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Documents authored by Jagadeesan, Meena

Document
DOI: 10.4230/LIPIcs.FORC.2026.15
Incentivizing High-Quality Content in Online Recommender Systems

Authors: Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, and Jacob Steinhardt

Published in: LIPIcs, Volume 368, 7th Symposium on Foundations of Responsible Computing (FORC 2026)

Abstract
In content recommender systems such as TikTok and YouTube, the platform’s recommendation algorithm shapes content producer incentives. Many platforms employ online learning, which generates intertemporal incentives, since content produced today affects recommendations of future content. We study the game between producers and analyze the content created at equilibrium. We prove that standard online learning algorithms, such as Hedge and EXP3, unfortunately incentivize producers to create low-quality content, where producers' effort approaches zero in the long run for typical learning rate schedules. Motivated by this negative result, we design learning algorithms that incentivize producers to invest high effort and achieve high user welfare. At a conceptual level, our work illustrates the unintended impact that a platform’s learning algorithm can have on content quality and introduces algorithmic approaches to mitigating these effects.
Cite as

Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, and Jacob Steinhardt. Incentivizing High-Quality Content in Online Recommender Systems. In 7th Symposium on Foundations of Responsible Computing (FORC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 368, pp. 15:1-15:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{hu_et_al:LIPIcs.FORC.2026.15,
  author =	{Hu, Xinyan and Jagadeesan, Meena and Jordan, Michael I. and Steinhardt, Jacob},
  title =	{{Incentivizing High-Quality Content in Online Recommender Systems}},
  booktitle =	{7th Symposium on Foundations of Responsible Computing (FORC 2026)},
  pages =	{15:1--15:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-419-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{368},
  editor =	{Lin, Huijia (Rachel)},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2026.15},
  URN =		{urn:nbn:de:0030-drops-259887},
  doi =		{10.4230/LIPIcs.FORC.2026.15},
  annote =	{Keywords: recommender systems, content quality, producer incentives, online learning, algorithmic game theory, Stackelberg games}
}
Document
DOI: 10.4230/LIPIcs.ITCS.2022.42
Individual Fairness in Advertising Auctions Through Inverse Proportionality

Authors: Shuchi Chawla and Meena Jagadeesan

Published in: LIPIcs, Volume 215, 13th Innovations in Theoretical Computer Science Conference (ITCS 2022)

Abstract
Recent empirical work demonstrates that online advertisement can exhibit bias in the delivery of ads across users even when all advertisers bid in a non-discriminatory manner. We study the design ad auctions that, given fair bids, are guaranteed to produce fair outcomes. Following the works of Dwork and Ilvento [2019] and Chawla et al. [2020], our goal is to design a truthful auction that satisfies "individual fairness" in its outcomes: informally speaking, users that are similar to each other should obtain similar allocations of ads. Within this framework we quantify the tradeoff between social welfare maximization and fairness. This work makes two conceptual contributions. First, we express the fairness constraint as a kind of stability condition: any two users that are assigned multiplicatively similar values by all the advertisers must receive additively similar allocations for each advertiser. This value stability constraint is expressed as a function that maps the multiplicative distance between value vectors to the maximum allowable 𝓁_{∞} distance between the corresponding allocations. Standard auctions do not satisfy this kind of value stability. Second, we introduce a new class of allocation algorithms called Inverse Proportional Allocation that achieve a near optimal tradeoff between fairness and social welfare for a broad and expressive class of value stability conditions. These allocation algorithms are truthful and prior-free, and achieve a constant factor approximation to the optimal (unconstrained) social welfare. In particular, the approximation ratio is independent of the number of advertisers in the system. In this respect, these allocation algorithms greatly surpass the guarantees achieved in previous work. We also extend our results to broader notions of fairness that we call subset fairness.
Cite as

Shuchi Chawla and Meena Jagadeesan. Individual Fairness in Advertising Auctions Through Inverse Proportionality. In 13th Innovations in Theoretical Computer Science Conference (ITCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 215, pp. 42:1-42:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{chawla_et_al:LIPIcs.ITCS.2022.42,
  author =	{Chawla, Shuchi and Jagadeesan, Meena},
  title =	{{Individual Fairness in Advertising Auctions Through Inverse Proportionality}},
  booktitle =	{13th Innovations in Theoretical Computer Science Conference (ITCS 2022)},
  pages =	{42:1--42:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-217-4},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{215},
  editor =	{Braverman, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2022.42},
  URN =		{urn:nbn:de:0030-drops-156385},
  doi =		{10.4230/LIPIcs.ITCS.2022.42},
  annote =	{Keywords: Algorithmic fairness, advertising auctions}
}
Document
DOI: 10.4230/LIPIcs.FORC.2020.7
Individual Fairness in Pipelines

Authors: Cynthia Dwork, Christina Ilvento, and Meena Jagadeesan

Published in: LIPIcs, Volume 156, 1st Symposium on Foundations of Responsible Computing (FORC 2020)

Abstract
It is well understood that a system built from individually fair components may not itself be individually fair. In this work, we investigate individual fairness under pipeline composition. Pipelines differ from ordinary sequential or repeated composition in that individuals may drop out at any stage, and classification in subsequent stages may depend on the remaining "cohort" of individuals. As an example, a company might hire a team for a new project and at a later point promote the highest performer on the team. Unlike other repeated classification settings, where the degree of unfairness degrades gracefully over multiple fair steps, the degree of unfairness in pipelines can be arbitrary, even in a pipeline with just two stages. Guided by a panoply of real-world examples, we provide a rigorous framework for evaluating different types of fairness guarantees for pipelines. We show that naïve auditing is unable to uncover systematic unfairness and that, in order to ensure fairness, some form of dependence must exist between the design of algorithms at different stages in the pipeline. Finally, we provide constructions that permit flexibility at later stages, meaning that there is no need to lock in the entire pipeline at the time that the early stage is constructed.
Cite as

Cynthia Dwork, Christina Ilvento, and Meena Jagadeesan. Individual Fairness in Pipelines. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 7:1-7:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{dwork_et_al:LIPIcs.FORC.2020.7,
  author =	{Dwork, Cynthia and Ilvento, Christina and Jagadeesan, Meena},
  title =	{{Individual Fairness in Pipelines}},
  booktitle =	{1st Symposium on Foundations of Responsible Computing (FORC 2020)},
  pages =	{7:1--7:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-142-9},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{156},
  editor =	{Roth, Aaron},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2020.7},
  URN =		{urn:nbn:de:0030-drops-120235},
  doi =		{10.4230/LIPIcs.FORC.2020.7},
  annote =	{Keywords: algorithmic fairness, fairness under composition, pipelines}
}
Document
RANDOM
DOI: 10.4230/LIPIcs.APPROX-RANDOM.2019.61
Simple Analysis of Sparse, Sign-Consistent JL

Authors: Meena Jagadeesan

Published in: LIPIcs, Volume 145, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019)

Abstract
Allen-Zhu, Gelashvili, Micali, and Shavit construct a sparse, sign-consistent Johnson-Lindenstrauss distribution, and prove that this distribution yields an essentially optimal dimension for the correct choice of sparsity. However, their analysis of the upper bound on the dimension and sparsity requires a complicated combinatorial graph-based argument similar to Kane and Nelson’s analysis of sparse JL. We present a simple, combinatorics-free analysis of sparse, sign-consistent JL that yields the same dimension and sparsity upper bounds as the original analysis. Our analysis also yields dimension/sparsity tradeoffs, which were not previously known. As with previous proofs in this area, our analysis is based on applying Markov’s inequality to the pth moment of an error term that can be expressed as a quadratic form of Rademacher variables. Interestingly, we show that, unlike in previous work in the area, the traditionally used Hanson-Wright bound is not strong enough to yield our desired result. Indeed, although the Hanson-Wright bound is known to be optimal for gaussian degree-2 chaos, it was already shown to be suboptimal for Rademachers. Surprisingly, we are able to show a simple moment bound for quadratic forms of Rademachers that is sufficiently tight to achieve our desired result, which given the ubiquity of moment and tail bounds in theoretical computer science, is likely to be of broader interest.
Cite as

Meena Jagadeesan. Simple Analysis of Sparse, Sign-Consistent JL. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 145, pp. 61:1-61:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{jagadeesan:LIPIcs.APPROX-RANDOM.2019.61,
  author =	{Jagadeesan, Meena},
  title =	{{Simple Analysis of Sparse, Sign-Consistent JL}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019)},
  pages =	{61:1--61:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-125-2},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{145},
  editor =	{Achlioptas, Dimitris and V\'{e}gh, L\'{a}szl\'{o} A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2019.61},
  URN =		{urn:nbn:de:0030-drops-112762},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2019.61},
  annote =	{Keywords: Dimensionality reduction, Random projections, Johnson-Lindenstrauss distribution, Hanson-Wright bound, Neuroscience-based constraints}
}
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