A ModelOps Approach to Address Ethical Concerns in AI Systems


Human-generated data carries inherent biases, which are transferred to the machines that learn using this data, and ultimately seep into the decisioning that follows- often creating a vicious cycle that reinforces this pattern. Artificial Intelligence proliferates business value by embedding Machine Learning, automation, and intelligent decisioning within business processes. As AI systems guide organizational decision-making and impact lives of individuals and societies, there is a need to evaluate ethical implications of these decisions.

This session takes the philosophical question of ethical responsibility to the pragmatic realm and provides a business framework to address ethical concerns in AI systems. It provides the foundation required to build and implement business AI that is responsible and future-proof.


May Masoud is an AI Advisor at SAS Canada, trained in classical statistics and modern machine learning. Leveraging her analytics background, she helps businesses implement components of artificial intelligence and machine learning to surface actionable insights from their data and eliminate inefficiencies from the analytics process.

May developed her quantitative foundation through an undergraduate degree in Statistics and Economics. Following that, she advanced her applied analytics toolkit with a Masters of Business Analytics from the Schulich School of Business. This cocktail of technical and business expertise has shaped May into an analytics practitioner and thought leader. Outside of her client engagements, May invests her time in developing and delivering thought leadership for the business and academic community. A large focus of these efforts is on topics such as artificial intelligence and the ethics of technology.

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