AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates

Abstract

Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow. Commonly known as AutoML or AutoAI, these technologies aim to relieve data scientists from the tedious manual work. However, today's AutoAI systems often present only limited to no information about the process of how they select and generate model results. Thus, users often do not understand the process, neither do they trust the outputs. In this short paper, we provide a first user evaluation by 10 data scientists of an experimental system, AutoAIViz, that aims to visualize AutoAI's model generation process. We find that the proposed system helps users to complete the data science tasks, and increases their understanding, toward the goal of increasing trust in the AutoAI system.

arXiv comment5 pages, 1 figure, IUI2020

References in corpus (10)

• Model Cards for Model Reporting
• Questioning the AI: Informing Design Practices for Explainable AI User Experiences
• Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making
• Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
• Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects
• How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?
• ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
• One button machine for automating feature engineering in relational databases
• Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
• An ADMM Based Framework for AutoML Pipeline Configuration

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• AutoML in The Wild: Obstacles, Workarounds, and Expectations
• VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization
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• Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML
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• Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling
• Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments
• AutoDOViz: Human-Centered Automation for Decision Optimization
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• PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines
• Assessment of cognitive characteristics in intelligent systems and predictive ability
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