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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 13: AAAI-21 Technical Tracks 13

Fair Influence Maximization: a Welfare Optimization Approach

February 1, 2023

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Authors

Aida Rahmattalabi

University of Southern California


Shahin Jabbari

Harvard University


Himabindu Lakkaraju

Harvard University


Phebe Vayanos

University of Southern California


Max Izenberg

Pardee RAND Graduate School


Ryan Brown

RAND Corporation


Eric Rice

University of Southern California


Milind Tambe

Harvard University


DOI:

10.1609/aaai.v35i13.17383


Abstract:

Several behavioral, social, and public health interventions, such as suicide/HIV prevention or community preparedness against natural disasters, leverage social network information to maximize outreach. Algorithmic influence maximization techniques have been proposed to aid with the choice of ``peer leaders'' or ``influencers'' in such interventions. Yet, traditional algorithms for influence maximization have not been designed with these interventions in mind. As a result, they may disproportionately exclude minority communities from the benefits of the intervention. This has motivated research on fair influence maximization. Existing techniques come with two major drawbacks. First, they require committing to a single fairness measure. Second, these measures are typically imposed as strict constraints leading to undesirable properties such as wastage of resources. To address these shortcomings, we provide a principled characterization of the properties that a fair influence maximization algorithm should satisfy. In particular, we propose a framework based on social welfare theory, wherein the cardinal utilities derived by each community are aggregated using the isoelastic social welfare functions. Under this framework, the trade-off between fairness and efficiency can be controlled by a single inequality aversion design parameter. We then show under what circumstances our proposed principles can be satisfied by a welfare function. The resulting optimization problem is monotone and submodular and can be solved efficiently with optimality guarantees. Our framework encompasses as special cases leximin and proportional fairness. Extensive experiments on synthetic and real world datasets including a case study on landslide risk management demonstrate the efficacy of the proposed framework.

Topics: AAAI

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HOW TO CITE:

Aida Rahmattalabi||Shahin Jabbari||Himabindu Lakkaraju||Phebe Vayanos||Max Izenberg||Ryan Brown||Eric Rice||Milind Tambe Fair Influence Maximization: a Welfare Optimization Approach Proceedings of the AAAI Conference on Artificial Intelligence (2021) 11630-11638.

Aida Rahmattalabi||Shahin Jabbari||Himabindu Lakkaraju||Phebe Vayanos||Max Izenberg||Ryan Brown||Eric Rice||Milind Tambe Fair Influence Maximization: a Welfare Optimization Approach AAAI 2021, 11630-11638.

Aida Rahmattalabi||Shahin Jabbari||Himabindu Lakkaraju||Phebe Vayanos||Max Izenberg||Ryan Brown||Eric Rice||Milind Tambe (2021). Fair Influence Maximization: a Welfare Optimization Approach. Proceedings of the AAAI Conference on Artificial Intelligence, 11630-11638.

Aida Rahmattalabi||Shahin Jabbari||Himabindu Lakkaraju||Phebe Vayanos||Max Izenberg||Ryan Brown||Eric Rice||Milind Tambe. Fair Influence Maximization: a Welfare Optimization Approach. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.11630-11638.

Aida Rahmattalabi||Shahin Jabbari||Himabindu Lakkaraju||Phebe Vayanos||Max Izenberg||Ryan Brown||Eric Rice||Milind Tambe. 2021. Fair Influence Maximization: a Welfare Optimization Approach. "Proceedings of the AAAI Conference on Artificial Intelligence". 11630-11638.

Aida Rahmattalabi||Shahin Jabbari||Himabindu Lakkaraju||Phebe Vayanos||Max Izenberg||Ryan Brown||Eric Rice||Milind Tambe. (2021) "Fair Influence Maximization: a Welfare Optimization Approach", Proceedings of the AAAI Conference on Artificial Intelligence, p.11630-11638

Aida Rahmattalabi||Shahin Jabbari||Himabindu Lakkaraju||Phebe Vayanos||Max Izenberg||Ryan Brown||Eric Rice||Milind Tambe, "Fair Influence Maximization: a Welfare Optimization Approach", AAAI, p.11630-11638, 2021.

Aida Rahmattalabi||Shahin Jabbari||Himabindu Lakkaraju||Phebe Vayanos||Max Izenberg||Ryan Brown||Eric Rice||Milind Tambe. "Fair Influence Maximization: a Welfare Optimization Approach". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.11630-11638.

Aida Rahmattalabi||Shahin Jabbari||Himabindu Lakkaraju||Phebe Vayanos||Max Izenberg||Ryan Brown||Eric Rice||Milind Tambe. "Fair Influence Maximization: a Welfare Optimization Approach". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 11630-11638.

Aida Rahmattalabi||Shahin Jabbari||Himabindu Lakkaraju||Phebe Vayanos||Max Izenberg||Ryan Brown||Eric Rice||Milind Tambe. Fair Influence Maximization: a Welfare Optimization Approach. AAAI[Internet]. 2021[cited 2023]; 11630-11638.


ISSN: 2374-3468


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