Artificial Intelligence is a hot topic across industries. Suddenly everybody is discussing AI applications. The reality is that the entry barrier to AI modelling is so low and any programmers and data analysts can develop AI algorithms. With sufficient amount of training data, the result is likely to be called “mostly accurate”.
However, there is an important lesson that AI training courses or vendors will choose not to tell. While the quality of AI models is benchmarked, the “mostly accurate” result is provided at an aggregated level. In the real world, 95% vs 5% accuracy makes little differences as the individual prediction run is either correct or wrong. For example, if an AI model classifies your resume and classified incorrectly, despite that the overall accuracy is 99%. You are still disadvantaged and will certainly complain. The result of this will cascade to higher-levels and influence other users (they don’t know whether their result is correct or not). At the end, nobody will trust the system based on a biased perception.
McKinsey has a similar view. Their report (https://www.mckinsey.com/featured-insights/artificial-intelligence/ai-adoption-advances-but-foundational-barriers-remain) suggested that one barrier to AI adoption is that human may override the AI decisions.
At the end of day, the success measure of AI should not be on how well the algorithm is benchmarked, but rather on how well the result is accepted. This requires a process-oriented approach in the solution development.
