This session will include a moderated Q&A featuring questions from the live audience.
Robust data analytics and machine learning models depend on four key elements: data, algorithms, assumptions, and ethics. Although data, algorithms, and assumptions help in deriving insights, the next phase in the analytics lifecycle—transforming insights into decisions—is dependent on ethics.
Navigating grey zones related to ethics in data and analytics is a challenge for most organizations. Legal and regulatory frameworks typically offer clear directions and prescriptions on the application of data and analytics, but ethical issues in data and analytics are often ambiguous and subjective. In addition, most of the current discussion on ethics focuses on data privacy.
To avoid missteps, companies need a clear and holistic understanding of the dimensions and factors that influence their ethical norms. In this presentation, Mr. Southekal will talk about five key ethical factors or dimensions organizations can leverage for implementing the insights from data analytics and ML.