Version 1
: Received: 21 September 2024 / Approved: 23 September 2024 / Online: 23 September 2024 (12:59:08 CEST)
How to cite:
Chintalapati, A.; Enkhbat, K.; Annamalai, R.; Amali, G. B.; Ozaydin, F.; Noel, M. M. Quantum-Enhanced Algorithmic Fairness and the Advancement of AI Integrity and Responsibility. Preprints2024, 2024091749. https://doi.org/10.20944/preprints202409.1749.v1
Chintalapati, A.; Enkhbat, K.; Annamalai, R.; Amali, G. B.; Ozaydin, F.; Noel, M. M. Quantum-Enhanced Algorithmic Fairness and the Advancement of AI Integrity and Responsibility. Preprints 2024, 2024091749. https://doi.org/10.20944/preprints202409.1749.v1
Chintalapati, A.; Enkhbat, K.; Annamalai, R.; Amali, G. B.; Ozaydin, F.; Noel, M. M. Quantum-Enhanced Algorithmic Fairness and the Advancement of AI Integrity and Responsibility. Preprints2024, 2024091749. https://doi.org/10.20944/preprints202409.1749.v1
APA Style
Chintalapati, A., Enkhbat, K., Annamalai, R., Amali, G. B., Ozaydin, F., & Noel, M. M. (2024). Quantum-Enhanced Algorithmic Fairness and the Advancement of AI Integrity and Responsibility. Preprints. https://doi.org/10.20944/preprints202409.1749.v1
Chicago/Turabian Style
Chintalapati, A., Fatih Ozaydin and Mathew Mithra Noel. 2024 "Quantum-Enhanced Algorithmic Fairness and the Advancement of AI Integrity and Responsibility" Preprints. https://doi.org/10.20944/preprints202409.1749.v1
Abstract
In the evolving digital landscape, the pervasive influence of Artificial Intelligence (AI) on social media platforms reveals a compelling paradox: the capability to provide personalized experiences juxtaposed with inherent biases reminiscent of human imperfections. Such biases prompt rigorous contemplation on matters of fairness, equity, and societal ramifications, and penetrate the foundational essence of AI. Within this intricate context, the research ventures into novel domains by examining the potential of quantum computing as a viable remedy for bias in artificial intelligence. The conceptual framework of the Quantum Sentinel is presented - an innovative approach that employs quantum principles for the detection and scrutiny of biases in AI algorithms. Furthermore, the study poses and investigates the question of whether the integration of advanced quantum computing to address AI bias is seen as an excessive measure or a requisite advancement commensurate with the intricacy of the issue. By intertwining quantum mechanics, AI bias, and the philosophical considerations they induce, this research fosters a discourse on the journey toward ethical AI, thus establishing a foundation for an ethically conscious and balanced digital environment.
Keywords
quantum AI; AI integrity; AI fairness; support vector machines; quantum Zeno effect
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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