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Robert (Munro) Monarch - Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI

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Robert (Munro) Monarch Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI
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Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively.Summary Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the technology Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster.About the bookHuman-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. Youll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. Youll learn to create training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.Whats inside Identifying the right training and evaluation data Finding and managing people to annotate data Selecting annotation quality control strategies Designing interfaces to improve accuracy and efficiencyAbout the authorRobert (Munro) Monarch is a data scientist and engineer who has built machine learning data for companies such as Apple, Amazon, Google, and IBM. He holds a PhD from Stanford.Robert holds a PhD from Stanford focused on Human-in-the-Loop machine learning for healthcare and disaster response, and is a disaster response professional in addition to being a machine learning professional. A worked example throughout this text is classifying disaster-related messages from real disasters that Robert has helped respond to in the past.Table of ContentsPART 1 - FIRST STEPS 1 Introduction to human-in-the-loop machine learning 2 Getting started with human-in-the-loop machine learning PART 2 - ACTIVE LEARNING 3 Uncertainty sampling 4 Diversity sampling 5 Advanced active learning 6 Applying active learning to different machine learning tasks PART 3 - ANNOTATION 7 Working with the people annotating your data 8 Quality control for data annotation 9 Advanced data annotation and augmentation 10 Annotation quality for different machine learning tasks PART 4 - HUMANCOMPUTER INTERACTION FOR MACHINE LEARNING 11 Interfaces for data annotation 12 Human-in-the-loop machine learning products

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Robert (Munro) Monarch - Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI
Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI
Robert (Munro) Monarch
Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI

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inside front cover

Quick reference guide for this book

Understand a models uncertainty

Recruit annotators

Softmax Base/Temperature (3.1.2, A.1-A.2)

In-house experts (7.2)

Least Confidence (3.2.1)

Outsourced workers (7.3)

Margin of Confidence (3.2.2)

Crowdsourced workers (7.4)

Ratio of Confidence (3.2.3)

End users (7.5.1)

Entropy (3.2.4)

Volunteers (7.5.2)

Ensembles of Models (3.4.1)

People playing games (7.5.3)

Query by Committee & Dropouts (3.4.2)

Manage annotation quality

Aleatoric & Epistemic Uncertainty (3.4.3)

Ground truth data (8.1.1)

Active Transfer Learning for Uncertainty (5.2)

Expected accuracy/agreement & adjusting for random chance (8.1.2, A.3.3)

Identify gaps in a models knowledge

Dataset reliability with Krippendorff's alpha (8.2.3)

Model-based Outliers (4.2, 4.6.1)

Individual annotator agreement (8.2.5)

Cluster-based Sampling (4.3, 4.6.2)

Per-label & per-demographic agreement (8.2.6)

Representative Sampling (4.4, 4.6.3)

Extending accuracy with agreement for real-world diversity (8.2.7)

Real-world Diversity (4.5, 4.6.4)

Aggregating annotations (8.3.1-3, 9.2.1-2)

Active Transfer Learning for Representative Sampling (5.3)

Eliciting annotator-reported confidences (8.3.4)

Create a complete active learning strategy

Calculating annotation uncertainty (8.3.5)

Combining Uncertainty Sampling and Diversity Sampling (5.1.1-6)

Quality control by expert review (8.4)

Expected Error Reduction (5.1.8)

Multistep workflows and adjudication/review tasks (8.5)

Active Transfer Learning for Adaptive Sampling (5.4)

Creating models to predict whether a single annotation is:

correct (9.2.3)

in agreement (9.2.4)

from a bot (9.2.5)

Active Learning for already-labeled data (6.6.1)

Data-filtering with rules (9.5.1)

Training data search (9.5.2)

Implement active learning with different machine learning architectures

Trust model predictions as labels (9.3.1)

Logistic Regression & MaxEnt (3.3.1)

Use a model prediction as an annotation (9.3.2)

Support Vector Machines (3.3.2)

Cross-validating to find mislabeled data (9.3.3)

Bayesian Models (3.3.3)

Decision Trees & Random Forests (3.3.4)

Diversity Sampling (4.6.1-4)

Human-in-the-Loop Machine Learning Active learning and annotation for human-centered AI - image 1

Human-in-the-Loop Machine Learning

Active learning and annotation for human-centered AI

Robert (Munro) Monarch

Foreword by Christopher D. Manning

To comment go to liveBook

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Manning Publications Co.

20 Baldwin Road Technical

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ISBN: 9781617296741

front matter
foreword

With machine learning now deployed widely in many industry sectors, artificial intelligence systems are in daily contact with human systems and human beings. Most people have noticed some of the user-facing consequences. Machine learning can either improve peoples lives, such as with the speech recognition and natural language understanding of a helpful voice assistant, or it can annoy or even actively harm humans, with examples ranging from annoyingly lingering product recommendations to rsum review systems that are systematically biased against women or under-represented ethnic groups. Rather than thinking about artificial intelligence operating in isolation, the pressing need this century is for the exploration of human-centered artificial intelligencethat is, building AI technology that effectively cooperates and collaborates with people, and augments their abilities.

This book focuses not on end users but on how people and machine learning come together in the production and running of machine learning systems. It is an open secret of machine learning practitioners in industry that obtaining the right data with the right annotations is many times more valuable than adopting a more advanced machine learning algorithm. The production, selection, and annotation of data is a very human endeavor. Hand-labeling data can be expensive and unreliable, and this book spends much time on this problem. One direction is to reduce the amount of data that needs to be labeled while still allowing the training of high-quality systems through active learning approaches. Another direction is to exploit machine learning and humancomputer interaction techniques to improve the speed and accuracy of human annotation. Things do not stop there: most large, deployed systems also involve various kinds of human review and updating. Again, the machine learning can either be designed to leverage the work of people, or it can be something that humans need to fight against.

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