Human-AI Co-Learning
Human-AI Interaction vs Human-AI Co-Learning
![](https://github.com/janetyc/literature-human-ai-collaboration/raw/master/figures/human-ai-co-learning.png)
Three Key Concepts of Human-AI Co-Learning
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Mutual Understanding:
Human and AI are developing common knowledge (i.e., a shared mental model) through an iterative, interactive process.
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Mutual Benefits:
Human and AI as a team achieves superior results that a single human or AI cannot achieve alone.
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Mutual Growth:
Human and AI both have a growth mindset —i.e. they learn together, learn from each other, learn with each other, and grow and evolve over time.
AI sucess vs AI failure
![](https://github.com/janetyc/literature-human-ai-collaboration/raw/master/figures/sucess-failure.png)
Human-AI Collaboration
Literature for Human-AI collaboration, Hybrid Intelligence, Human-AI interaction
Human-Centered Artificial Intelligence
Interactive Machine Learning
- Power to the People: The Role of Humans in Interactive Machine Learning (AI Magine 2014)
Hybrid Intelligence
Collective Intelligence / Crowdsourcing
Machine Teaching
Agency vs Automation
![](https://github.com/janetyc/literature-human-ai-collaboration/raw/master/figures/control-automation.png)
- Man-Computer Symbiosis (Licklider, 1960)
- Direct Manipulation vs. Interface Agents (1997)
- Principles of Mixed-Initiative User Interfaces (Horvitz, 1999)
- Agency plus automation: Designing artificial intelligence into interactive systems (Heer, 2019)
Human-AI Interaction
AI/ML as UX
Intelligibility, Explainability, Explainable AI
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How Good is 85%? A Survey Tool to Connect Classifier Evaluation to Acceptability of Accuracy (CHI 2015)
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"Why Should I Trust You?": Explaining the Predictions of Any Classifier (KDD 2016)
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Towards A Rigorous Science of Interpretable Machine Learning (2017)
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Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda (CHI 2018)
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Designing Theory-Driven User-Centric Explainable AI (CHI 2019)
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Explaining Decision-Making Algorithms through UI (CHI 2019)
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The Challenge of Crafting Intelligible Intelligence (Weld and Bansal, CACM 2019)
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Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning (Kaur et al., CHI 2020)
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Questioning the AI: Informing Design Practices for Explainable AI User Experiences (Liao et al., CHI 2020)
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No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML (Smith-Renner et al., CHI 2020)
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Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems. (Buçinca et al., IUI 2020)
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(Tutorial Slides)"Introduction to Interpretable Machine Learning", Deep Learning Summer school at University of Toronto, Vector institute in 2018, by Been Kim
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(Tutorial Slides)Interpretable Machine Learning: The fuss, the concrete and the questions, Harvard university
Tutorial by Finale Doshi-Velez and Been Kim, ICML 2017
FATE: Fairness, Accountability, Transparency, and Ethics in AI
Human-AI Team
Human-in-the-loop vs Machine-in-the-loop (or AI-in-the-loop)
![](https://github.com/janetyc/literature-human-ai-collaboration/raw/master/figures/human-centered-AI.png)
Human-in-the-loop around AI
Machine-in-the-loop around Humans
- [Creative writing with a machine in the loop: Case studies on slogans and stories](IUI 2018)
Applications for Human-AI Co-Learning
Healthcare
Writing
Creativity (Drawing, Music, etc)
Design
Conversational Agents
Teaching & Learning
Others
Courses
- Human-AI Interaction, by Chinmay Kulkarni an Mary Beth Kery, CMU (former class by Jeff Bigham and Joseph Seering, Fall 2018)
- CS 889: Human-AI Interaction by Edith Law, U.Waterloo
- CS 294: Fairness in Machine Learning by ,UC Bekeley
- CS598RK: HCI for Machine Learning, by Ranjitha Kumar and Jinda Han,UIUC
- Human-centered Machine Learning (2018 Spring), by Chenhao Tan, University of Colorado Boulder
- CS6724: Advanced Topics in Human-Computer Interaction: Human-AI Interaction, by Kurt Lurther, Virginia Tech
- Designing AI to Cultivate Human Well-Being, by Jennifer Aaker and Fei-Fei Li, Stanford
- CS279R: Research Topics in HCI: Human-AI Interaction
- CS492F: Human-AI Interaction, by Jean Young Song and Juho Kim, KAIST
- CS279R Research Topics in HCI: Human-AI Interaction, by Elena L. Glassman, Harvard
Labs
Workshops & Tutorials
Others Links
Other Awesome HAI list
For artist