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Embedded Ethics for Responsible Artificial Intelligence Systems (EE-RAIS) in disaster management: a conceptual model and its deployment

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Abstract

In this paper, we argue that Responsible Artificial Intelligence Systems (RAIS) require a shift toward embedded ethics to address value-based challenges facing AI in disaster management; and we propose a model to achieve it. Disaster management requires Artificial Intelligence Systems (AIS) that would be sensitive to ethical, legal, and multi-dimensional values while being responsive and accountable in complex and acute disruptions that simultaneously call for fair, value-laden, and immediate decisions. Without such a necessary shift, AIS will be incapable of responding properly to major value-based challenges of axiological and hierarchical types, and might leave AIS vulnerable to meta-disasters, such as intelligent digital disasters. This study focuses on RAI in the context of disaster management and proposes a model of Embedded Ethics for Responsible Artificial Intelligence Systems (EE-RAIS), which is empowered by four platforms of embedded ethics—educational, cross-functional, developmental, and algorithmic embedded ethics—as well as four imperative metrics—ethical intelligence, legal intelligence, social-emotional competency, and artificial wisdom. The final section of the paper explores how EE-RAIS can be deployed for the purpose of disaster management and fair crisis informatics.

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Notes

  1. . Global Risk Management Study. (2019). Defining the risk function’s sphere of control, & Responsible AI in practice—essential but not easy. (2021). Accenture.

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Acknowledgements

We are immensely grateful to the anonymous referees of this journal for their comments and insightful suggestions.

Funding

The authors would like to acknowledge funding support from the Texas A&M X-Grant Presidential Excellence Fund (Grant AI2102). Any opinions, findings, conclusions, or recommendations expressed in this research are those of the authors and do not necessarily reflect the views of the funding agency.

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Authors and Affiliations

  1. Department of Philosophy, The State University of New York at Albany, Albany, NY, 12203, USA

    Saleh Afroogh

  2. Zachry Department of Civil and Environmental Engineering, Texas A&M University, 199 Spence St., College Station, TX, 77840, USA

    Ali Mostafavi

  3. Department of Genetics, Stanford University School of Medicine, 450 Serra Mall, Stanford, CA, 94305, USA

    Ali Akbari

  4. Department of Philosophy (PhD Alumni), IPM Institute for Research in Fundamental Sciences, Tehran, Iran

    Yasser Pouresmaeil

  5. Global Urban Studies Department, Rutgers University, Newark, NJ, 07102, USA

    Sajedeh Goudarzi

  6. Comparative Literature, The University at Buffalo, Buffalo, USA

    Faegheh Hajhosseini

  7. Turner & Townsend Inc., Mission Viejo, CA, 92691, USA

    Kambiz Rasoulkhani

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  1. Saleh Afroogh
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Contributions

The hypothesis and approach to the work were conceived collaboratively by the authors. AM and SA generated the idea for the research. AM conducted three focus groups on the research topic; SA conducted the research and formed the arguments for Embedded Ethics for Responsible AIS approach. SA, A.A., and Y.P. developed Sects. 2, 3, and 4.1. S.A, S.G, F.H, and K.R. developed Sect. 4.2. Sections 1, 2, and conclusion are authored by SA. The authors agree to be accountable for the relevant aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Saleh Afroogh.

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Afroogh, S., Mostafavi, A., Akbari, A. et al. Embedded Ethics for Responsible Artificial Intelligence Systems (EE-RAIS) in disaster management: a conceptual model and its deployment. AI Ethics 4, 1117–1141 (2024). https://doi.org/10.1007/s43681-023-00309-1

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