Last week, AscentCore hosted an absolutely awesome live session to walk through our latest research, lead by Cornel Stefanache (PHD, CTO at AscentCore) and Sara Visovan (AscentCore Labs). If you missed the live presentation, you can check it out here.
You won’t find a better or more accessible play-by-play analysis of how counterfactuals can be used to reduce data bias in your AI models. We share the datasets, models, heuristics, use cases, and results…everything you need to see in order to demonstrate the solution and highlight AscentCore’s industry-leading capability in the field of AI/ML, applied to digital products.
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Our goal for this research was to advance and improve the implementation of AI/ML for a myriad of digital technology applications. To that end, our aim was to improve AI models and remove data bias in order to :
- Apply Machine Learning (ML) to critical business use cases including Medical Diagnostics, Self-driving Cars, Fraud Detection, and a host of Financial Services tasks.
- Contribute to increased user trust in ML services.
- Flesh out the concept of Explainable Artificial Intelligence (XAI).
- Demonstrate the fragility of ML services using XAI
The complete research white paper is also available for download using the link below.
