IDEAS home Printed from https://ideas.repec.org/p/osf/lawarx/yu2h3.html
   My bibliography  Save this paper

Framing Human-Automation Regulation: A New Modus Operandi from Cognitive Engineering

Author

Listed:
  • Canellas, Marc
  • Haga, Rachel

Abstract

CITE AS: Canellas, Marc and Haga, Rachel and Miller, Matthew and Bhattacharyya, Raunak and Minotra, Dev and Razin, Yosef, Framing Human-Automation Regulation: A New Modus Operandi from Cognitive Engineering (March 23, 2017). We Robot 2017 at Yale School of Law. Human-automated systems are becoming ubiquitous in our society, from the one-on-one interactions of a driver and their automated vehicle to large-scale interactions of managing a world-wide network of commercial aircraft. Realizing the importance of effectively governing these human-automated systems, there been a recent renaissance of legal-ethical analysis of robotics and artificial-intelligence-based systems. As cognitive engineers, we authored this paper to embrace our responsibility to support effective governance of these human-automated systems. We believe that there are unique synergies between the cognitive engineers who shape human-automated systems by designing the technology, training, and operations, and the lawyers who design the rules, laws, and governance structures of these systems. To show how cognitive engineering can provide a foundation for effective governance, we define and address five essential questions regarding human-automated systems: 1) Complexity: What makes human-automation systems complex? 2) Definitions: How should we define and classify different types of human-autonomous systems? 3) Transparency: How do we determine and achieve the right levels of transparency for operators and regulators? 4) Accountability: How should we determine responsibility for the actions of human-automation systems? 5) Safety: How do human-automated systems fail? Our answers, drawn from the diverse domains related to cognitive engineering, show that care should be taken when making assumptions about human-automated systems, that cognitive engineering can provide a strong foundation for legal-ethical regulations of human-automated systems, and that there is still much work to be done by lawyers, ethicists, and technologists together.

Suggested Citation

  • Canellas, Marc & Haga, Rachel, 2017. "Framing Human-Automation Regulation: A New Modus Operandi from Cognitive Engineering," LawArXiv yu2h3, Center for Open Science.
    • Handle: RePEc:osf:lawarx:yu2h3
    DOI: 10.31219/osf.io/yu2h3
    as

    Download full text from publisher

    File URL: https://osf.io/download/5e95016ff1353503ecd532be/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/yu2h3?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
    ---><---

    References listed on IDEAS

    as
    1. Canellas, Marc & Haga, Rachel, 2016. "Lost in Translation: Building a Common Language for Regulating Autonomous Weapons," LawArXiv z5mye, Center for Open Science.
    2. W. Clifton Baldwin & Wilson N. Felder & Brian J. Sauser, 2011. "Taxonomy of increasingly complex systems," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 9(3), pages 298-316.
    3. Davide Castelvecchi, 2016. "Can we open the black box of AI?," Nature, Nature, vol. 538(7623), pages 20-23, October.
    4. Gordon R. Foxall, 2005. "Understanding Consumer Choice," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-51002-9, October.
    Full references (including those not matched with items on IDEAS)

    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. Alireza Rezazadeh & Yasamin Jafarian & Ali Kord, 2022. "Explainable Ensemble Machine Learning for Breast Cancer Diagnosis Based on Ultrasound Image Texture Features," Forecasting, MDPI, vol. 4(1), pages 1-13, February.
    2. Anna Lewandowska & Joanna Witczak & Pasquale Giungato & Christian Dierks & Przemyslaw Kurczewski & Katarzyna Pawlak-Lemanska, 2018. "Inclusion of Life Cycle Thinking in a Sustainability-Oriented Consumer’s Typology: A Proposed Methodology and an Assessment Tool," Sustainability, MDPI, vol. 10(6), pages 1-19, June.
    3. Hsu, Chia-Wei & Xiong, Rui & Chen, Nan-Yow & Li, Ju & Tsou, Nien-Ti, 2022. "Deep neural network battery life and voltage prediction by using data of one cycle only," Applied Energy, Elsevier, vol. 306(PB).
    4. James, Victoria K., 2010. "A socio-cultural approach to exploring consumer boycott intelligence: A commentary essay," Journal of Business Research, Elsevier, vol. 63(4), pages 363-365, April.
    5. Laith T. Khrais, 2020. "Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce," Future Internet, MDPI, vol. 12(12), pages 1-14, December.
    6. Martin Eling & Davide Nuessle & Julian Staubli, 2022. "The impact of artificial intelligence along the insurance value chain and on the insurability of risks," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(2), pages 205-241, April.
    7. Iivi Riivits-Arkonsuo & Anu Leppiman, 2015. "Young Consumers and their Brand Love," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 5(10), pages 33-44, Octomber.
    8. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method," Applied Energy, Elsevier, vol. 348(C).
    9. Alwosheel, Ahmad & van Cranenburgh, Sander & Chorus, Caspar G., 2018. "Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis," Journal of choice modelling, Elsevier, vol. 28(C), pages 167-182.
    10. Mencarelli, Rémi & Lombart, Cindy, 2017. "Influences of the perceived value on actual repurchasing behavior: Empirical exploration in a retailing context," Journal of Retailing and Consumer Services, Elsevier, vol. 38(C), pages 12-21.
    11. Eduardo Graells-Garrido & Vanessa Peña-Araya & Loreto Bravo, 2020. "Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned," Sustainability, MDPI, vol. 12(15), pages 1-17, July.
    12. Pujin Wang & Jianzhuang Xiao & Ken’ichi Kawaguchi & Lichen Wang, 2022. "Automatic Ceiling Damage Detection in Large-Span Structures Based on Computer Vision and Deep Learning," Sustainability, MDPI, vol. 14(6), pages 1-24, March.
    13. De Cannière, Marie Hélène & De Pelsmacker, Patrick & Geuens, Maggie, 2009. "Relationship Quality and the Theory of Planned Behavior models of behavioral intentions and purchase behavior," Journal of Business Research, Elsevier, vol. 62(1), pages 82-92, January.
    14. Chenfeng Yan & Quan Chen & Xinyue Zhou & Xin Dai & Zhilin Yang, 2024. "When the Automated fire Backfires: The Adoption of Algorithm-based HR Decision-making Could Induce Consumer’s Unfavorable Ethicality Inferences of the Company," Journal of Business Ethics, Springer, vol. 190(4), pages 841-859, April.
    15. Brian G Booth & Eva Hoefnagels & Toon Huysmans & Jan Sijbers & Noël L W Keijsers, 2020. "PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-22, February.
    16. Dominic Chalmers & Niall G. MacKenzie & Sara Carter, 2021. "Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution," Entrepreneurship Theory and Practice, , vol. 45(5), pages 1028-1053, September.
    17. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
    18. Pei-Chen Tsai & Tsung-Hua Lee & Kun-Chi Kuo & Fang-Yi Su & Tsung-Lu Michael Lee & Eliana Marostica & Tomotaka Ugai & Melissa Zhao & Mai Chan Lau & Juha P. Väyrynen & Marios Giannakis & Yasutoshi Takas, 2023. "Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    19. Hui Niu & Siyuan Li & Jian Li, 2022. "MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization," Papers 2210.01774, arXiv.org.
    20. Michael Meiser & Ingo Zinnikus, 2024. "A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities," Energies, MDPI, vol. 17(9), pages 1-29, April.

    More about this item

    Statistics

    Access and download statistics

    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:osf:lawarx:yu2h3. 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: OSF (email available below). General contact details of provider: https://osf.io/preprints/lawarxiv/discover .

    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.