IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v67y2021ics0160791x21001536.html
   My bibliography  Save this article

Animals and AI. The role of animals in AI research and application – An overview and ethical evaluation

Author

Listed:
  • Bossert, Leonie
  • Hagendorff, Thilo

Abstract

Artificial intelligence (AI) technologies and their fields of application are among the most debated developments of recent times. Although being widely discussed academically, publicly and in policy debates, certain aspects of their research, development and application are completely ignored, namely the impact AI has on animals. Animals are affected by the research on and development of this technology since it partially relies on animal testing. In addition, AI is also being applied to improve monitoring and marketing of animals in an agricultural context. We argue that it is insufficient to exclude these aspects from debates around AI. In addition to the surveillance-applications on animals, which can be evaluated as impacting them negatively, AI applications, from which individual animals can benefit, do exist. These can primarily be found in nature and wildlife conservation, as we point out at the end of the paper. By providing an overview on how these technologies are applied to animals and how this affects them, this paper aims to fill a previously existing research gap.

Suggested Citation

  • Bossert, Leonie & Hagendorff, Thilo, 2021. "Animals and AI. The role of animals in AI research and application – An overview and ethical evaluation," Technology in Society, Elsevier, vol. 67(C).
    • Handle: RePEc:eee:teinso:v:67:y:2021:i:c:s0160791x21001536
    DOI: 10.1016/j.techsoc.2021.101678
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X21001536
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techsoc.2021.101678?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. H Bart van der Worp & David W Howells & Emily S Sena & Michelle J Porritt & Sarah Rewell & Victoria O'Collins & Malcolm R Macleod, 2010. "Can Animal Models of Disease Reliably Inform Human Studies?," PLOS Medicine, Public Library of Science, vol. 7(3), pages 1-8, March.
    2. Cockshott, Paul & Renaud, Karen, 2016. "Humans, robots and values," Technology in Society, Elsevier, vol. 45(C), pages 19-28.
    3. Oriol Vinyals & Igor Babuschkin & Wojciech M. Czarnecki & Michaël Mathieu & Andrew Dudzik & Junyoung Chung & David H. Choi & Richard Powell & Timo Ewalds & Petko Georgiev & Junhyuk Oh & Dan Horgan & M, 2019. "Grandmaster level in StarCraft II using multi-agent reinforcement learning," Nature, Nature, vol. 575(7782), pages 350-354, November.
    4. Reis, Germano Glufke & Heidemann, Marina Sucha & Borini, Felipe Mendes & Molento, Carla Forte Maiolino, 2020. "Livestock value chain in transition: Cultivated (cell-based) meat and the need for breakthrough capabilities," Technology in Society, Elsevier, vol. 62(C).
    5. Anthony M. Zador, 2019. "A critique of pure learning and what artificial neural networks can learn from animal brains," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    6. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    7. Li, Jian & Huang, Jin-Song, 2020. "Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory," Technology in Society, Elsevier, vol. 63(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tironi, Martín & Rivera Lisboa, Diego Ignacio, 2023. "Artificial intelligence in the new forms of environmental governance in the Chilean State: Towards an eco-algorithmic governance," Technology in Society, Elsevier, vol. 74(C).
    2. Khaliq, Abdul & Waqas, Ali & Nisar, Qasim Ali & Haider, Shahbaz & Asghar, Zunaina, 2022. "Application of AI and robotics in hospitality sector: A resource gain and resource loss perspective," Technology in Society, Elsevier, vol. 68(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Zhengzhi & Zheng, Lei & Perc, Matjaž & Li, Yumeng, 2024. "Interaction state Q-learning promotes cooperation in the spatial prisoner's dilemma game," Applied Mathematics and Computation, Elsevier, vol. 463(C).
    2. Weifan Long & Taixian Hou & Xiaoyi Wei & Shichao Yan & Peng Zhai & Lihua Zhang, 2023. "A Survey on Population-Based Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
    3. Xuan-Kun Li & Jian-Xu Ma & Xiang-Yu Li & Jun-Jie Hu & Chuan-Yang Ding & Feng-Kai Han & Xiao-Min Guo & Xi Tan & Xian-Min Jin, 2024. "High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    4. Li, Wenqing & Ni, Shaoquan, 2022. "Train timetabling with the general learning environment and multi-agent deep reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 230-251.
    5. Qingyan Li & Tao Lin & Qianyi Yu & Hui Du & Jun Li & Xiyue Fu, 2023. "Review of Deep Reinforcement Learning and Its Application in Modern Renewable Power System Control," Energies, MDPI, vol. 16(10), pages 1-23, May.
    6. Michael Curry & Alexander Trott & Soham Phade & Yu Bai & Stephan Zheng, 2022. "Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning," Papers 2201.01163, arXiv.org, revised Feb 2022.
    7. Dong Liu & Feng Xiao & Jian Luo & Fan Yang, 2023. "Deep Reinforcement Learning-Based Holding Control for Bus Bunching under Stochastic Travel Time and Demand," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    8. Christopher R. Madan, 2020. "Considerations for Comparing Video Game AI Agents with Humans," Challenges, MDPI, vol. 11(2), pages 1-12, August.
    9. Yuling Huang & Xiaoping Lu & Chujin Zhou & Yunlin Song, 2023. "DADE-DQN: Dual Action and Dual Environment Deep Q-Network for Enhancing Stock Trading Strategy," Mathematics, MDPI, vol. 11(17), pages 1-27, August.
    10. Lauralyn A McIntyre & David Moher & Dean A Fergusson & Katrina J Sullivan & Shirley H J Mei & Manoj Lalu & John Marshall & Malcolm Mcleod & Gilly Griffin & Jeremy Grimshaw & Alexis Turgeon & Marc T Av, 2016. "Efficacy of Mesenchymal Stromal Cell Therapy for Acute Lung Injury in Preclinical Animal Models: A Systematic Review," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-16, January.
    11. Yuchen Zhang & Wei Yang, 2022. "Breakthrough invention and problem complexity: Evidence from a quasi‐experiment," Strategic Management Journal, Wiley Blackwell, vol. 43(12), pages 2510-2544, December.
    12. Arsenyan, Jbid & Mirowska, Agata & Piepenbrink, Anke, 2023. "Close encounters with the virtual kind: Defining a human-virtual agent coexistence framework," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    13. Federico Bolaños & Javier G. Orlandi & Ryo Aoki & Akshay V. Jagadeesh & Justin L. Gardner & Andrea Benucci, 2024. "Efficient coding of natural images in the mouse visual cortex," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    14. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
    15. Yi, Zonggen & Luo, Yusheng & Westover, Tyler & Katikaneni, Sravya & Ponkiya, Binaka & Sah, Suba & Mahmud, Sadab & Raker, David & Javaid, Ahmad & Heben, Michael J. & Khanna, Raghav, 2022. "Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system," Applied Energy, Elsevier, vol. 328(C).
    16. Zhang, Weidong & Zuo, Na & He, Wu & Li, Songtao & Yu, Lu, 2021. "Factors influencing the use of artificial intelligence in government: Evidence from China," Technology in Society, Elsevier, vol. 66(C).
    17. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    18. Ostheimer, Julia & Chowdhury, Soumitra & Iqbal, Sarfraz, 2021. "An alliance of humans and machines for machine learning: Hybrid intelligent systems and their design principles," Technology in Society, Elsevier, vol. 66(C).
    19. Boute, Robert N. & Gijsbrechts, Joren & van Jaarsveld, Willem & Vanvuchelen, Nathalie, 2022. "Deep reinforcement learning for inventory control: A roadmap," European Journal of Operational Research, Elsevier, vol. 298(2), pages 401-412.
    20. Kimberley E Wever & Carlijn R Hooijmans & Niels P Riksen & Thomas B Sterenborg & Emily S Sena & Merel Ritskes-Hoitinga & Michiel C Warlé, 2015. "Determinants of the Efficacy of Cardiac Ischemic Preconditioning: A Systematic Review and Meta-Analysis of Animal Studies," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-17, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:teinso:v:67:y:2021:i:c:s0160791x21001536. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/technology-in-society .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.