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1
Intro
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Rons background
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Overview
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Imagenet
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Google Search
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Diabetic Retinopathy
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Google Assistant
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Google Cloud
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Problems with AI
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disproportionate performance
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Compass
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Microsoft Twitter
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Filter Bubbles
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Amazon Search Algorithm
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Principles
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Digital Wellbeing
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Machine Learning AI
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Data Cards
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Model Cards
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Model Interpretation
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Local Interpretation
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Shapley Values
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Integrated gradients
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Gradients
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Examples
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Visualization Tools
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Multiple metrics
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Secondary models
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Over optimizing
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Case study fairness
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How does prediction change
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How effective is this system
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What if we generalize
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Collecting more data
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Changing the loss function
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The net effect
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Lessons
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AI Guidebook
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AI Ethics Guidelines
Description:
Explore responsible AI practices for engineers in this GOTO Copenhagen 2019 conference talk. Delve into the challenges and opportunities presented by AI advancements, including bias, adversarial attacks, and unintended societal impacts. Learn about Google's approach to responsible AI through principles, governance, and design practices. Examine techniques, research findings, and open challenges in areas such as unintended consequences, model understanding, secondary metrics, and engineering objectives. Gain insights from a case study demonstrating how these considerations come together in practice. Discover emerging approaches for aligning machine learning with human values and understand why responsibility is crucial in meeting stakeholder expectations. Benefit from the speaker's expertise as Technical Director for Applied Artificial Intelligence at Google Cloud, covering topics like ImageNet, Google Search, Diabetic Retinopathy, and Google Assistant. Explore concepts such as disproportionate performance, filter bubbles, data cards, model interpretation, and visualization tools. Gain practical knowledge on addressing fairness in AI systems, including strategies for data collection, loss function adjustments, and overall system effectiveness evaluation. Read more

Responsible AI for Engineers

GOTO Conferences
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