Allison Koenecke

About Me

I'm an Assistant Professor of Information Science at Cornell University (and a field faculty member of the Computer Science department). My research interests lie broadly at the intersection of economics and computer science, focusing on algorithmic fairness. My projects apply computational methods, such as machine learning and causal inference, to study societal inequities in domains including online services and public health. I am regularly quoted as an expert on disparities in automated speech-to-text systems.

Previously, I was a postdoc at Microsoft Research New England in the Machine Learning and Statistics group. Before that, I received my PhD from Stanford's Institute for Computational & Mathematical Engineering under the guidance of my reading committee: Susan Athey, Sharad Goel, and Hal Varian. Awards won include the NSF Graduate Research Fellowship and Forbes 30 Under 30 in Science.

You can reach me at: koenecke at cornell.edu

CV

Curriculum Vitae (updated Winter 2023)

At Cornell, I teach a range of Information Science classes, including Introduction to Data Science (Fall '22, Fall '23), Data Science for Global Development (Spring '23), and Designing Fair Algorithms (Spring '24).

I spent several summers during my PhD interning as a data scientist at Facebook, Google, and Microsoft. Much of my recent research focuses on fairness in algorithmic systems developed by big tech companies (such as speech-to-text, online ad targeting services, recommendation systems, and A/B experimentation pipelines). During my PhD, I also co-founded Women in Math, Stats, and Computational Engineering (WiMSCE).

Prior to beginning my graduate studies, I received my Bachelor's in Mathematics with Computer Science from MIT, and then worked at NERA Economic Consulting in New York (leading teams of research analysts in the M&A and antitrust litigation space).

Media

News Coverage 🗞️

Fairness Research: New York Times (1), New York Times (2), Scientific American, Ars Technica, Stanford News, Stanford Engineering Magazine, PNAS QnA, Reuters, IEEE, Business Insider, The Verge, Wired, Inverse, Consumer Reports, Venture Beat, The National, Mother Jones, Law360, Financial Times P&C Specialist, Modern Farmer, Cornell News (1), Cornell News (2)
Public Health Research: New York Times (3), Forbes, WebMD, Howard Hughes Medical Institute, Science Daily
Women in STEM: Man-Made by Tracey Spicer (Book Interview), Venture Beat, Stanford News (1), Stanford News (2), Algorithmic Justice League, MIT News, Women in Data Science

Podcast Appearances 🎧

Research

Selected Publications

(more on Google Scholar)

Careless Whisper: Speech-to-Text Hallucination Harms (FAccT, 2024)
Allison Koenecke, Anna Seo Gyeong Choi, Katelyn Mei, Hilke Schellmann, Mona Sloane
Mitigating Allocative Tradeoffs and Harms in an Environmental Justice Data Tool (Nature Machine Intelligence, 2024)
Benjamin Q. Huynh, Elizabeth T. Chin, Allison Koenecke, Derek Ouyang, Daniel E. Ho, Mathew V. Kiang, and David H. Rehkopf

Should I Stop or Should I Go: Early Stopping of Randomized Experiments on Heterogeneous Populations (NeurIPS, 2023)

Hammaad Adam, Fan Yin, Neil Tenenholtz, Lorin Crawford, Lester Mackey, and Allison Koenecke
→ Presented at CODE 2022, IC2S2 2023, INFORMS 2023, NeurIPS 2023 (Spotlight Paper), CODE 2023
→ Twitter thread here

Federated Causal Inference in Heterogeneous Observational Data (Statistics in Medicine, 2023)

Ruoxuan Xiong, Allison Koenecke, Michael Powell, Zhu Shen, Joshua T. Vogelstein, and Susan Athey
→ Recorded talk: MSR Summit ; Twitter thread here, Lay abstract here

Augmented Datasheets for Speech Datasets and Ethical Decision-Making (FAccT, 2023)

Orestis Papakyriakopoulos, Anna Seo Gyeong Choi, William Thong, Dora Zhao, Jerone Andrews, Rebecca Bourke, Alice Xiang, and Allison Koenecke
→ Twitter thread here

Auditing Cross-Cultural Consistency of Human-Annotated Labels for Recommendation Systems (FAccT, 2023)

Rock Yuren Pang, Jack Cenatempo, Franklyn Graham, Bridgette Kuehn, Maddy Whisenant, Portia Botchway, Katie Stone Perez, and Allison Koenecke
→ Twitter thread here

Allison Koenecke, Eric Giannella, Robb Willer, and Sharad Goel
→ Plenary talk at IC2S2 2023 ; Twitter thread here

Trucks Don't Mean Trump: Diagnosing Human Error in Image Analysis (FAccT, 2022)

JD Zamfirescu-Pereira, Jerry Chen, Emily Wen, Allison Koenecke, Nikhil Garg, Emma Pierson
→ Presented at NeurIPS WWHMD 2021 ; Twitter thread here

Alpha-1 Adrenergic Receptor Antagonists to Prevent Hyperinflammation and Death from Lower Respiratory Tract Infection (eLife, 2021)

Allison Koenecke, Michael Powell, Ruoxuan Xiong, Zhu Shen, Nicole Fischer, Sakibul Huq, Adham M. Khalafallah, Marco Trevisan, Pär Sparen, Juan J Carrero, Akihiko Nishimura, Brian Caffo, Elizabeth A. Stuart, Renyuan Bai, Verena Staedtke, Nickolas Papadopoulos, Kenneth W. Kinzler, Bert Vogelstein, Shibin Zhou, Chetan Bettegowda, Maximilian F. Konig, Brett Mensh, Joshua T. Vogelstein, and Susan Athey

Ten Rules for Conducting Retrospective Pharmacoepidemiological Analyses: Example COVID-19 Study (Frontiers in Pharmacology, 2021)

Michael Powell, Allison Koenecke, James Byrd, Akihiko Nishimura, Maximilian Konig, Ruoxuan Xiong, Sadiqa Mahmood, Vera Mucaj, Chetan Bettegowda, Liam Rose, Suzanne Tamang, Adam Sacarny, Brian Caffo, Susan Athey, Elizabeth Stuart, and Joshua Vogelstein
→ Recorded talk: Toronto Workshop on Reproducibility 2022

The Association Between Alpha-1 Adrenergic Receptor Antagonists and In-Hospital Mortality from COVID-19 (Frontiers in Medicine, 2021)

Liam Rose, Laura Graham, Allison Koenecke, Michael Powell, Ruoxuan Xiong, Zhu Shen, Kenneth W. Kinzler, Chetan Bettegowda, Bert Vogelstein, Maximilian F. Konig, Susan Athey, Joshua T. Vogelstein, and Todd H. Wagner

Racial Disparities in Automated Speech Recognition (Proceedings of the National Academy of Sciences, 2020)

Allison Koenecke, Andrew Nam, Emily Lake, Joe Nudell, Minnie Quartey, Zion Mengesha, Connor Toups, John Rickford, Dan Jurafsky, and Sharad Goel
→ Project Website and Explainer Video; Twitter thread here
→ Recorded talks: Stanford ICME, URI, Stanford Engineering, CodeX, IC2S2, and ICME Xpo

Learning Twitter User Sentiments on Climate Change with Limited Labeled Data (ICWSM, SocialSens Workshop, 2020)

Allison Koenecke and Jordi Feliu-Fabà

Curriculum Learning in Deep Neural Networks for Financial Forecasting (ECML-PKDD, MIDAS Workshop, 2019)

Allison Koenecke and Amita Gajewar
→ Best Paper Award, Mining Data for Financial Applications 2019
→ Presented at JSM 2022

A Game Theoretic Setting of Capitation Versus Fee-For-Service Payment Systems (PLOS ONE, 2019)