With so much riding on the performance and accuracy of artificial intelligence algorithms -- from medical diagnoses to legal advice to financial planning -- there have been calls for the "professionalization" of AI developers, through mechanisms such as certifications and accreditations, all the way up to government mandates. After all, it is argued, healthcare professionals, lawyers and financial advisors all require varying levels of certification, why shouldn't the people creating the AI systems that could replace the advice of these professionals also be verified?
"For example, you understand that architects, electricians and other construction professionals know how to build a house," says Fernando Lucini, global lead in data science and machine learning engineering at Accenture. "They've had requisite training and understand their roles and responsibilities, safety standards and protocols to follow throughout the construction process. It's unlikely that you'd trust a 'citizen architect' to build your home in the same way that you wouldn't visit a 'citizen doctor' when you get sick."
A counter-argument may be that there are already too few people well-versed in the ways of AI, and we need everyone we can pull into such efforts. Any attempts at requiring formal certifications or accreditation may stymie such efforts.
In a recent post and executive brief[1], Lucini urges organizations movie on their own toward professionalizing the roles and responsibilities of AI practitioners. "Stakeholders -- from practitioners to leaders across the private and public sector -- must come together to distinguish clear roles and responsibilities for AI practitioners; demand the right level of education and training for said practitioners; define processes for developing, deploying and managing AI, and democratize AI literacy across the enterprise. Real value can only be realized when trained AI practitioners are working hand in hand with