Call for Participation

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The 4th Workshop on Mechanism Design for Social Good (MD4SG ’20) will take place virtually on 17-19 August 2020.

The goal of the workshop is to highlight work where techniques from algorithms, optimization, and mechanism design, along with insights from other disciplines, have the potential to improve access to opportunity for historically underserved and marginalized communities. The workshop will feature keynote presentations, contributed talks, and a virtual poster session on work at this research interface. The event aims to facilitate interactions between academia, policy, and industry with a focus on bridging research and policy; to this end, there will also be presentations of problem pitches and demonstrations as well as networking and discussion opportunities.

We solicit submissions of research papers, position and policy papers, as well as special problem- and practice-driven submissions, to be presented at the workshop. The deadline for submissions is Wednesday, July 1.

We encourage submissions from across various disciplines covering domains including bias and discrimination, civic participation, computational sustainability, developing nations, economic inequality, education, healthcare, housing, labor markets, and privacy and security. Submissions can be research papers introducing new theory or applications. We also strongly encourage submissions of position papers synthesizing existing work and perspectives or highlighting future directions, as well as problem pitches and demonstration submissions that are particularly aligned with policy or practice. Submissions will fall into one of four tracks:

• AI and Machine Learning including bias, fairness, and ethics, fair division and resource allocation, human computer interaction in socio-technical systems; multi-agent systems, privacy and security, computational social choice, and statistical reasoning.
• Empirical Studies and Policy including applied machine learning, causal inference, computational social science, empirical analysis of real-world systems, empirical methods, experimental results, and papers on policy interventions informed by empirical methods, as well as empirical validation of policy interventions.
• Theory including algorithm design, fairness and resource allocation, game theory, market and mechanism design, optimization, operations management, social choice theory, social network analysis, and theory of machine learning.

The last track is for special submissions such as problem pitches or demonstrations. We especially encourage individuals from government and policy, as well as individuals in non-government organizations and industry, to submit to this track.

• Problems and Demonstrations:
  • Problem Pitches including white papers on problems arising in practice that deserve wider academic attention, and papers pitching methods for addressing real-world problems through research.
  • Demonstrations including prototyped and/or deployed software systems and mobile platforms.

For all submissions, topics of interest include but are not limited to:

• market design challenges in resource-constrained settings
• measuring and evaluating progress to achieve sustainable development goals
• reducing inefficiencies in smallholder farms and under-resourced supply chains
• design of algorithms that mitigate bias and improve diversity
• allocating health insurance funds and managing access to healthcare
• equitable provision of scarce healthcare resources
• ethics of using solutions informed by algorithm and mechanism design in public sector settings
• market regulations for data and privacy
• algorithmic proposals to encourage civic participation
• evaluating fairness in electoral representation
• mechanisms and policy-based interventions with the aim of reducing systemic socioeconomic inequality
• mitigating unequal economic outcomes in on- and off-line labor markets
• detecting existence or causes of exploitative market behavior in on- and off-line labor markets
• allocating low-income housing assistance
• evaluating students, teachers, or schools and improving allocation of educational resources
• design of public and shared transportation systems
• design of flexible and equitable supply chains for food allocation

Submission Specifications:

• Research Papers: Submissions should make theoretical and/or empirical contributions to previously studied problems or propose new ones motivated by societal applications.
• Position Papers: Submissions should propose open problems or explore understudied perspectives in the intersection of mechanism design, algorithms, optimization and fields such law, policy and philosophy.
• Problem Pitches: Submissions should provide
  1. background information on the problems,
  2. examples of how techniques for resource allocation, definition of appropriate metrics and objectives, design of systems, alignment of incentives, and related issues could significantly alleviate these problems, and
  3. summary of any existing methods or approaches for addressing these problems. Submissions are also encouraged to provide a discussion of challenges in providing holistic solutions and/or introduce new methods for addressing the problems.
• Demonstrations: Demo submissions should be accompanied by a short description describing the system or platform, the problems it seeks to address, and the potential to use the tool in conjunction with algorithm and mechanism design and related tools to improve access to opportunity. Submissions should also include instructions for using the system or platform.

Submissions will be evaluated on the following criteria:

• Research and Position Papers:
  • Quality of submission as measured by accuracy and clarity of exposition.
  • Relevance to MD4SG and the workshop theme of bridging research and policy.
  • Novelty of domain: we particularly encourage work on applications that have been less explored within the fields of algorithms, optimization and mechanism design.
  • Significant positive consideration will be given to submissions with:
    • Potential for policy-oriented follow-up work. We welcome submissions that learn from or inform public policy;
    • Potential for creating interdisciplinary collaborations. We welcome submissions with the potential to spark collaborations across different fields;
    • Presentation of domain-specific knowledge. We especially welcome practitioners with interest or experience in translating between policy issues and academic research approaches
• Problem Pitches and Demonstrations:
  • Submissions will be evaluated based on their contributions to one or more of the following:
    • Novelty of problem/domain to the MD4SG community and the broader fields of computer science, economics and operations research.
    • Comprehensive exposition of background on problem and attempted solutions.
    • Potential for future collaborations and/or follow up from the MD4SG community and policymakers.
    • Discussion of practical, structural and/or societal challenges in attempted or proposed solutions.

 

Submission Instructions:

Authors should upload a PDF of their paper to EasyChair. There are no specific formatting instructions or length requirements. In addition to the PDF, authors are asked to upload a separate abstract and a 200-250 word description onto EasyChair summarizing their submission and its relevance to the workshop’s theme. Authors do not need to be the first author of the submitted work. Authors should list all co-authors on the presented work both in the PDF of the submission as well as on EasyChair.

Authors may submit papers that are working papers, papers that have already been published, or are under review. If the work is already published, please include a citation on EasyChair.

There will be no published proceedings. All submissions will be peer-reviewed by at least 2 reviewers. The committee reserves the right not to review all the technical details of submissions. Submissions are single-blind (i.e., authors should include their name and affiliation in the paper).
 

Important Information:

 

• Paper Submission Deadline: July 1 at 3 PM ET
• Paper Submission Page: EasyChair
• Notification: July 30
• Workshop Date: August 17-19

 

Organizing Committee:

 

Program Chairs:

• Francisco J. Marmolejo Cossio, University of Oxford
• Faidra Monachou, Stanford University

Steering Committee:

• Rediet Abebe, Harvard University
• Kira Goldner, Columbia University
• Jon Kleinberg, Cornell University
• Illenin Kondo, University of Notre-Dame
• Sera Linardi, University of Pittsburgh
• Irene Lo, Stanford University
• Ana-Andreea Stoica, Columbia University

Area Chairs

• Theory: Daniel Freund, MIT Sloan; Sam Taggart, Oberlin College; Matt Weinberg, Princeton University
• Empirical Studies and Policy: Zoe B. Cullen, Harvard Business School; Robert Manduca, University of Michigan
• AI and ML: Dina Machuve, Nelson Mandela African Institution of Science and Technology; Ana-Andreea Stoica, Columbia University; Bryan Wilder, Harvard University
• Problems and Demos: Araba Sey, United Nations University; Abhishek Gupta, Microsoft and Montreal AI Ethics Institute

Program Committee

• Itai Ashlagi, Stanford University
• Beatriz Ahumada, University of Pittsburgh
• Hamsa Bastani, Wharton School - University of Pennsylvania
• Michael Carlos Best, Columbia University
• Michael Best, Georgia Institute of Technology
• Elettra Bietti, Harvard Law School
• Abeba Birhane, University College Dublin
• Peter Blair, Harvard University
• Mark Braverman, Princeton University
• Sydnee Caldwell, UC Berkeley
• Augustin Chaintreau, Columbia University
• Jose R. Correa, Universidad de Chile
• Lenore Cowen, Tufts University
• Krishna Dasaratha, Harvard University
• Bikramaditya Datta, Indian Institute of Technology, Kanpur
• Jonathan Davis, University of Oregon
• Maria De-Arteaga, University of Texas at Austin
• Joann de Zegher, MIT Sloan
• John P. Dickerson, University of Maryland
• Edith Elkind, University of Oxford
• Meryem Essaidi, Princeton University
• Elizabeth Evans, Cayena Capital Management, LLC
• Elena Falcettoni, Federal Reserve Board of Governors
• Jessie Finnochiaro, University of Colorado Boulder
• Felix Fischer, Queen Mary University of London
• Rupert Freeman, Microsoft Research
• Jiarui Gan, University of Oxford
• Nikhil Garg, Stanford University
• Joel Goh, NUS Business School
• Michelle Gonzalez Amador, United Nations University
• Abhishek Gupta, Microsoft and Montreal AI Ethics Institute
• Wade Hann-Caruthers, Caltech
• Yoan Hermstrüwer, Max Planck Institute for Research on Collective Goods
• Marc Juarez, University of Southern California
• Anson Kahng, Carnegie Mellon University
• Adam Kapor, Princeton University
• Maximilian Kasy, University of Oxford
• Matthew Kenney, Duke University
• Robizon Khubulashvili, Pennsylvania State University
• Lynn Kirabo, Carnegie Mellon University
• Sara Kingsley, Carnegie Mellon University
• Karen Levy, Cornell University
• Wanyi Li, Stanford University
• Edwin Lock, University of Oxford
• Vahideh Manshadi, Yale University
• Nicholas Mattei, Tulane University
• Duncan McElfresh, University of Maryland
• Jasmin McNealy, University of Florida
• Teddy Mekonnen, Brown University
• Eric Mibuari, Harvard University
• Ken Moon, University of Pennsylvania - Wharton
• Nyalleng Moorosi, Google AI South Africa
• Zanele Munyikwa, MIT Sloan
• George Obaido, University of the Witwatersrand, Johannesburg
• Chinasa Okolo, Cornell University
• Roya Pakzad, Taraaz
• Bobby Pakzad-Hurson, Brown University
• Lucy Qin, Brown University
• Manish Raghavan, Cornell University
• Evan Riehl, Cornell University
• Sarah Riley, Cornell University
• David Robinson, Upturn
• Daniela Saban, Stanford University
• Zhaowei She, Georgia Institute of Technology
• Eric Sodomka, Facebook Research
• Nicolas Stier, Facebook Research
• Inbal Talgam-Cohen, Technion - Israel Institute of Technology
• Alex Teytelboym, University of Oxford
• Neil Thakral, Brown University
• Winnie van Dijk, University of Chicago
• Daniel Waldinger, New York University
• Yixin Wang, Columbia University
• Anne Washington, New York University
• Lily Xu, Harvard University
• Angela Zhou, Cornell University