MAY - JUNE - 20229will not only help you to understand the program behavior and expected quality of results, but will also help you to identify and plan for upcoming improvement opportunities.Procure Reliable DataThe term "GIGO" (i.e., garbage in, garbage out) is a term used to describe the outcome of poor data and code quality. In short, you get what you give. Errors or misunderstandings of data used by your calculations may result in driving poor decisions.If you do not scrub your data and test your code thoroughly with an abundance of test cases, you may generate questionable results which can be easily misinterpreted. Also, the quantity of historical data will affect the level of certainty that you may expect. It may be okay if your model is based upon 10 years of financial data, given the understanding that there was a two-year economic correction within the data; only you can accept that level of model reliability or strive to procure a longer history of data to add more stability in calculations. Putting it All Together -Assumptions, Rules, and DataArtificial Intelligence is driven from this trinity: assumptions, rules, and data. These are the requirements that go into building and operating the model. Having clear and meaningful documentation to support the model is paramount to its sound operation within the expected level of certainty. This documentation also allows you to trace your steps when troubleshooting the model's code and criteria and to facilitate further "what if" analysis and expansion of the model complexity in the future. Comprehensive and controlled testing of data and AI code results are very important to understand the model behavior and its expected level of reliability.ConclusionThose stepping into the world of model development and implementing AI technology should be aware of the risks around the quality and integrity of these systems. A comprehensive understanding of the assumptions, rules, and data within these systems will assist in gauging the reliability of these tools when making business decisions. Applying a high level of due diligence can protect the company from poor use of this technology and even provide regulators with a higher level of comfort, especially when this technology is used for making decisions that affect consumers and customers. Strong documentation will also support the company's due care if challenged by any unforeseen litigation. BCThose stepping into the world of model development and implementing AI technology should be aware of the risks around the quality and integrity of these systems
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