Mechanism Design for Social Good
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Abstract (arXiv)
Across various domains--such as health, education, and housing--improving societal welfare involves allocating resources, setting policies, targeting interventions, and regulating activities. These solutions have an immense impact on the day-to-day lives of individuals, whether in the form of access to quality healthcare, labor market outcomes, or how votes are accounted for in a democratic society. Problems that can have an out-sized impact on individuals whose opportunities have historically been limited often pose conceptual and technical challenges, requiring insights from many disciplines. Conversely, the lack of interdisciplinary approach can leave these urgent needs unaddressed and can even exacerbate underlying socioeconomic inequalities. To realize the opportunities in these domains, we need to correctly set objectives and reason about human behavior and actions. Doing so requires a deep grounding in the field of interest and collaboration with domain experts who understand the societal implications and feasibility of proposed solutions. These insights can play an instrumental role in proposing algorithmically-informed policies. In this article, we describe the Mechanism Design for Social Good (MD4SG) research agenda, which involves using insights from algorithms, optimization, and mechanism design to improve access to opportunity. The MD4SG research community takes an interdisciplinary, multi-stakeholder approach to improve societal welfare. We discuss three exciting research avenues within MD4SG related to improving access to opportunity in the developing world, labor markets and discrimination, and housing. For each of these, we showcase ongoing work, underline new directions, and discuss potential for implementing existing work in practice.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Oct 2018.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 4; 2024: 2; 2025: 1.
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- 2023Harms from Increasingly Agentic Algorithmic Systemscited by 134
- 2023The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practicecited by 86
- 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspectivecited by 33
- 2021Envisioning Communities: A Participatory Approach Towards AI for Social Goodcited by 29
- 2021Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"cited by 26
- 2021Strategic Rankingcited by 21
- 2024Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumventioncited by 21
- 2022Human-centered mechanism design with Democratic AIcited by 3
- 2022HCMD-zero: Learning Value Aligned Mechanisms from Datacited by 2
- 2024Intuitions of Compromise: Utilitarianism vs. Contractualismcited by 1
- 2025Towards Meaningful Transparency in Civic AI Systemscited by 1
- 2022A Non-Expert's Introduction to Data Ethics for Mathematicianscited by 0
1 more
- 2023Strategic Evaluation: Subjects, Evaluators, and Societycited by 0
- 2025Towards Meaningful Transparency in Civic AI Systems9 Oct 2025
- 2024Intuitions of Compromise: Utilitarianism vs. Contractualism7 Oct 2024
- 2024Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention3 May 2024
- 2023Strategic Evaluation: Subjects, Evaluators, and Society5 Oct 2023
- 2023The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice2 Oct 2023
- 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective18 Apr 2023
- 2023Harms from Increasingly Agentic Algorithmic Systems20 Feb 2023
- 2022HCMD-zero: Learning Value Aligned Mechanisms from Data21 Feb 2022
- 2022Human-centered mechanism design with Democratic AI27 Jan 2022
- 2022A Non-Expert's Introduction to Data Ethics for Mathematicians18 Jan 2022
- 2021Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"1 Nov 2021
- 2021Strategic Ranking16 Sep 2021
1 more
- 2023Harms from Increasingly Agentic Algorithmic Systemscited by 134
- 2023Harms from Increasingly Agentic Algorithmic Systems20 Feb 2023
- 2023The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practicecited by 86
- 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspectivecited by 33
- 2021Envisioning Communities: A Participatory Approach Towards AI for Social Goodcited by 29
- 2021Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"cited by 26
- 2021Strategic Rankingcited by 21
- 2024Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumventioncited by 21
- 2022Human-centered mechanism design with Democratic AIcited by 3
- 2022HCMD-zero: Learning Value Aligned Mechanisms from Datacited by 2
- 2024Intuitions of Compromise: Utilitarianism vs. Contractualismcited by 1
- 2025Towards Meaningful Transparency in Civic AI Systemscited by 1
- 2022A Non-Expert's Introduction to Data Ethics for Mathematicianscited by 0
- 2023Strategic Evaluation: Subjects, Evaluators, and Societycited by 0
- 2025Towards Meaningful Transparency in Civic AI Systems9 Oct 2025
- 2024Intuitions of Compromise: Utilitarianism vs. Contractualism7 Oct 2024
- 2024Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention3 May 2024
- 2023Strategic Evaluation: Subjects, Evaluators, and Society5 Oct 2023
- 2023The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice2 Oct 2023
- 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective18 Apr 2023
- 2022HCMD-zero: Learning Value Aligned Mechanisms from Data21 Feb 2022
- 2022Human-centered mechanism design with Democratic AI27 Jan 2022
- 2022A Non-Expert's Introduction to Data Ethics for Mathematicians18 Jan 2022
- 2021Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"1 Nov 2021
- 2021Strategic Ranking16 Sep 2021
- 2021Envisioning Communities: A Participatory Approach Towards AI for Social Good4 May 2021