discrimination and unfair outcomes against animals