Course Outline

This is a course outline for Spring 2021.

Week 1. Introduction

• No readings assigned

Week 2. Human experiences with AI in platforms, workplaces, and cities

• I always assumed that I wasn’t really that close to her. Reasoning about Invisible Algorithms in News Feeds
• Working with machines: The impact of algorithmic and data-driven management on human workers
• AI Now Report 2019
• (Optional) Discrimination through Optimization: How Facebook’s ad delivery can lead to biased outcomes.

Week 3. Designing AI user experiences

• Guidelines for Human-AI interaction
• People + AI Guidebook

Week 4. Transparent and explainable AI

• How the machine ‘thinks’: Understanding opacity in machine learning algorithms
• ‘It’s Reducing a Human Being to a Percentage’ Perceptions of justice in algorithmic decisions
• An evaluation of the human-interpretability of explanation
• Explore Google What-If Tool
• Explore IBM AI Explainability 360
• (Optional) Why should i trust you?” Explaining the predictions of any classifier
• (Optional) Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability

Week 5. Fair and responsible AI

• Improving fairness in machine learning systems: What do industry practitioners need?
• Procedural justice in algorithmic fairness: Leveraging transparency and outcome control for fair algorithmic mediation
• A survey on bias and fairness in machine learning
• Explore IBM AI Fairness 360 Demo
• (Optional) Gender shades: Intersectional accuracy disparities in commercial gender classification

Week 6. Designing AI with stakeholders

• Toward situated interventions for algorithmic equity: lessons from the field
• WeBuildAI: Participatory framework for algorithmic governance
• (Optional) Value-sensitive algorithm design: Method, case study, and lessons

Week 7. Human-in-the-loop systems and human-AI collaboration

• Disparate interactions: An algorithm-in-the-loop analysis of fairness in risk assessments
• Human-centered tools for coping with imperfect algorithms during medical decision-making
• Does the Whole Exceed its Parts? The effect of AI explanations on Complementary Team Performance

Week 8. Spring Break

• No readings assigned

Week 9. Designing for failure

• Will you accept an imperfect AI? exploring designs for adjusting end-user expectations of AI systems
• Ambiguity-aware AI Assistants for Medical Data Analysis

Week 10. Data ethics and transparency

• Big data ethics
• Annotation and Benchmarking on understanding and transparency of Machine learning Lifecycles

Week 11. Embodied AI: Robots and self-driving cars

• Expressing thought: improving robot readability with animation principles
• Ghost driver: A field study investigating the interaction between pedestrians and driverless vehicles

Week 12. NLP-based AI: Chatbots and voice agents

• Voice interfaces in everyday life
• Calendar. help: Designing a workflow-based scheduling agent with humans in the loop

Week 13. Work session

• Work session for term project

Week 14. Work session

• Work session for term project

Week 15. Final presentation