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

• URL: http://arxiv.org/abs/2202.10848v1
• Date: Tue, 22 Feb 2022 12:23:21 GMT
• Title: Speciesist bias in AI -- How AI applications perpetuate discrimination and unfair outcomes against animals
• Abstract summary: This paper is the first to describe the'speciesist bias' and investigate it in several different AI systems. It stresses the influence AI systems can have on either increasing or reducing the violence that is inflicted on animals.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Speciesism in AI: Evaluating Discrimination Against Animals in Large Language Models [0.0]
  We investigate whether large language models (LLMs) exhibit speciesist bias.<n>LLMs reliably detected speciesist statements but rarely condemned them.<n>We argue that expanding AI fairness and alignment frameworks to explicitly include non-human moral patients is essential.
  arXiv  Detail & Related papers  (2025-08-15T15:22:00Z)
• Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing [55.2480439325792]
  Misclassification can lead to false plagiarism accusations and misleading claims about AI prevalence in online content. We systematically evaluate eleven state-of-the-art AI-text detectors using our AI-Polished-Text Evaluation dataset. Our findings reveal that detectors frequently misclassify even minimally polished text as AI-generated, struggle to differentiate between degrees of AI involvement, and exhibit biases against older and smaller models.
  arXiv  Detail & Related papers  (2025-02-21T18:45:37Z)
• Aligning Generalisation Between Humans and Machines [74.120848518198]
  AI technology can support humans in scientific discovery and forming decisions, but may also disrupt democracies and target individuals.<n>The responsible use of AI and its participation in human-AI teams increasingly shows the need for AI alignment.<n>A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
  arXiv  Detail & Related papers  (2024-11-23T18:36:07Z)
• "I Am the One and Only, Your Cyber BFF": Understanding the Impact of GenAI Requires Understanding the Impact of Anthropomorphic AI [55.99010491370177]
  We argue that we cannot thoroughly map the social impacts of generative AI without mapping the social impacts of anthropomorphic AI. anthropomorphic AI systems are increasingly prone to generating outputs that are perceived to be human-like.
  arXiv  Detail & Related papers  (2024-10-11T04:57:41Z)
• Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
  This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act) Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence. As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
  arXiv  Detail & Related papers  (2024-10-10T17:38:38Z)
• Human Bias in the Face of AI: The Role of Human Judgement in AI Generated Text Evaluation [48.70176791365903]
  This study explores how bias shapes the perception of AI versus human generated content. We investigated how human raters respond to labeled and unlabeled content.
  arXiv  Detail & Related papers  (2024-09-29T04:31:45Z)
• Rolling in the deep of cognitive and AI biases [1.556153237434314]
  We argue that there is urgent need to understand AI as a sociotechnical system, inseparable from the conditions in which it is designed, developed and deployed. We address this critical issue by following a radical new methodology under which human cognitive biases become core entities in our AI fairness overview. We introduce a new mapping, which justifies the humans to AI biases and we detect relevant fairness intensities and inter-dependencies.
  arXiv  Detail & Related papers  (2024-07-30T21:34:04Z)
• The Pursuit of Fairness in Artificial Intelligence Models: A Survey [2.124791625488617]
  This survey offers a synopsis of the different ways researchers have promoted fairness in AI systems. A thorough study is conducted of the approaches and techniques employed by researchers to mitigate bias in AI models. We also delve into the impact of biased models on user experience and the ethical considerations to contemplate when developing and deploying such models.
  arXiv  Detail & Related papers  (2024-03-26T02:33:36Z)
• Bending the Automation Bias Curve: A Study of Human and AI-based Decision Making in National Security Contexts [0.0]
  We theorize about the relationship between background knowledge about AI, trust in AI, and how these interact with other factors to influence the probability of automation bias. We test these in a preregistered task identification experiment across a representative sample of 9000 adults in 9 countries with varying levels of AI industries.
  arXiv  Detail & Related papers  (2023-06-28T18:57:36Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies [11.323961700172175]
  This survey paper offers a succinct, comprehensive overview of fairness and bias in AI. We review sources of bias, such as data, algorithm, and human decision biases. We assess the societal impact of biased AI systems, focusing on the perpetuation of inequalities and the reinforcement of harmful stereotypes.
  arXiv  Detail & Related papers  (2023-04-16T03:23:55Z)
• Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
  Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research. An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system. We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
  arXiv  Detail & Related papers  (2022-04-11T14:36:39Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.