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Data and data­-driven artificial intelligence are impacting virtually every aspect of society including commerce, science, medicine, government, finance and education. Data drives value in all these various domains, but we know surprisingly little about how value accrues to data as it progresses through its lifecycle — collection, wrangling and integration, modeling and analysis, decision making, and curation.

Data value arises through its ability to foster new products and business models and to enable new discoveries across science, engineering and humanities. However, as a society we are also starting to become aware of the many harms data can bring and their associated risks. When used to inform decisions that affect individuals, for example, data can perpetuate or emphasize existing biases. Combining data from disparate sources can enable previously unseen insights but can also expose private information, either intentionally or unintentionally. Untrustworthy data can also wreak havoc on society, negatively impacting individual lives or putting democracy at risk.

Thus, a challenge we face today is to design systems and data­driven organizations that maximize data’s positive impact while minimizing the negative effects. But we cannot do this without understanding how data contributes to both good and bad outcomes: the crux of the problem is to understand what is the value of data and how does that value change over time, through various processing steps, and when being used in changing contexts.

The aim of this 3­-day workshop is to explore these questions about data value and to discuss approaches to answering them. We will approach the questions from different angles: economic theory, statistics, data semantics, privacy, data markets and software platforms, to name a few. We will incorporate perspectives from experts in these different disciplines and will include both presentations from academics who have worked in these areas and from practitioners who face these questions in their day­-to-­day activities. The topics of the workshop include:

  • Data valuation methods
  • Data markets and sharing
  • Data ownership
  • Data integration and migration
  • Privacy and risks
  • Trustworthiness, reputation, disinformation
  • Information Economics


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Raul Castro Fernandez Computer Science
University of Chicago
Michael Franklin Computer Science
University of Chicago