Understanding human decision-making is a key focus of behavioral economics, psychology, and neuroscience with far-reaching applications, from public policy to industry. Recently, advances in machine learning have resulted in better predictive models of human decisions and even enabled new theories of decision-making. On the other hand, machine learning systems are increasingly being used to make decisions that affect people, including hiring, resource allocation, and paroles. These lines of work are deeply interconnected: learning what people value is crucial both to predict their own decisions and to make good decisions for them. In this workshop, we will bring together experts from the wide array of disciplines concerned with human and machine decisions to exchange ideas around three main focus areas: (1) using theories of decision-making to improve machine learning models, (2) using machine learning to inform theories of decision-making, and (3) improving the interaction between people and decision-making AIs.
| Opening remarks | |
| Modeling Human Decision-Making: Never Ending Learning (Keynote) | |
| Evaluating and Improving Economic Models (Keynote) | |
| Break | |
| Integrating Explanation and Prediction in Computational Social Science (Keynote) | |
| Panel I: Human decisions (Panel, moderated by Annie Liang) | |
| Break | |
| New Perspectives on Habit Formation from Machine Learning and Neuroeconomics (Keynote) | |
| Keynote speakers Q&A (Panel) | |
| The Effect of an Algorithmic Tool on Child Welfare Decision Making: A Preliminary Evaluation (Contributed talk) | |
| Choices and Rankings with Irrelevant Alternatives (Keynote) | |
| Break | |
| Panel II: Machine decisions (Panel, moderated by Brian Christian) | |
| Bayesian Persuasion for Algorithmic Recourse (Contributed talk) | |
| Policing, Pain, and Politics: Diagnosing Human Bias and Error with Machine Learning (Keynote) | |
| Designing Defaults for School Choice (Contributed talk) | |
| Closing remarks | |
| Poster session I (Poster session) | |
| Poster session II (Poster session) | |
| Bayesian Persuasion for Algorithmic Recourse (Poster) | |
| Designing Defaults for School Choice (Poster) | |
| Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding (Poster) | |
| Catastrophe, Compounding & Consistency in Choice (Poster) | |
| Improving Human Decision-Making with Machine Learning (Poster) | |
| Explainable Patterns for Distinction and Prediction of Moral Judgement on Reddit (Poster) | |
| Assigning Credit to Human Decisions using Modern Hopfield Networks (Poster) | |
| Integrating Machine Learning and a Cognitive Modeling of Decision Making (Poster) | |
| Nearest-neighbor is more useful than feature attribution in improving human accuracy on image classification (Poster) | |
| Deep Gaussian Processes for Preference Learning (Poster) | |
| Representational Denoising to Improve Medical Image Decision Making (Poster) | |
| Trucks Don’t Mean Trump: Diagnosing Human Error in Image Analysis (Poster) | |
| Extrapolation Frameworks in Cognitive Psychology Suitable for Study of Image Classification Models (Poster) | |
| Improving Learning-to-Defer Algorithms Through Fine-Tuning (Poster) | |
| Semiparametric approaches for decision making in high-dimensional sensory discrimination tasks (Poster) | |
| Artificial Intelligence, Ethics, and Intergenerational Responsibility (Poster) | |
| Probabilistic Performance Metric Elicitation (Poster) | |
| Excited and aroused: The predictive importance of simple choice process metrics (Poster) | |
| In silico manipulation of human cortical computation underlying goal-directed learning (Poster) | |
| Will We Trust What We Don’t Understand? Impact of Model Interpretability and Outcome Feedback on Trust in AI (Poster) | |
| Leveraging Information about Background Music in Human-Robot Interaction (Poster) | |
| On the Value of ML Models (Poster) | |
| The Effect of an Algorithmic Tool on Child Welfare Decision Making: A Preliminary Evaluation (Poster) | |