This document discusses explainable artificial intelligence and the importance of interpretability for building trust in AI systems. It describes Bleckwen's approach to combining complex machine learning models with local surrogate models and technologies like LIME to extract explanations for decisions in a way that is understandable to humans. The goal is to enable effective collaboration between human and artificial intelligence, with applications in industries like banking to fight financial fraud using interpretable machine learning and behavioral profiling.
11. We believe in a collaboration between
Human and Artificial Intelligence.
Interpretability is the key enabler.
12. Why you should care about
Interpretability?
Image from Drive.ai, a self-driving car service for public use in Frisco, Texas
13. “Interpretability is the degree to which a human can
understand the cause of a decision.”
Miller, Tim. 2017. “Explanation in Artificial Intelligence: Insights from the Social Sciences.”
14. Why do we need interpretability?
STRENGTHEN TRUST AND
TRANSPARENCY
SATISFY REGULATORY
REQUIREMENTS
EXPLAIN DECISIONS
IMPROVE MODELS
Get more information in our blog post
15. But what about the trade-off Accuracy vs. Interpretability?
Image source: https://blog.bigml.com/2018/05/01/prediction-explanation-adding-transparency-to-machine-learning/amp/
19. Explaining the taxonomy of Interpretability
globallocal
Model-
agnostic
Model-specific
Added value for
industries
20. How we are helping the banking industry accelerate
the adoption of AI?
21. Our clients are fighting against financial
fraud with the power of Machine
Learning and the Behavioral Profiling
while keeping decisions
interpretable.
25. We combine best-in-class Machine Learning models with
interpretation technologies to get the best from
a collective artificial and human intelligence
Get more information in our blog post
27. DEEP TRUTH REVEALED
Anti-fraud solution for banks
Thank you for your attention.
www.bleckwen.ai
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Editor's Notes
Use case: google assistant that makes a call a human to book a hair cut booking for a client
Use case: AlphaGo defeated the Maste Go Lee Sedol, 4-1
Use case: Alibaba's smart warehouse where robots do 70% of the work.
They can carry up to 500 kilograms above them around the warehouse floor.
They have special sensors to avoid colliding into each other and they can be summoned using wifi.
Eyes of Watson: This image presents IBM's Eyes of Watson demo for breast cancer detection, which was presented at the 2016 Annual Meeting of the Radiological Society of North America (RSNA). The demo highlights IBM's capabilities in medical imaging with a question-answer format. The Watson-based technology is designed to serve as a cognitive assistant to radiologists in their workflows. Machine learning tools are also used to combine multimodal semantic image descriptions (for mammography, ultrasound and MRI) with clinical data, facilitating estimation of correct differential diagnosis and patient management recommendation.
