CIOReview | | 9 MAY 2022representative of the general population being modeled to ensure the model created is not skewed creating biases against the unrepresented category. Shortage of data science skills is a real issue. Besides continuing to hire data scientists, banks should offer "data science internships" leading to full-time employment. Banks also need to encourage existing staff with flair for mathematics/statistics to take courses offered by platforms like Coursera and Udemy at a nominal cost and flexible time lines, and try their hand at solving problems local to their field of work, under the mentorship of a "Citizen Data Science" program.Resistance from the practitioners in trusting the model's decisions requires engaging the practitioners, early on, in the modelling process.On closing, there is an Orwellian school of thought that the machines are taking control. But the reality is that AI is just a prediction tool that can be applied to make better decisions for the benefit of customers, employees and organization. It can be seen as evolution of the data driven enterprise into a machine learning driven enterprise, moving from a rear view mirror driven approach (business intelligence) to a predictive approach of machine learning and AI.Finally, there's the impact of automation in general and AI/ML in particular, displacing jobs as the nature of work changes. While there will new jobs created, as in "bot supervisors" in RPA, and "trainers," "validators" and "explainers" of the model, these will not offset the jobs that have been displaced. Organizations need to retrain staff for the new roles as well as help place the displaced workers in other jobs both internally and externally.These are the key takeaways for organizations to succeed in their AI journey· Get the data in order; a clean, complete and representative of the population being modeled· Get the required data science skills, seeded with data scientists from other areas of the bank and/or external hires supplemented by internships and citizen programs· Create an explorer group, that looks for opportunities in different lines of business· Partner with a solutions provider that can bring technical and domain skills to jump start the program· Establish a competency center, that provides best practices and encourages reuse DATA IS THE CRITICAL RESOURCE THAT THE MACHINES USE TO BUILD THE AI/ML MODELS
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