explainable AI

Explainable AI: The Next Generation of AI

Complex AI systems are inherently black boxes with minimal insight into their operation. Enter Explainable AI. Explainable AI works to make these AI black-boxes more like AI glass-boxes. Businesses are now moving towards an explainability infused approach to decision-making : ‘Decisions with Humans + Data + Explainable AI’.

With explainability understand, analyze, validate, and monitor your AI solutions at scale, continuously.

What is Explainable AI

ML models driving AI systems are invariably black-boxes that can cause significant trust, compliance, ethics and business risks.

Explainable AI helps unlock the AI black-box so humans can understand what’s going on inside the AI solution - thereby enabling Responsible AI. By fostering transparency, Explainable AI allows enterprises, especially  regulated ones, to expand revenue opportunities by deploying AI in previously inaccessible use cases.

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Fiddler Explainable AI
Fiddler Explainable AI

Fiddler Explainable AI Engine

Fiddler infuses explainability into every stage of the AI lifecycle to most effectively surface visibility into how AI operates.

Fiddler’s explainable AI engine is the first to bring the latest research in AI explainability in an enterprise-grade platform to help businesses understand the ‘why’ and ‘how’ behind their AI.

Enterprises can either seamlessly plug-in Fiddler's Explainability solution into their ML platform or use Fiddler for their complete AI explainability needs.

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How is AI Explained

Latest AI research, including contributions from our team, brings explainability methods like Shapley Values and Integrated Gradients to understand ML model predictions. The Fiddler Engine enhances these techniques at scale to enable powerful new tools and use cases with easy interfaces for the entire team.

Hear Fiddler's Head of Data Science, Dr. Ankur Taly, explain how the Shapley Values algorithm works to uncover an AI prediction.

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Explainable AI Podcast

Join Fiddler's Head of Product Marketing, Anusha Sethuraman, as she chats with various industry leaders on Responsible AI and Explainable AI to discuss AI ethics, the need for transparency, how to implement explainable AI, and more.

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Lending prediction risk – Explanation chart

Explainable AI in Loan Underwriting

Deploy efficiently
Deploy efficiently and produce human-readable explanations accessible to users, regulators, and risk managers.

Save money
Reduce bad loans. Save millions of dollars by increasing the accuracy of models using explainability.

Build trust and loyalty
Use explanations to make end-user recommendations on how to change their loan outcomes.

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Explainable AI for Fraud Detection

Build Customer Trust
Reduce false positives, provide customer protection guidelines, and foster customer trust.

Reduce Costs
Resolve alerts faster, reduce costs in manual reviews, and increase robustness of fraud detection models.

Stay Compliant
Reduce the number of suspicious activity reports (SARs) and ensure compliance regulations.

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Lending prediction risk – Explanation chart
Lending prediction risk – Explanation chart

Explainable AI for Churn Detection

Actionable Insights
Get visibility into customer actions and empower customer success teams to solve issues quickly and accurately.

Increase Lifetime Value
With insights on customer actions, drive engagement up and increase revenue with increased loyalty.

Know Your Customers
Understand the 'why' behind customer actions, so you can replicate the ones that work and remove the ones that don't.

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Explainable ML Monitoring

Trust & Visibility
Monitor everything and stay in the know to ensure biases, data issues, and model decays are found and solved fast.

Deploy Successful AI
Real-time monitoring provides visibility, transparency, and insights into the Blackbox to see how models operate and ensure success.

Save Time & Money
Get fast issue identification and resolution with explanations, while ensuring compliance with industry regulations to minimize fines.

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Lending prediction risk – Explanation chart

Explainable AI Resources

Opening the Black Box with Explainable AI | TechCrunch Robotics + AI 2020 with Krishna Gade, CEO, Fiddler Labs
Explainable AI in the Industry: Practical Challenges and Lessons Learned with the Fiddler team

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