The challenge of crafting intelligible intelligence

Weld, Daniel S., Bansal, Gagan·2019·Communications of the ACM

Cited by (253)

Items 101–200 of 253

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  13. A Systematic Review on Model-agnostic XAI Libraries
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  20. Assessing deep learning: a work program for the humanities in the age of artificial intelligence
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  26. Semantic Description of Explainable Machine Learning Workflows for Improving Trust
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  32. Making SHAP Rap: Bridging Local and Global Insights Through Interaction and Narratives
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  33. Brain-Computer Interfaces for Human Augmentation
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  35. Responsible Machine Learning for Ethical Artificial Intelligence in Business and Industry
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  40. Designing for Human Data Interaction in Data-Driven Media Experiences
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  41. Understanding Is a Process
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  42. Basic Issues and Challenges on Explainable Artificial Intelligence (XAI) in Healthcare Systems
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  43. Keeping judges in the loop: a human–machine collaboration strategy against the blind spots of AI in criminal justice
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  44. reSHAPe: A Framework for Interactive Explanations in XAI Based on SHAP
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  45. Uncovering Semantic Bias in Neural Network Models Using a Knowledge Graph
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  46. Unifying Principles and Metrics for Safe and Assistive AI
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  47. Explainable Failure Predictions with RNN Classifiers based on Time Series Data
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  48. Efficient Explanations for Knowledge Compilation Languages
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  49. AIMEE: An Exploratory Study of How Rules Support AI Developers to Explain and Edit Models
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  53. EUCA: A Practical Prototyping Framework towards End-User-Centered Explainable Artificial Intelligence.
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  54. Efficient Explanations With Relevant Sets
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  57. Signs of the Time: Making AI Legible
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  58. Explanation-Based Tuning of Opaque Machine Learners with Application to Paper Recommendation
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  59. Next Big Challenges in Core AI Technology
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  63. Exploring the Role of Trust during Human-AI Collaboration in Managerial Decision-Making Processes
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  64. The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPR
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  73. Interpretable Policy Specification and Synthesis through Natural Language and RL
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  74. Review of Stuck Pipe Prediction Methods and Future Directions
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