Description

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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.

In addition to the talks, we will hold:

  • sessions with contributed talks by students and postdocs, and
  • a panel with companies that are building data markets.

Organizers

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R C F
Raul Castro Fernandez Computer Science
University of Chicago
M F
Michael Franklin Computer Science
University of Chicago
J H
Jason Hartline Northwestern University

Speakers

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D B
Dirk Bergemann Yale University
Y C
Yiling Chen Harvard University
M D
Munther Dahleh MIT
S D
Sylvie Delacroix University of Birmingham
R G
Robert Grossman University of Chicago
N I
Nicole Immorlica Microsoft Research
E K
Emir Kamenica University of Chicago
P K
Paris Koutris University of Wisconsin Madison
D N
Denis Nekipelov University of Virginia
J P
Jian Pei Simon Fraser University
M P
Matt Prewitt RadicalxChange Foundation
J S
Juan Sequeda data.world
H X
Haifeng Xu University of Virginia
C Z
Ce Zhang ETH Zurich
J Z
James Zou Stanford University

Schedule

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Monday, June 6, 2022
9:00-9:45 CDT
Data, Competition, and Digital Platforms

Speaker: Dirk Bergemann (Yale University)

9:45-10:30 CDT
TBA

Speaker: Yiling Chen (Harvard University)

10:30-11:00 CDT
Break
11:00-12:00 CDT
Student/Postdoc Session
  • Yingkai Li (Northwestern University)
  • Omar Montasser (TTIC)
  • Han Shao (TTIC)
  • Yifan Wu (Northwestern University)
12:00-13:00 CDT
Lunch
13:00-13:45 CDT
Information Sale with Externality

Speaker: Munther Dahleh (MIT)

13:45-14:30 CDT
Privacy Guarantees and Inference with an Oblivious Data Curator

Speaker: Denis Nekipelov (University of Virginia)

14:30-15:00 CDT
Break
15:00-15:45 CDT
Quantifying Information and Uncertainty

Speaker: Emir Kamenica (The University of Chicago)

15:45-16:15 CDT
Discussion and Wrap up
Tuesday, June 7, 2022
9:00-9:45 CDT
“Soul-Bound” Tokens, PETs, and More: Speculative Data Governance Structures

Speaker: Matt Prewitt (RadicalXchange)

9:45-10:30 CDT
The inherent instability of top-down valuation methods: bottom-up data trusts and their political, economic and social potential

Speaker: Sylvie Delacroix (University of Birmingham)

10:30-11:00 CDT
Break
11:00-12:00 CDT
Student/Postdoc Session
  • Amir Nouripour (MIT)
  • Nicholas Vincent (Northwestern University)
  • Steven Xia (The University of Chicago)
  • Boxin Zhao (The University of Chicago)
12:00-13:00 CDT
Lunch
13:00-13:45 CDT
Data Sharing starts at home

Speaker: Juan Sequeda (data.world)

13:45-14:30 CDT
Progressions of data Shapley valuation and its applications

Speaker: James Zou (Stanford University)

14:30-15:00 CDT
Break
15:00-16:15 CDT
Industry Panel
  • Dean Allemang (data.world)
  • Jay Bhankharia (Databricks)
  • Nick Jordan (Narrative I/O)
  • James Rhodes (Morningstar)
16:15-16:30 CDT
Discussion and Wrap up
17:00-19:00 CDT
Reception

David Rubenstein Forum, Room 701
1201 E. 60th Street

Wednesday, June 8, 2022
9:00-9:45 CDT
Incentivizing Exploration with Selective Data Disclosure

Speaker: Nicole Immorlica (Microsoft Research)

9:45-10:30 CDT
Optimal Pricing of Information

Speaker: Haifeng Xu (University of Virginia)

10:30-11:00 CDT
Break
11:00-11:45 CDT
Query-based and Model-based Data Pricing

Speaker: Paris Koutris (University of Wisconsin-Madison)

11:45-13:00 CDT
Lunch
13:00-13:45 CDT
Fair and efficient valuation in data marketplaces

Speaker: Jian Pei (Simon Fraser University)

13:45-14:30 CDT
TBA

Speaker: Ce Zhang (ETH Zurich)

14:30-15:00 CDT
Break
15:00-15:45 CDT
The Long Tail of Data Curation and its Impact of the Value of Data

Speaker: Bob Grossman (The University of Chicago)

15:45-16:15 CDT
Discussion and Next Steps