Navigating AI in Real-World Applications: Insights from Experts
In the realm of artificial intelligence (AI), applying theoretical concepts to real-world scenarios often presents unique challenges. Guest editor Anna Demming engages with a panel of experts to explore how to achieve "best practice" in AI within the constraints of practical applications and how to avoid common pitfalls.
Throughout the Real World Data Science AI series, various articles have delved into the intricacies of AI, its functionality, and the critical issues surrounding data, model evaluation, ethical considerations, and societal impacts. These discussions provide a foundation for understanding what best practices in AI should entail. However, there remains a gap between theory and practice, with recurring challenges. This article addresses how to navigate real-world limitations and highlights common hazards.
Panelists:
- Ali Al-Sherbaz: Academic Director in Digital Skills at the University of Cambridge, UK
- Janet Bastiman: Chief Data Scientist at Napier and Chair of the Royal Statistical Society Data Science & AI Section
- Jonathan Gillard: Professor of Statistics/Data Science at Cardiff University and Real World Data Science Board member
- Fatemeh Torabi: Senior Research Officer and Data Scientist in Health Data Science at Swansea University and Real World Data Science Board member
Key Insights:
Ali Al-Sherbaz emphasizes the importance of understanding AI fundamentals before embarking on projects. He notes that while computational power has improved over the years, success in AI relies on a deep understanding of the data and the context in which AI is applied. Human insight remains crucial in decision-making processes.
Janet Bastiman highlights the necessity for businesses to clearly define the problems they aim to solve with AI. She stresses the importance of domain expertise and ensuring that AI is the appropriate tool for the task. Misalignment between business goals and AI capabilities often leads to project failures.
Jonathan Gillard discusses the challenge of explainability in AI models, which are often seen as "black boxes." He underscores the need for transparency, especially in regulated industries, to justify AI decisions and ensure ethical practices.
Fatemeh Torabi points out the significance of having clear objectives and measures for AI projects. She emphasizes the balance between risks and benefits, particularly in sensitive fields like healthcare, where data quality and ethical considerations are paramount.
Challenges and Solutions:
The panelists address various challenges, such as the need for high-quality data, the complexities of data management, and the ethical implications of AI decisions. They advocate for a collaborative approach between academia and industry to develop relevant skills and expertise. The rapid evolution of AI tools and techniques necessitates continuous learning and adaptation.
Future Directions:
The discussion also touches on the evolving landscape of AI regulation and the potential for deregulation as familiarity with AI increases. The panelists express curiosity about the future ethical norms surrounding AI and the role of human oversight in AI-driven processes.
Conclusion:
As AI continues to advance, the demand for diverse expertise and a nuanced understanding of data and its implications grows. The panelists agree that while AI offers significant potential, human judgment and ethical considerations remain indispensable in harnessing its capabilities effectively.
About the Author:
Anna Demming is a freelance science writer and editor based in Bristol, UK, with a PhD in physics from King’s College London. She has contributed to various publications, including The Observer, New Scientist, Scientific American, Physics World, and Chemistry World.
This article is republished from Real World Data Science under a Creative Commons Attribution 4.0 International licence. Read the original article here.
