Demystifying AI: Removing barriers and biases

There is no denying that a successful AI model can result in many benefits, ranging from revenue growth to better decision-making and improved customer experience.

The challenge is not understanding the benefits or seeing key outputs. The real issue is model explainability.

Stakeholders understand the inputs feeding an AI model and can see first-hand the outputs produced, but the uncertainty of what happens in between creates a level of risk that leaders want to avoid.

As individuals come in and out of the organization and more data is collected, model explainability is essential for current and future success.

Model explainability is not only necessary for interpreting output data or debugging systems, but it also plays an important role in uncovering biases.

At Kin + Carta, we believe that transparency enables explainability, explainability enables ethical recommendations and ethical recommendations enable sustainability. This interactive discussion will focus on the characteristics of AI systems that achieve all of these goals, and deliver outsized results.