2nd EcoPaDL at NeurIPS 2022

2nd Workshop on ECOnomics of Privacy and Data Labor

About the workshop

Although data is considered to be the “new oil”, pricing data is an exceptionally difficult problem, and for a variety of reasons. For starters, there are economic and legal challenges to pricing, trading, and assessing data. Raw use of data has been invaluable in several sectors such as advertising, healthcare, etc, but often in violation of people’s privacy, which results in ongoing legal uncertainty and challenges. Labeled data has also been extremely valuable for the training of machine learning models (driverless car industry). The resulting importance of data labeling is evidenced by the growth of annotation companies such as Figure8 and Scale.AI, but this poses additional legal and economic questions. What is the fair value to pay those who annotate such data? What about those whose data is annotated? The common economic model of exchanging data (or attention) for services, sometimes known as surveillance capitalism or the attention economy, also raises questions about whether the services provided to users for “free” in exchange for their data and attention are commensurate with the economic value captured from the personal data involved in such exchanges. Data marketplaces have been proposed and implemented to formally address some of these concerns with new ways of doing business (such as AWS, Snowflake,Narrative.io, nitrogen.ai), but these marketplaces suffer from a lack of good pricing models. They also fail to maintain the right of the data owners to define how their own data will be used. There have been numerous suggestions for sharing data while maintaining privacy, such as data unions, privacy preserving digital twins, and federated learning, but such new proposals create new legal, economic, and technical challenges. Likewise, it is technically challenging to define the value of data. While there are several proposals for solving the technical part of data markets and fair pricing, there is not yet a dominant paradigm for pricing data or a consensus about what the relevant valuation factors are. What’s more, technical methods should be guided by fundamental economics and legal requirements. This workshop aspires to bring together researchers from the fields (and subfields) of

  • Machine learning and computer science
  • Privacy-preserving machine learning methods
  • Federated learning
  • Data sharing infrastructure
  • Economics (and other social sciences)
  • Information economics
  • Data labor
  • Crowdsourcing
  • Pricing of machine learning models
  • Law and policy
  • legal and ethical implications of data trading
  • AI ethics
  • Data and intellectual property

Similar Workshops

Schedule

Time (EST) Title Presenter Slides Video
9:00am to 10:00am Keynote 1 TBD x x
10:00am to 10:45am Contributed Talks TBD x x
10:45am to 11:15am BREAK      
11:15am to 12:15pm Panel (Industry): Urgent and difficult challenges in data marketplaces and data economics TBD x x
12:15pm to 1:15 pm Lunch      
1:15pm to 2:15pm Keynote 2 TBD x x
2:15pm to 3:15pm Brainstorming x x  
3:15pm to 3:30pm BREAK x x  
3:30pm to 4:00pm Invited talk TBD x x
4:00pm to 5:00pm Panel (Academia): Exciting opportunities for research and development in the next 3-5 years. TBD x x
5:00pm to 5:05pm Closing Remarks Organizers Slides Video

Call for papers

We welcome submissions presenting interesting and initial ideas that address fundamental research and technical issues in this challenging area and especially encourage reports on system level research and interdisciplinary practice related to data management and data science in data marketplaces. We also welcome new visions and critical reviews on marketplaces.

TOPICS OF INTEREST Topics of interest include, but not limited to:

  • Data valuation
  • Data and Machine Learning Model pricing
  • Data acquisition
  • Data quality measurement
  • Data utility
  • Arbitrage and prevention
  • Game-theoretic approaches to data markets
  • Privacy issues in data trading
  • System support for data trading
  • Data labor
  • Crowdsourcing
  • Pricing of machine learning models
  • Law and policy

Important Dates

Paper submission deadline: Sep 22, 2022, 11:59 PM (AoE, UTC-12)

Acceptance notification:Oct 20, 2022, AoE

Mandatory SlidesLive upload for speaker videos: Nov 03, 2022, AoE

Workshop: December 2nd, 2022 (CST time zone)

Topics of interest

  • privacy-preserving machine learning methods
  • algorithmic economics
  • federated learning
  • data policies, AI ethics
  • pricing of machine learning models
  • data marketplaces
  • the economics of data labor and crowdsourcing
  • legal and ethical implications of data trading

Submission Guidelines

You are invited to submit papers of up to six pages. You have unlimitted space for references. If you you want to submit a longer paper, we ask that they write a 2-6 page summary and submit it, along with an attachment or link to the full paper. To be considered, papers must be received by the submission deadline (see Important Dates). Submissions must be original work and may be under submission to another venue at the time of review. Authors are encouraged to use the NeurIPS 2022 style guidelines as described here, but they are free to use other formats.

Submission Site

Submission link: TBD

All questions about submissions should be emailed to ecopadl2022@googlegroups.com

Organizers