About

Brent Hecht is an Associate Professor of Computer Science and Communication Studies at Northwestern University. He holds a Ph.D. in computer science from Northwestern University, a Master's degree in geography from UC Santa Barbara, and dual Bachelor's degrees in computer science and geography from Macalester College. His research lies at the intersection of human–computer interaction, social computing, and spatial computing, with active areas including location-aware technologies and understanding and improving the interactions between algorithms and society. Dr. Hecht employs mixed methods approaches, emphasizing 'big data' projects, and has collaborated with prominent research institutions such as Google Research, Xerox PARC, and Microsoft Research. His work has been featured by various media outlets including NPR, Time Magazine, The Washington Post, and others. He has received awards for his research at top-tier venues like ACM CHI, ACM CSCW, ACM Mobile HCI, and COSIT, and has been a keynote speaker at events such as WikiSym.

Research topics

• Computer Science
• World Wide Web
• Psychology
• Internet privacy
• Information Retrieval
• Social Science
• Engineering
• Political Science
• Sociology
• Programming language
• Communication
• Software engineering
• Mathematics
• Medicine
• Social psychology
• Knowledge management
• Applied psychology
• Business
• Data science
• Economics
• Microeconomics

Selected publications

• Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models

  2024-01-01 · 11 citations

  articleOpen access

  Ying-Chun Lin, Jennifer Neville, Jack Stokes, Longqi Yang, Tara Safavi, Mengting Wan, Scott Counts, Siddharth Suri, Reid Andersen, Xiaofeng Xu, Deepak Gupta, Sujay Kumar Jauhar, Xia Song, Georg Buscher, Saurabh Tiwary, Brent Hecht, Jaime Teevan. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

  PublisherOA PDFDOI

• A Canary in the AI Coal Mine: American Jews May Be Disproportionately Harmed by Intellectual Property Dispossession in Large Language Model Training

  2024-05-11 · 4 citations

  preprintOpen access

  Systemic property dispossession from minority groups has often been carried out in the name of technological progress. In this paper, we identify evidence that the current paradigm of large language models (LLMs) likely continues this long history. Examining common LLM training datasets, we find that a disproportionate amount of content authored by Jewish Americans is used for training without their consent. The degree of over-representation ranges from around 2x to around 6.5x. Given that LLMs may substitute for the paid labor of those who produced their training data, they have the potential to cause even more substantial and disproportionate economic harm to Jewish Americans in the coming years. This paper focuses on Jewish Americans as a case study, but it is probable that other minority communities (e.g., Asian Americans, Hindu Americans) may be similarly affected and, most importantly, the results should likely be interpreted as a “canary in the coal mine” that highlights deep structural concerns about the current LLM paradigm whose harms could soon affect nearly everyone. We discuss the implications of these results for the policymakers thinking about how to regulate LLMs as well as for those in the AI field who are working to advance LLMs. Our findings stress the importance of working together towards alternative LLM paradigms that avoid both disparate impacts and widespread societal harms.

  PublisherOA PDFDOI

• Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models

  arXiv (Cornell University) · 2024-03-19

  preprintOpen access

  Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems. Users express their satisfaction or dissatisfaction with diverse conversational patterns in both general-purpose (ChatGPT and Bing Copilot) and task-oriented (customer service chatbot) conversational systems. Existing approaches based on featurized ML models or text embeddings fall short in extracting generalizable patterns and are hard to interpret. In this work, we show that LLMs can extract interpretable signals of user satisfaction from their natural language utterances more effectively than embedding-based approaches. Moreover, an LLM can be tailored for USE via an iterative prompting framework using supervision from labeled examples. The resulting method, Supervised Prompting for User satisfaction Rubrics (SPUR), not only has higher accuracy but is more interpretable as it scores user satisfaction via learned rubrics with a detailed breakdown.

  PublisherOA PDFDOI

• Targeted Training for Multi-organization Recommendation

  ACM Transactions on Recommender Systems · 2023-06-03

  article

  Making recommendations for users in diverse organizations ( orgs ) is a challenging task for workplace social platforms such as Microsoft Teams and Slack. The current industry-standard model training approaches either use data from all organizations to maximize information or train organization-specific models to minimize noise. Our real-world experiments show that both approaches are poorly suited for the multi-org recommendation setting where different organizations’ interaction patterns vary in their generalizability. We introduce targeted training , which improves on standard practices by automatically selecting a subset of orgs for model development whose data are cleanest and best represent global trends. We demonstrate how and when targeted training improves over global training through theoretical analysis and simulation. Our experiments on large-scale datasets from Microsoft Teams, SharePoint, Stack Exchange, DBLP, and Reddit show that in many cases targeted training can improve mean average precision (MAP) across orgs by 10–15% over global training, is more robust to orgs with lower data quality, and generalizes better to unseen orgs. Our training framework is applicable to a wide range of inductive recommendation models, from simple regression models to graph neural networks (GNNs).

  PublisherDOI

• Climate mitigation potentials of teleworking are sensitive to changes in lifestyle and workplace rather than ICT usage

  Proceedings of the National Academy of Sciences · 2023-09-18 · 33 citations

  articleOpen access

  The growth in remote and hybrid work catalyzed by the COVID-19 pandemic could have significant environmental implications. We assess the greenhouse gas emissions of this transition, considering factors including information and communication technology, commuting, noncommute travel, and office and residential energy use. We find that, in the United States, switching from working onsite to working from home can reduce up to 58% of work's carbon footprint, and the impacts of IT usage are negligible, while office energy use and noncommute travel impacts are important. Our study also suggests that achieving the environmental benefits of remote work requires proper setup of people's lifestyle, including their vehicle choice, travel behavior, and the configuration of home and work environment.

  PublisherOA PDFDOI

• Causal Effect Estimation under Interference on Hypergraphs

  AI Matters · 2023-06-01

  article

  Hypergraphs offer a powerful abstraction for representing multi-way group interactions, allowing hyperedges to connect any number of nodes. In contrast to prevailing approaches that focus on capturing statistical dependencies, our research explores hypergraphs from a causal perspective. Specifically, we tackle the problem of estimating individual treatment effects (ITE) on hypergraphs, aiming to determine the causal impact of interventions (e.g., wearing face covering) on outcomes (e.g., COVID-19 infection) for each individual node. Existing ITE estimation methods either assume no interference between individuals or consider interference only among connected individuals in regular graphs. However, such assumptions may not hold in real-world hypergraphs. Recognizing this, we propose a novel causality learning framework HyperSCI by modeling high-order interference on hyper-graphs. Through extensive experiments on real-world hypergraphs, we validate the effectiveness of HyperSCI and highlight the potential of causal inference in hypergraphs with complex group interactions. 1

  PublisherDOI

• The Dimensions of Data Labor: A Road Map for Researchers, Activists, and Policymakers to Empower Data Producers

  2023-06-12 · 30 citations

  articleOpen accessSenior author

  Many recent technological advances (e.g. ChatGPT and search engines) are possible only because of massive amounts of user-generated data produced through user interactions with computing systems or scraped from the web (e.g. behavior logs, user-generated content, and artwork). However, data producers have little say in what data is captured, how it is used, or who it benefits. Organizations with the ability to access and process this data, e.g. OpenAI and Google, possess immense power in shaping the technology landscape. By synthesizing related literature that reconceptualizes the production of data for computing as “data labor”, we outline opportunities for researchers, policymakers, and activists to empower data producers in their relationship with tech companies, e.g advocating for transparency about data reuse, creating feedback channels between data producers and companies, and potentially developing mechanisms to share data’s revenue more broadly. In doing so, we characterize data labor with six important dimensions - legibility, end-use awareness, collaboration requirement, openness, replaceability, and livelihood overlap - based on the parallels between data labor and various other types of labor in the computing literature.

  PublisherOA PDFDOI

• Learning Causal Effects on Hypergraphs (Extended Abstract)

  2023-08-01 · 1 citations

  articleOpen access

  Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes. Different from most existing studies which leverage statistical dependencies, we study hypergraphs from the perspective of causality. Specifically, we focus on the problem of individual treatment effect (ITE) estimation on hypergraphs, aiming to estimate how much an intervention (e.g., wearing face covering) would causally affect an outcome (e.g., COVID-19 infection) of each individual node. Existing works on ITE estimation either assume that the outcome of one individual should not be influenced by the treatment of other individuals (i.e., no interference), or assume the interference only exists between connected individuals in an ordinary graph. We argue that these assumptions can be unrealistic on real-world hypergraphs, where higher-order interference can affect the ITE estimations due to group interactions. We investigate high-order interference modeling, and propose a new causality learning framework powered by hypergraph neural networks. Extensive experiments on real-world hypergraphs verify the superiority of our framework over existing baselines.

  PublisherOA PDFDOI

• Measuring the Monetary Value of Online Volunteer Work

  Proceedings of the International AAAI Conference on Web and Social Media · 2022-05-31 · 30 citations

  articleOpen access

  Online volunteers are a crucial labor force that keeps many for-profit systems afloat (e.g. social media platforms and online review sites). Despite their substantial role in upholding highly valuable technological systems, online volunteers have no way of knowing the value of their work. This paper uses content moderation as a case study and measures its monetary value to make apparent volunteer labor’s value. Using a novel dataset of private logs generated by moderators, we use linear mixed-effect regression and estimate that Reddit moderators worked a minimum of 466 hours per day in 2020. These hours are worth 3.4 million USD based on the median hourly wage for comparable content moderation services in the U.S. We discuss how this information may inform pathways to alleviate the one-sided relationship between technology companies and online volunteers.

  PublisherOA PDFDOI

• Measuring the Monetary Value of Online Volunteer Work

  arXiv (Cornell University) · 2022-05-28

  preprintOpen access

  Online volunteers are a crucial labor force that keeps many for-profit systems afloat (e.g. social media platforms and online review sites). Despite their substantial role in upholding highly valuable technological systems, online volunteers have no way of knowing the value of their work. This paper uses content moderation as a case study and measures its monetary value to make apparent volunteer labor's value. Using a novel dataset of private logs generated by moderators, we use linear mixed-effect regression and estimate that Reddit moderators worked a minimum of 466 hours per day in 2020. These hours amount to 3.4 million USD a year based on the median hourly wage for comparable content moderation services in the U.S. We discuss how this information may inform pathways to alleviate the one-sided relationship between technology companies and online volunteers.

  PublisherOA PDFDOI

Recent grants

• CAREER: Understanding and Addressing Geographic Inequalities in Location-Aware Technologies

  NSF · $87k · 2016–2016

• III: Small: Collaborative Research: Automatically Generating Contextually-Relevant Visualizations

  NSF · $121k · 2014–2017

• CHS: Small: Collaborative Research: Human-Centered Semantic Relatedness

  NSF · $248k · 2015–2016

• CHS: Small: Collaborative Research: Human-Centered Semantic Relatedness

  NSF · $248k · 2016–2019

• III: Small: Collaborative Research: Automatically Generating Contextually-Relevant Visualizations

  NSF · $93k · 2016–2018

Frequent coauthors

• Johannes Schöning

  University of St. Gallen

  40 shared

• Nicholas Vincent

  University of East Anglia

  20 shared

• Isaac Johnson

  Wikimedia Foundation

  19 shared

• Loren Terveen

  University of Minnesota

  19 shared

• Jacob Thebault-Spieker

  University of Wisconsin–Madison

  18 shared

• Darren Gergle

  Northwestern University

  15 shared

• Jaime Teevan

  15 shared

• Hanlin Li

  The University of Texas at Austin

  12 shared

Awards & honors

• awards for his research at top-tier publication venues in hu…