Explainable AI: From Black Box to Clarity Using Interactive Dashboards

Author(s): Veritas AI

Originally published on Towards AI.

This member-only story is on us. Upgrade to access all of Medium.

AI models, initially designed to solve big and complex problems such as financial market forecasts or disease diagnosis, have thus proven their efficiency. Nevertheless, their functioning still relies on the “black box” principle; this turns the interaction for users into an inability to explain how and why a particular model decided so. Any lack of transparency ensues with issues of trust, and ethical concerns when trust should not be questioned in areas such as health, finance, or criminal justice.

This is the problem that XAI solves in this direction: techniques and methods to make humans understand and have trust in the results generated by AI models. The ultimate goal of transparency of AI systems can be achieved by giving explanations of how models work. Dashboards have become a common intervention in efforts aimed at improving the explainability of AI models. AI behavior can be visualized and interactively monitored through dashboards on a near real-time basis. This article explores the power of the dashboard in enhancing the interpretability and usability of AI models for technical and non-technical audiences alike.

While this indeed points to tremendous promise in the ability of AI to… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI