  Massive efforts are made to reduce biases in both data and algorithms in
order 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 the first to describe the 'speciesist bias' and
investigate it in several different AI systems. Speciesist biases are learned
and solidified by AI applications when they are trained on datasets in which
speciesist patterns prevail. These patterns can be found in image recognition
systems, large language models, and recommender systems. Therefore, AI
technologies currently play a significant role in perpetuating and normalizing
violence against animals. This can only be changed when AI fairness frameworks
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, and especially on farmed animals.
