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Schedule

The course schedule is below. Please be aware that specifics are subject to change. Readings are chosen to be interesting, informative, and, sometimes, controversial. Opinions expressed in the readings may or may not represent the opinions of the instructors.

Table of Contents

Week 1: Introduction to Human-AI Interaction

Week 2: Practical Machine Learning

Week 3: Agents, Direct Manipulation, and Interactive ML

Week 4: Machine Learning + UX

Week 5: Where Does Data Come From?

Week 6: Data, Bias, and Trust

Week 7: AI (and Technology) Ethics

Week 8: Active Learning (Putting Humans In-the-Loop)

Week 9: Content Moderation

Week 10: Facilitating Human-Machine Partnerships

Week 11: Recommender Systems

Week 12: Visualization

Week 13: Explainable AI

Week 14:  ChatBots and Conversational Agents

Week 15:  AI, Humans, and the World

Week 1: Introduction to Human-AI Interaction

Goal:  Overview of the course and motivation for deeply considering humans in AI systems. Why do humans matter in AI? Where do humans fit into AI systems? What are the opportunities for doing better with better consideration of people?

August 27, 2018 (class 0) - Introduction

In Class:

Readings (read before class):
Assignments:

August 29, 2018 (class 1) - Intelligent Augmentation vs. Artificial Intelligence  

In Class:

Readings (read before class):
Assignments:

Week 2: Practical Machine Learning

Goal:  Gain some experience applying machine learning to real problems focusing on classification problems, and understand the supervised machine learning process (data collection, data curation, model building, testing, iteration).

 

September 5, 2018 - Introduction to Machine Learning Classification

Readings:

In-Class Activities

Assignments

Week 3: Agents, Direct Manipulation, and Interactive ML

Goal:  The great interaction debate, including tradeoffs inherent in different approaches, what has changed (or not) since the original debates, and introduction to design patterns that reduce negative impacts of uncertainty in AI systems.

September 10, 2018 - Direct Manipulation vs. Agents

Readings:

        In-Class:

Assignments:

September 12, 2018 - Interactive Machine Learning

        In Class:

Readings:

Week 4: Machine Learning + UX

Goal: Introduction to machine learning, how it surfaces in user interfaces, and how to think about machine learning performance (errors, recall, precision, etc.)

September 17, 2018 - How Does ML Surface in UX?

In Class:
Readings:

September 19, 2018 - Design + ML

In Class:
Readings:
Assignments:

Week 5: Where Does Data Come From?

Goal:  In the real world, data for the problems you care about won’t usually be prepackaged into nice, already-existing datasets -- you’ll have to create new datasets, and most of the data you’ll be interested in comes from people. This week is about where data comes from, and how you can go about finding, collecting, and managing data. We’ll pay special attention to the humans involved.

September 24, 2018 - Crowdsourcing Data Collection

Readings:

September 26, 2018 - Who Is The Crowd?

        In Class:

Readings:

Week 6: Data, Bias, and Trust

Goal:  Despite our best efforts, all datasets have bias. In some especially bad cases, data might be biased against a marginal subpopulation (e.g., racial bias, gender bias, etc.), but sometimes bias can be harder to spot. Once we recognize that our data will have bias, we can work toward datasets that limit undesirable bias and seek to mitigate potential negative effects of remaining bias in the models we create. As we build models with data that make it into user interfaces, we will work toward understanding how people might appropriately decide when and how to trust the underlying models that result.

October 1, 2018 - Data, Bias, and Targeting

Readings:

October 3, 2018 - Trustworthy AI

In Class:

Readings:

Assignments:

Week 7: AI (and Technology) Ethics

Goal:  Ethics are the moral principles that govern our behavior in our lives. The ethics by which we choose to develop technologies (including AI technologies) can determine in very real ways how those technologies will benefit (or not) the humans who provide the data to train models, who use the systems we develop directly, or who may face the consequences of AI systems deployed for them or around them.

October 8, 2018 - Ethics of massive user data

Readings:

October 10, 2018 - Accuracy Problems or Technology Problems?

In Class:

Readings:

Week 8: Active Learning (Putting Humans In-the-Loop)

Goal:  In machine learning classes, we sometimes treat datasets as fixed. In the real world, data continues to grow, and a big challenge is thus how to continue to incorporate new data into models as they grow. This is challenging because we need to have continued access to ground truth labels, don’t want to accidentally make performance worse, and most machine learning algorithms don’t allow new examples to be easily added without retraining the whole model.

October 15, 2018 - Interactive Machine Learning

Readings:

October 17, 2018 - Humans in the Loop

In-Class:
Readings:

        Assignments:

Week 9: Content Moderation

Goal:  The rise of the social web was once heralded as liberating, democratizing force that would only lead to positive changes in the world. In the past few years, we’ve learned that social web technologies are amplifiers, and they can amplify both good and bad aspects of humanity. What is the role of AI in helping users deal with content? In this week, we’ll cover how people are working with AI to moderate content, how content algorithms drive engagement and seek to influence human behavior, and overview technology tasked with identifying hateful, fake, or otherwise harmful online content (and the very real difficulties of figuring out these problems automatically).

October 22, 2018 - The Role of AI in Content Moderation

Readings:

October 24, 2018 - Content Moderation by Amplifying Human Moderators

In-Class:

Readings:

Week 10: Facilitating Human-Machine Partnerships

Goal: Both humans and machines struggle to complete many types of tasks well, and oftentimes they have complementary strengths. Constructing fruitful human-machine partnerships is thus promising in a lot of hard domains, but non-trivial in most of them. Humans can struggle to understand and fix the sometimes nonsensical errors made by AI, and rigid AI systems can struggle to incorporate the decontextualized and slow input of people.

October 29, 2018 - Crowds + Machine Learning + Users

Readings:

October 31, 2018 👻 -- Human Robot Interaction

In Class:

Readings:
Assignments:

Week 11: Recommender Systems

November 5, 2018 - Introduction to Recommender Systems

Readings:

November 7, 2018 - Effects of Recommender Systems on People

In-Class:
Readings:

Week 12:  Visualization

November 12, 2018 - An AI-Centered Introduction to Visualization

Readings:

November 14, 2018 - Visualizing AI to Understand It Better

In-Class:

Readings:

Assignments:

Week 13: Explainable AI

Goal: AI systems are being deployed in increasingly diverse and complicated situations, and the machine learning models underlying these systems are often incredibly difficult to understand, having been trained

November 19, 2018

        In-Class:

Readings:

November 21, 2018 🦃

Week 14:  ChatBots and Conversational Agents

Goal: Chatbots, dialog systems, and conversational agents are starting to be used and interacted with by people at a huge scale. Twitter bots are interacting with unsuspecting people, companies continue to rely on increasingly sophisticated dialog systems to reduce costs, and listening devices are entering our homes at large scale in the form of speech-controlled devices. How are these systems built, what are their limitations, and what might they look like in the future?

November 26, 2018

Readings:

November 28, 2018

In Class:

Readings:
        Assignments:

Week 15:  AI, Humans, and the World

December 3, 2018 - Jobs?

In Class:

Readings:

December 5, 2018 - How to make AI better for humans, and how not to break the world

In Class: