With AI and ML becoming everyday jargons while building business strategies, it is crucial for an organization to have a full understanding of the AI decision-making processes with model monitoring and accountability of AI and not trust them blindly. ML models are often thought of as black boxes that are impossible to interpret and hence lack trust at many levels by business leaders. Bias in the data sets has been a long-standing risk in training AI models. Add to those drifts in model performance, and the trust factor reduces further.
Explainable AI is one of the key requirements for implementing responsible AI, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability, and accountability.In this webinar, our top minds will talk about what Explainable AI is and how organizations can build and deploy models at scale with utmost trust and confidence.
Table of Contents
Topics Covered
- What is explainable AI?
- Why does explainable AI matter?
- Value of explainable AI
- Interpretability vs. explainability
- Considerations for explainable AI
- How to get started with implementing explainable AI?
- Case studies
Speaker Profile
Sayan Goswami is a seasoned management consultant with over seven years of experience in the Big Data and Analytics field. Sayan has a strong background in building Big Data Platforms, Social Media Analyzers, Conversational AI systems, and driving Digital Transformations at scale for many organizations. Adept at analyzing opportunity areas and quickly developing a roadmap, Sayan has helped many Fortune 500 organizations become digitally mature at speed.
To be a part of this upcoming webinar, register here:
North America Region:
- Date | Time: 6th September 2022 | 10:30 AM PST
- Download Now: Click here!
EMEA Region:
- Date | Time: 7th September 2022 | 11:00 AM UTC
- Download Now: Click here!
