CRAFT session

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Workshop Description

Our FAccT 2021 CRAFT session, "Narratives and Counternarratives on Data Practices in the Global South," is an interactive workshop that uses storytelling as a method to question common assumptions around data practices in countries and communities that are often grouped as "the Global South." Building on our current project around data sharing in Africa, -- and our own autoethnographic observations in other countries -- we will provide various theme-based stories that are informed by common narratives around data practices and invite participants to challenge these narratives from various angles including historical contexts and cultures, legal limitations, accessibility, impact assessments, accountability mechanism, and more.

Our post-conference plan is to create "Narratives and Counternarratives on Data Practices in the Global South" story cards. Our hope is that these cards then will be used by data science educators, civil society groups, philanthropic groups, inter-governmental organizations such as the UN agencies to expose them to narratives and counternarratives of data practices in the Global South and inform them about challenges before they embark on any new initiatives or partnerships.

The CRAFT Workshop is taking place on March 5th, 6-7:30 PM (UTC). Registration information can be found on the ACM FAccT website.

Organized by: Rediet Abebe, Abeba Birhane, George Obaido, Roya Pakzad.

Data Externalies Workshop

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Workshop Description

Externalities shape the data economy as we experience it. Besides uneven market shares, power asymmetries, and high levels of data sharing, little to no reimbursement to data subjects for their contributions are characteristics of this new market. Data externalities, recently theorized in Microeconomics, offer an explanation for the absence of significant reimbursements for data. In this tutorial, we will introduce models in which data externalities arise. Through a series of case studies, we will expose crucial aspects of the contracting environment that aggravate data externalities and allow participants to develop potential interventions. We aim to both translate insights from Microeconomics for the FAccT community and highlight opportunities for further research directions at the interface of market design, fairness, and accountability.

Structure

• Introduction
• Breakout Room Discussions
• Network Externalities vs. Data Externalities
• Data Co-op
• Data Governance and AI
• Conclusion

Slides

• Main Presentation
• Discussion Group 1: Making Algorithms Forget
• Discussion Group 2: Complementary and Substitutable Data
• Discussion Group 3: Collective Bargaining over Data

The Data Externalities Workshop is taking place on March 4th, 4-5:30 PM (UTC). Registration information can be found on the ACM FAccT website.

Organized by: Rediet Abebe, Charles Cui, Mihaela Curmei, Andreas Haupt, Yixin Wang.

References

• Daron Acemoglu, Ali Makhdoumi, Azarakhsh Malekian, and Asuman Ozdaglar. 2019. Too Much Data: Prices and Inefficiencies in Data Markets. https://doi.org/ 10.1017/CBO9781107415324.004
• Dirk Bergemann and Alessandro Bonatti. 2019. The Economics of Social Data: An Introduction. SSRN Electronic Journal (2019). https://doi.org/10.2139/ssrn.3459793
• Theo Bertram, Elie Bursztein, Stephanie Caro, Hubert Chao, Rutledge Feman, P. Fleischer, Albin Gustafsson, Jess Hemerly, Chris Hibbert, L. Invernizzi, Lanah Kammourieh Donnelly, Jason Ketover, Jay Laefer, P. Nicholas, Y. Niu, H. Obhi, D. Price, A. Strait, Kurt Thomas, and A. Verney. 2018. Three years of the Right to be Forgotten.
• Alessandro Blasimme, Effy Vayena, and Ernst Hafen. 2018. Democratizing health research through data cooperatives. Philosophy & Technology 31, 3 (2018), 473– 479.
• Yinzhi Cao and Junfeng Yang. 2015. Towards making systems forget with machine unlearning. In 2015 IEEE Symposium on Security and Privacy. IEEE, 463–480.
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• Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou. 2019. Making AI forget you: Data deletion in machine learning. In Advances in Neural Information Processing Systems. 3518–3531.
• Aditya Golatkar, Alessandro Achille, and Stefano Soatto. 2020. Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations. arXiv preprint arXiv:2003.02960 (2020).
• Ernst Hafen, D Kossmann, and A Brand. 2014. Health data cooperatives-citizen empowerment. Methods Inf Med 53, 2 (2014), 82–86.
• Thomas Hardjono and Alex Pentland. 2019. Data cooperatives: Towards a foundation for decentralized personal data management. arXiv preprint arXiv:1905.08819 (2019).
• Thomas Hardjono and Alex Pentland. 2019. Empowering Artists, Songwriters & Musicians in a Data Cooperative through Blockchains and Smart Contracts. arXiv preprint arXiv:1911.10433 (2019).
• Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou. 2020. Approximate Data Deletion from Machine Learning Models: Algorithms and Evaluations. arXiv preprint arXiv:2002.10077 (2020).
• Yang Liu, Zhuo Ma, Ximeng Liu, and Jianfeng Ma. 2020. Learn to Forget: User-Level Memorization Elimination in Federated Learning. arXiv preprint arXiv:2003.10933 (2020).
• Joan Rodon Mòdol. 2019. Citizens’ Cooperation in the Reuse of Their Personal Data: The Case of Data Cooperatives in Healthcare. In Collaboration in the Digital Age. Springer, 159–185.
• A Pentland, T Hardjono, J Penn, C Colclough, B Ducharmee, and L Mandel. 2019. Data Cooperatives: Digital Empowerment of Citizens and Workers.
• Ankit Singh Tanwar, Nikolaos Evangelatos, Julien Venne, Lesley Ann Ogilvie, Kapaettu Satyamoorthy, and Angela Brand. 2020. Global Open Health Data Cooperatives Cloud in an Era of COVID-19 and Planetary Health. OMICS: A Journal of Integrative Biology (2020).
• Yinjun Wu, Edgar Dobriban, and Susan B. Davidson. 2020. DeltaGrad: Rapid retraining of machine learning models. arXiv:2006.14755 [cs.LG]
• Minhui Xue, Gabriel Magno, Evandro Cunha, Virgilio Almeida, and Keith W Ross. 2016. The right to be forgotten in the media: A data-driven study. Proceedings on Privacy Enhancing Technologies 2016, 4 (2016), 389–402.
• Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. 2016. Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530 (2016).

Speakers and Organizers

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