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Software Engineering’s Key Role in AI Content Trustworthiness

Author

Listed:
  • Wumi AJAYI

    (Software Engineering Department, Babcock University, Ilisan Remo, Ogun State Nigeria.)

  • Adekoya Damola Felix

    (Computer Science Department, Lead City University, Ibadan. Oyo State Nigeria.)

  • Ojarikre Oghenenerowho Princewill

    (Computer Science Department, Lead City University, Ibadan. Oyo State Nigeria.)

  • Fajuyigbe Gbenga Joseph

    (Computer Science Department, Lead City University, Ibadan. Oyo State Nigeria.)

Abstract

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. It can also be defined as the science and engineering of making intelligent machines, especially intelligent computer programs. In recent decades, there has been a discernible surge in the focus of the scientific and government sectors on reliable AI. The International Organization for Standardization, which focuses on technical, industrial, and commercial standardization, has devised several strategies to promote trust in AI systems, with an emphasis on fairness, transparency, accountability, and controllability. Therefore, this paper aims to examine the role of Software Engineering in AI Content trustworthiness.

Suggested Citation

  • Wumi AJAYI & Adekoya Damola Felix & Ojarikre Oghenenerowho Princewill & Fajuyigbe Gbenga Joseph, 2024. "Software Engineering’s Key Role in AI Content Trustworthiness," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(4), pages 183-201, April.
    • Handle: RePEc:bjc:journl:v:11:y:2024:i:4:p:183-201
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    References listed on IDEAS

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    1. Nenad Tomašev & Julien Cornebise & Frank Hutter & Shakir Mohamed & Angela Picciariello & Bec Connelly & Danielle C. M. Belgrave & Daphne Ezer & Fanny Cachat van der Haert & Frank Mugisha & Gerald Abil, 2020. "AI for social good: unlocking the opportunity for positive impact," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
    2. Wenjuan Fan & Jingnan Liu & Shuwan Zhu & Panos M. Pardalos, 2020. "Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS)," Annals of Operations Research, Springer, vol. 294(1), pages 567-592, November.
    3. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
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