This guide is designed to assist Educational Technology leaders and developers create more inclusive and equitable AI for higher education. It begins by defining key concepts like equity, trust, accountability, and responsibility in the context of AI in EdTech. It then examines how bias impacts equity and how addressing it can improve student outcomes, institutional metrics, and product value. It offers a comprehensive roadmap for ensuring equity is infused throughout the machine learning pipeline, complete with rationale, resources, sample code, examples, and tools project teams and developers can quickly implement.
Building AI solutions requires a comprehensive toolkit encompassing various tools, libraries, frameworks, and platforms. Paritii AI has created a zoo of predevelopment considerations, development, production, and post-production processes that incorporate bias and equity considerations in the entire AI/ML lifecycle in this GitHub repo. Developers wishing to develop bias-aware ML/AI products can follow the guidelines, code, and technical documentation in this repository's project lifecycle. Junior developers can start with the Demo Notebook, which introduces a practical Ml/AI use case.
Experienced developers wishing to dive deeper into the technical aspects of ensuring equity in AI can start with the Pre-Development resource, then move to Model-Development and then Post-Development.
Non-technical team members and leaders who want more context and information on equity considerations can visit our Website (https://www.paritii.com/equity-in-ai-guide).