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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 data-­driven 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 intend to produce a report that incorporates the outcomes of the workshop.

Full agenda and registration at IMSI.


Add To Calendar 06/06/2022 09:00 AM 06/08/2022 04:15 PM Data Value: Assessment and Evolution Workshop Rubenstein Forum false