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Building trust and confidence in AI
Journal of AI, Robotics & Workplace Automation,
1
(4),
350-359
(2022)
Abstract
While some industries are rushing to adopt artificial intelligence (AI) technologies, there are those who are lagging behind, due either to their own lack of confidence or to perceived conflicts with regulation or their customer needs. This paper covers some of the myths perpetuated within the AI community regarding trust and confidence and how you can begin to build AI solutions with end user trust as a priority considering the latest regulatory proposals.
Keywords: trust; confidence; explainability; testing; risk; legislation
The full article is available to subscribers to the journal.
Author's Biography
Janet Bastiman is a committee member for the Royal Statistical Society Data Science Section and the IEEE UK STEM initiative. She regularly contributes evidence to the UK Parliament Science and Technology Committee. In addition to leading the artificial intelligence (AI) strategy at Napier, Janet is also an AI specialist for London-based venture capital company MMC Ventures. Janet holds two undergraduate degrees and a Master’s in addition to her doctorate in computational neuroscience and has worked in industry for over 20 years. She regularly speaks at conferences including Minds Mastering Machines, InfoQ, London AI Summit, as well as giving her time to grassroots meet-ups. Janet is passionate about ensuring that this pervasive technology is developed with best practices and that it is well understood by more than just its implementers.
