Interpretability vs Explainability: Unpacking the Role of Human Morality in AI Models


AI is here to stay, and whilst organisations scramble to embrace the power that AI brings, the responsibility of adopting a fair and transparent approach is ever present.

Modernising processes to adapt to an AI-driven world brings with it the risk of deploying unfair or biased solutions with the potential to negatively impact groups of people or sections of society.

In this talk, data scientist Prathiba Krishna will delve into the implications of deploying AI solutions and how to instil conscious considerations for those solutions to be ethical.

The speaker will also talk about the importance of interpretability and explainability of models within the Analytics lifecycle and to shed light on the possible actions that organisations may want to consider while implementing AI responsibly. The talk will structure on the benefits and pitfalls within this journey.


Prathiba is an experienced Data Scientist with a rich background in the Insurance industry. With a Master’s degree in Operational Research with Applied Statistics and Risk, her passion takes form through seeing the varying applications of Machine Learning and AI techniques, and how they propel data scientists to build better models and solutions. Skilled in data analysis and modelling, she utilizes SAS software and Open Source to assess and address problems within enterprise organizations.

Open Data Science




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