AI and Africa’s Struggling Public Institutions

Geoffrey Hinton, one of the founding fathers of deep learning, a notable technique driving AI, recently tweeted, ‘Suppose you have cancer and you have to choose between a black box AI surgeon that cannot explain how [treatment] works but has a 90 per cent cure rate and a human surgeon with an 80 per cent cure rate. Do you want the AI surgeon to be illegal?’

AI is a general-purpose technology with the potential to transform economies and societies at large, but responsible use is crucial. Due to the potential for AI’s implementation across diverse sectors, there should be consistency to demands for transparency, especially in high-stakes areas like healthcare. For instance, demanding interpretable models in criminal justice to reduce recidivism but ignoring this issue when tackling health issues such as coronavirus or cancer is short-sighted. Although the rules of engagement guiding both domains differ, the taxonomy of software used is similar. In reference to Hinton’s tweet, if an AI surgeon has a 90-per-cent success rate when treating cancer, what happens to the remaining ten per cent of patients? If their lives are lost, is this approach fair? Transparency equips us with the knowledge to deliver more, even though the involvement of technical and non-technical approaches makes the transparent use of AI a herculean task. One approach for the use of AI in healthcare is to make patients aware of the implications and error rates and for companies to allocate research and development resources for the development of interpretable models and tools across various sectors. While there is a push to continuously democratize AI tools and models, scaling AI in healthcare is just one sector that still relies heavily on accessing sub-population data and domain-expertise, which can be both unreliable and expensive.