About

Chara Podimata is the Class of 1942 Career Development Assistant Professor and an Assistant Professor of Operations Research and Statistics at MIT Sloan. Her research interests focus on the social aspects of computing, specifically examining how humans adapt to machine learning algorithms used for consequential decision-making. During her PhD studies, she interned at Microsoft Research and Google, supported by a Microsoft Dissertation Grant and a Siebel Scholarship. She earned her PhD from Harvard University, where she was advised by Yiling Chen, and subsequently was a FODSI postdoctoral fellow at UC Berkeley. Outside of her academic pursuits, she spends her time adventuring with her pup, Terra.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Mathematical economics
• Mathematics
• Mathematical optimization
• Discrete mathematics

Selected publications

• Do LLMs Track Public Opinion? A Multi-Model Study of Favorability Predictions in the 2024 U.S. Presidential Election

  arXiv (Cornell University) · 2026-02-06

  articleOpen accessSenior author

  We investigate whether Large Language Models (LLMs) can track public opinion as measured by exit polls during the 2024 U.S. presidential election cycle. Our analysis focuses on headline favorability (e.g., "Favorable" vs. "Unfavorable") of presidential candidates across multiple LLMs queried daily throughout the election season. Using the publicly available llm-election-data-2024 dataset, we evaluate predictions from nine LLM configurations against a curated set of five high-quality polls from major organizations including Reuters, CNN, Gallup, Quinnipiac, and ABC. We find systematic directional miscalibration. For Kamala Harris, all models overpredict favorability by 10-40% relative to polls. For Donald Trump, biases are smaller (5-10%) and poll-dependent, with substantially lower cross-model variation. These deviations persist under temporal smoothing and are not corrected by internet-augmented retrieval. We conclude that off-the-shelf LLMs do not reliably track polls when queried in a straightforward manner and discuss implications for election forecasting.

  PublisherOA PDF

• Do LLMs Track Public Opinion? A Multi-Model Study of Favorability Predictions in the 2024 U.S. Presidential Election

  Open MIND · 2026-02-06

  preprintSenior author

  We investigate whether Large Language Models (LLMs) can track public opinion as measured by exit polls during the 2024 U.S. presidential election cycle. Our analysis focuses on headline favorability (e.g., "Favorable" vs. "Unfavorable") of presidential candidates across multiple LLMs queried daily throughout the election season. Using the publicly available llm-election-data-2024 dataset, we evaluate predictions from nine LLM configurations against a curated set of five high-quality polls from major organizations including Reuters, CNN, Gallup, Quinnipiac, and ABC. We find systematic directional miscalibration. For Kamala Harris, all models overpredict favorability by 10-40% relative to polls. For Donald Trump, biases are smaller (5-10%) and poll-dependent, with substantially lower cross-model variation. These deviations persist under temporal smoothing and are not corrected by internet-augmented retrieval. We conclude that off-the-shelf LLMs do not reliably track polls when queried in a straightforward manner and discuss implications for election forecasting.

  DOI

• Contextual Dynamic Pricing with Heterogeneous Buyers

  ArXiv.org · 2025-12-10

  preprintOpen access

  We initiate the study of contextual dynamic pricing with a heterogeneous population of buyers, where a seller repeatedly posts prices (over $T$ rounds) that depend on the observable $d$-dimensional context and receives binary purchase feedback. Unlike prior work assuming homogeneous buyer types, in our setting the buyer's valuation type is drawn from an unknown distribution with finite support size $K_{\star}$. We develop a contextual pricing algorithm based on optimistic posterior sampling with regret $\widetilde{O}(K_{\star}\sqrt{dT})$, which we prove to be tight in $d$ and $T$ up to logarithmic terms. Finally, we refine our analysis for the non-contextual pricing case, proposing a variance-aware zooming algorithm that achieves the optimal dependence on $K_{\star}$.

  PublisherOA PDFDOI

• Incentive-Aware Machine Learning; Robustness, Fairness, Improvement & Causality

  ArXiv.org · 2025-05-08

  preprintOpen access1st authorCorresponding

  The article explores the emerging domain of incentive-aware machine learning (ML), which focuses on algorithmic decision-making in contexts where individuals can strategically modify their inputs to influence outcomes. It categorizes the research into three perspectives: robustness, aiming to design models resilient to "gaming"; fairness, analyzing the societal impacts of such systems; and improvement/causality, recognizing situations where strategic actions lead to genuine personal or societal improvement. The paper introduces a unified framework encapsulating models for these perspectives, including offline, online, and causal settings, and highlights key challenges such as differentiating between gaming and improvement and addressing heterogeneity among agents. By synthesizing findings from diverse works, we outline theoretical advancements and practical solutions for robust, fair, and causally-informed incentive-aware ML systems.

  PublisherOA PDFDOI

• Large-Scale, Longitudinal Study of Large Language Models During the 2024 US Election Season

  ArXiv.org · 2025-09-22

  preprintOpen access

  The 2024 US presidential election is the first major contest to occur in the US since the popularization of large language models (LLMs). Building on lessons from earlier shifts in media (most notably social media's well studied role in targeted messaging and political polarization) this moment raises urgent questions about how LLMs may shape the information ecosystem and influence political discourse. While platforms have announced some election safeguards, how well they work in practice remains unclear. Against this backdrop, we conduct a large-scale, longitudinal study of 12 models, queried using a structured survey with over 12,000 questions on a near-daily cadence from July through November 2024. Our design systematically varies content and format, resulting in a rich dataset that enables analyses of the models' behavior over time (e.g., across model updates), sensitivity to steering, responsiveness to instructions, and election-related knowledge and "beliefs." In the latter half of our work, we perform four analyses of the dataset that (i) study the longitudinal variation of model behavior during election season, (ii) illustrate the sensitivity of election-related responses to demographic steering, (iii) interrogate the models' beliefs about candidates' attributes, and (iv) reveal the models' implicit predictions of the election outcome. To facilitate future evaluations of LLMs in electoral contexts, we detail our methodology, from question generation to the querying pipeline and third-party tooling. We also publicly release our dataset at https://huggingface.co/datasets/sarahcen/llm-election-data-2024

  PublisherOA PDFDOI

• Incentivizing Desirable Effort Profiles in Strategic Classification: The Role of Causality and Uncertainty

  ArXiv.org · 2025-02-10

  preprintOpen access

  We study strategic classification in binary decision-making settings where agents can modify their features in order to improve their classification outcomes. Importantly, our work considers the causal structure across different features, acknowledging that effort in a given feature may affect other features. The main goal of our work is to understand \emph{when and how much agent effort is invested towards desirable features}, and how this is influenced by the deployed classifier, the causal structure of the agent's features, their ability to modify them, and the information available to the agent about the classifier and the feature causal graph. In the complete information case, when agents know the classifier and the causal structure of the problem, we derive conditions ensuring that rational agents focus on features favored by the principal. We show that designing classifiers to induce desirable behavior is generally non-convex, though tractable in special cases. We also extend our analysis to settings where agents have incomplete information about the classifier or the causal graph. While optimal effort selection is again a non-convex problem under general uncertainty, we highlight special cases of partial uncertainty where this selection problem becomes tractable. Our results indicate that uncertainty drives agents to favor features with higher expected importance and lower variance, potentially misaligning with principal preferences. Finally, numerical experiments based on a cardiovascular disease risk study illustrate how to incentivize desirable modifications under uncertainty.

  PublisherOA PDFDOI

• Can Users Fix Algorithms? A Game-Theoretic Analysis of Collective Content Amplification in Recommender Systems

  arXiv (Cornell University) · 2025-06-05

  preprintOpen accessSenior author

  Users of social media platforms based on recommendation systems (e.g. TikTok, X, YouTube) strategically interact with platform content to influence future recommendations. On some such platforms, users have been documented to form large-scale grassroots movements encouraging others to purposefully interact with algorithmically suppressed content in order to counteractively ``boost'' its recommendation. However, despite widespread documentation of this phenomenon, there is little theoretical work analyzing its impact on the platform or users themselves. We study a game between users and a RecSys, where users (potentially strategically) interact with the content available to them, and the RecSys -- limited by preference learning ability -- provides each user her approximately most-preferred item. We compare recommendations and social welfare when users interact with content according to their personal interests and when a collective of users intentionally interacts with an otherwise suppressed item. We provide sufficient conditions to ensure a pareto improvement in recommendations and strict increases in user social welfare under collective interaction, and provide a robust algorithm to find an effective collective strategy. Interestingly, despite the intended algorithmic protest of these movements, we show that for commonly assumed recommender utility functions, effective collective strategies also improve the utility of the RecSys. Our theoretical analysis is complemented by empirical results of effective collective interaction strategies on the GoodReads dataset and an online survey on how real-world users attempt to influence others' recommendations on RecSys platforms. Our findings examine how and when platforms' recommendation algorithms may incentivize users to collectivize and interact with content in algorithmic protest as well as what this collectivization means for the platform.

  PublisherOA PDFDOI

• Desirable Effort Fairness and Optimality Trade-offs in Strategic Learning

  ArXiv.org · 2025-10-21

  preprintOpen accessSenior author

  Strategic learning studies how decision rules interact with agents who may strategically change their inputs/features to achieve better outcomes. In standard settings, models assume that the decision-maker's sole scope is to learn a classifier that maximizes an objective (e.g., accuracy) assuming that agents best respond. However, real decision-making systems' goals do not align exclusively with producing good predictions. They may consider the external effects of inducing certain incentives, which translates to the change of certain features being more desirable for the decision maker. Further, the principal may also need to incentivize desirable feature changes fairly across heterogeneous agents. How much does this constrained optimization (i.e., maximize the objective, but restrict agents' incentive disparity) cost the principal? We propose a unified model of principal-agent interaction that captures this trade-off under three additional components: (1) causal dependencies between features, such that changes in one feature affect others; (2) heterogeneous manipulation costs between agents; and (3) peer learning, through which agents infer the principal's rule. We provide theoretical guarantees on the principal's optimality loss constrained to a particular desirability fairness tolerance for multiple broad classes of fairness measures. Finally, through experiments on real datasets, we show the explicit tradeoff between maximizing accuracy and fairness in desirability effort.

  PublisherOA PDFDOI

• When Should you Offer an Upgrade: Online Upgrading Mechanisms for Resource Allocation

  arXiv (Cornell University) · 2024-02-13 · 3 citations

  preprintOpen access

  In this work, we study an upgrading scheme for online resource allocation problems. We work in a sequential setting, where at each round a request for a resource arrives and the decision-maker has to decide whether to accept it (and thus, offer the resource) or reject it. The resources are ordered in terms of their value. If the decision-maker decides to accept the request, they can offer an upgrade-for-a-fee to the next more valuable resource. This fee is dynamically decided based on the currently available resources. After the upgrade-for-a-fee option is presented to the requester, they can either accept it, get upgraded, and pay the additional fee, or reject it and maintain their originally allocated resource. We take the perspective of the decision-maker and wish to design upgrading mechanisms in a way that simultaneously maximizes revenue and minimizes underutilization of resources. Both of these desiderata are encapsulated in a notion of regret that we define, and according to which we measure our algorithms' performance. We present a fast algorithm that achieves O(log T) regret. Finally, we implemented our algorithm utilizing data akin to those observed in the hospitality industry and estimated our upgrading mechanism would increase the annual revenue by over 17%.

  PublisherOA PDFDOI

• Strategyproof Decision-Making in Panel Data Settings and Beyond

  ACM SIGMETRICS Performance Evaluation Review · 2024-06-11

  article

  We consider the problem of decision-making using panel data, in which a decision-maker gets noisy, repeated measurements of multiple units (or agents). We consider the setup used in synthetic control methods, where there is a pre-intervention period when the principal observes the outcomes of each unit, after which the principal uses these observations to assign a treatment to each unit. Unlike this classical setting, we permit the units generating the panel data to be strategic, i.e. units may modify their pre-intervention outcomes in order to receive a more desirable intervention. The principal's goal is to design a strategyproof intervention policy, i.e. a policy that assigns units their utility-maximizing interventions despite their potential strategizing. We first identify a necessary and sufficient condition under which a strategyproof intervention policy exists, and provide a strategyproof mechanism with a simple closed form when one does exist. Along the way, we prove impossibility results for strategic multiclass classification, a natural extension of the well-studied (binary) strategic classification problem to the multiclass setting, which may be of independent interest. When there are two interventions, we establish that there always exists a strategyproof mechanism, and provide an algorithm for learning such a mechanism. For three or more interventions, we provide an algorithm for learning a strategyproof mechanism if there exists a sufficiently large gap in the principal's rewards between different interventions. Finally, we empirically evaluate our model using real-world panel data collected from product sales over 18 months. We find that our methods compare favorably to baselines which do not take strategic interactions into consideration, even in the presence of model misspecification.

  PublisherDOI

Frequent coauthors

• Akshay Krishnamurthy

  7 shared

• Zhiwei Steven Wu

  6 shared

• Thodoris Lykouris

  Massachusetts Institute of Technology

  6 shared

• Robert E. Schapire

  5 shared

• Yiling Chen

  4 shared

• Keegan Harris

  Carnegie Mellon University

  4 shared

• Zhe Feng

  3 shared

• Kyriakos Lotidis

  Stanford University

  3 shared

Labs

• Chara PodimataPI

  Not provided

Education

• PhD, School of Engineering and Applied Sciences

  Harvard University

Awards & honors

• Microsoft Dissertation Grant
• Siebel Scholarship