Schedule
Please, check out this schedule frequently as it will likely change a bit throughout the quarter. In addition, I will post readings for each week the previous week.
| Date | Lecture | Keywords | Readings |
|---|---|---|---|
| 09/30 | The value of data and data markets | introductions, administrivia, grading criteria, what is a data market, what is the value of data, economics of data, privacy, incentives | 1-.Carriere-Swallow, Mr Yan, and Mr Vikram Haksar. The economics and implications of data: an integrated perspective. International Monetary Fund, 2019. 2-. Jeannette Wing. The Data Life Cycle. HDSR, 2019. |
| 10/07 | Data value chains and data as a “first-class citizen” | economic properties of data, privacy, acquisition, preparation, metadata management, data catalogs | 1-. Nithya Sambasivan, Shivani Kapania, Hannah Highfill, Diana Akron, Praveen Paritosh, Lora M Aroyo. “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI. CHI, 2021. 2-. Stephen Stigler. Data Have a Limited Shelf Life. HDSR, 2019. 3-. Christine L. Borgman. The Lives and After Lives of Data. HDSR, 2019. 4-. Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford. Datasheets for Datasets. Arxiv. 2018 |
| 10/14 | Workshop day (optional) | Elicitation mechanisms in practice | IDEAL Workshop: Elicitation mechanisms in practice. Part of the special quarter on Data Economics |
| 10/21 | Value of Information or the Value of Data for Decision Making | Information Structures, Information Economics, Statistical Decision Making | 1-. Gerald Feltham. The Value of Information. The Accounting Review. 1968 2-. Christian Gollier. The Economics of Risk and Time - (Only Chapter 23. The Value of Information). 1999 3-. Patrick Bajari, Victor Chernozhukov, Ali Hortaçsu, Junichi Suzuki. The Impact of Big Data on Firm Performance: An Empirical Investigation AEA 2019 |
| 10/28 | The value of data and its connection to Machine Learning | machine learning, relationship between decision making and prediction, instrumental value of training data, shapley value and revenue allocation, the (new) new data economy | 1-. Eric Breck, Neoklis Polyzotis, Sudip Roy, Steven Euijong Whang, Martin Zinkevich Data Validation for Machine Learning SysML 2019. 2-. Gavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Lujan Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection JMLR 2012 3-. Amirata Ghorbani, James Zou, Data Shapley: Equitable Valuation of Data for Machine Learning ICML 2019 |
| 11/04 | Data Sharing and Organization | data sharing architectures, the value of pooling data, compliance and security, privacy and externalities, data warehousing, lakes, and knowledge graphs, data mesh and other organization methods | 1-. Data Warehouse–Wikipedia 2-. Michael Armbrust, Ali Ghodsi, Reynold Xin, Matei Zaharia Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics CIDR 2021 3-. Zhamak Dehghani How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh |
| 11/11 | Data Markets and the New Data Economy | definition of data market, types and examples of data markets, definition of the data economy, incentive engineering, informing models with data, loop between design and implementation | TBD |
| 11/18 | Theoretical Data Markets, Data Pricing, and other Issues | game theory, mechanism design, data pricing | 1-. Anish Agarwal, Munther Dahleh, Tuhin Sarkar A Marketplace for data: an algorithmic solution EC 2019 2-. Charles Jones, Christopher Tonetti, Nonrivalry and the Economics of Data American Economic Review. September 2020 |
| 11/25 | Thanskgiving | – | |
| 12/02 | Data Governance and the Opportunity of Well-Functioning Data Markets | unions, trusts, cooperations, dividends, data federalism, data-as-labor | 1-. Mozilla Insights, Jonathan van Geuns, Ana Brandusescu What Does it Mean? Shifting Power Through Data Governance Mozilla Foundation September 2020 2-. Data for Empowerment 3-. Sylvie Delacroix, Neil D Lawrence. Bottom-up data Trusts: disturbing the ‘one size fits all’ approach to data governance. International Data Privacy Law, Volume 9, Issue 4, November 2019 4-. TBD |
| 12/09 | Project Presentations. Summary and Wrap up | Students present quarter projects, peer evaluation | – |