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Ethical AI: its implications for Enterprise AI Use-cases and Governance
Explainability, Bias, Reproducibility & Accountability
Abstract. With the growing adoption of Open Source based AI/ML systems in enterprises, there is a need to ensure that AI/ML applications are responsibly trained and deployed. This effort is complicated by different governmental organizations and regulatory bodies releasing their own guidelines and policies with little to no agreement on the definition of terms, e.g. there are 20+ definitions of ‘fairness’. In this article, we will provide an overview explaining the key components of this ecosystem: Data, Models, Software, Ethics and Vendor Management. We will outline the relevant regulations such that Compliance/Legal teams are better prepared to establish a comprehensive AI Governance framework. Along the way, we will also highlight some of the technical solutions available today that can be used to automate these compliance requirements.
This is an extended article accompanying my presentation on “Open Source Enterprise AI/ML Governance” at Linux Foundation’s Open Compliance Summit, Dec 2020 (link) (pptx)
Enterprise AI
For the last 4–5 years, we have been working hard towards implementing various AI/ML use-cases at enterprises. We have been focusing on building the most performant models, and now that we have a few of them in production; it is time to move beyond model precision to a more holistic Enterprise AI…