Home / Current Issue / Paper 1706538
The Human-Centric AI Manifesto: Principles for Ethical and Responsible Artificial Intelligence
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
Artificial Intelligence (AI) is transforming societies, industries, and individual lives at an unprecedented pace. However, without guiding principles, the risks of unethical, opaque, or biased AI systems increase, threatening human autonomy, privacy, and well-being. Inspired by the Agile Manifesto, we propose the "Human-Centric AI Manifesto" as a set of core principles that prioritize human interests in the design, deployment, and governance of AI systems. This manifesto advocates for transparency, accountability, fairness, and collaboration, aiming to align AI development with societal values and ethical imperatives. We discuss the importance of each principle and illustrate their practical implications for AI developers, stakeholders, and policymakers. The Human-Centric AI Manifesto offers a framework for building AI systems that enhance human capabilities, respect individual rights, and promote trust and inclusivity in technological innovation.
References
[1] Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review, 1(1), 1-15.
[2] Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399.
[3] Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
[4] Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of the Conference on Fairness, Accountability, and Transparency, 77-91.
[5] Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538(7625), 311-313.
[6] Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Springer.
[7] Doshi-Velez, F., & Kim, B. (2017). Towards a Rigorous Science of Interpretable Machine Learning. arXiv preprint.
[8] Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018). Fair, Transparent, and Accountable Algorithmic Decision-making Processes: The Premise, the Proposed Solutions, and the Open Challenges. Philosophy & Technology, 31(4), 611-627.
[9] Beck, K., et al. (2001). Manifesto for Agile Software Development. Agile Alliance.
[10] Roberts, D., & Brij, D. (2016). Agile Principles and Ethical AI Development: A Comparative Study. Journal of Software Development, 3(2), 101-118.
[11] Zicari, R. V. (2021). Implementing AI Ethics in Practice: Ethical Impact Assessments and Ethics Boards. Springer.
[12] Raji, I. D., & Buolamwini, J. (2019). Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products. Proceedings of the Conference on Fairness, Accountability, and Transparency, 429-439.
[13] Jobin, A., Ienca, M., & Vayena, E. (2020). Governance of AI: Ensuring Human-Centric AI through Policy and Regulatory Measures. AI & Society, 35(3), 611-622.
[14] Mitchell, M., et al. (2019). Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220-229.
[15] Veale, M., & Binns, R. (2017). Fairer Machine Learning in the Real World: Mitigating Discrimination without Sacrificing Performance. Big Data & Society, 4(2), 1-17.
[16] To include these references in an SCI paper, format them according to the journal’s guidelines (APA, IEEE, etc.). Here's how these references might look in APA format:
[17] Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review, 1(1), 1-15. https://doi.org/10.1162/99608f92.8cd550d1
[18] Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
[19] Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of the Conference on Fairness, Accountability, and Transparency, 77-91. https://doi.org/10.1145/3287560.3287596
How to cite this paper
@article{1706538,
author = {Joel Frenette},
title = {The Human-Centric AI Manifesto: Principles for Ethical and Responsible Artificial Intelligence},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {5},
pages = {406-412},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706538.pdf},
abstract = {Artificial Intelligence (AI) is transforming societies, industries, and individual lives at an unprecedented pace. However, without guiding principles, the risks of unethical, opaque, or biased AI systems increase, threatening human autonomy, privacy, and well-being. Inspired by the Agile Manifesto, we propose the "Human-Centric AI Manifesto" as a set of core principles that prioritize human interests in the design, deployment, and governance of AI systems. This manifesto advocates for transparency, accountability, fairness, and collaboration, aiming to align AI development with societal values and ethical imperatives. We discuss the importance of each principle and illustrate their practical implications for AI developers, stakeholders, and policymakers. The Human-Centric AI Manifesto offers a framework for building AI systems that enhance human capabilities, respect individual rights, and promote trust and inclusivity in technological innovation.},
month = {November},
}