Speaker: Rui Vieira
Open source business automation (OSBA) is a useful tool to help orchestrate complex business workflows. But what if you could use artificial intelligence (AI) to help extend those automations even further?
Although AI and machine learning (ML) techniques can also greatly benefit OSBA, fairness and transparency are fundamental requirements when implementing or using AI/ML outcomes.
In this session we will focus on how we implemented different explainability techniques in the TrustyAI project to allow different aspects of opaque predictive models' outcomes to be better understood by both end users and ML practitioners.
We will discuss feature importance estimation using LIME and SHAP and counterfactual explanations and how they can benefit OSBA processes.
The attendees will leave this session familiar with concepts such as why is explainability important, knowledge of the techniques implemented to achieve black-box model explainability and an example of a real-world service oriented OSBA incorporating these techniques.
Sched: [ Ссылка ]
