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Allison Koenecke

@allisonkoe.bsky.social

asst prof @ cornell info sci | fairness in tech, public health & services | alum of MSR, Stanford ICME, NERA Econ, MIT Math | she/her | koenecke.infosci.cornell.edu

32 Posts2635 Follower294 Folgt
Emma Harvey@emmharv.bsky.social · 3mo ago

🎉 "Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems" by Jing Nathan Yan, me, Junxiong Wang, Jeff Rzeszotarski, and @allisonkoe.bsky.social is now available in the #CHI2026 proceedings! 🔗: dl.acm.org/doi/10.1145/...

Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems | Proceedings of the 2026 CHI Conference on Human Factors in Computing Syste...

dl.acm.org

Cornell Tech@cornelltech.bsky.social · 4mo ago

Making big tech algorithms “fair” is harder than it looks. New research finds that improving fairness is not as simple as applying technical solutions – it requires considering fairness at every stage of the engineering workflow. Learn more: https://bit.ly/4tne0Fz @cornellbowers.bsky.social

Divya Shanmugam@dmshanmugam.bsky.social · 5mo ago

New in Nature Health: how might we move towards a world in which race is not used in clinical algorithms? We need (1) careful comparison of race-aware and race-neutral algorithms and (2) systemic efforts to address underlying disparities.

Prof. Nicole Holliday@mixedlinguist.bsky.social · 8mo ago

Do you know Zoom & other companies are encouraging discrimination against employees for things like "pausing too long" or not sounding sufficiently "charismatic"? Read all about our dystopian present in my new paper, out today in JASA! (Seriously, read it. It's important) doi.org/10.1121/10.0...

Socially prescriptive speech technologies: Linguistic, technical, and ethical issues

Speech technology tools can be powerful and transformative for individuals, businesses, and governments. Socially prescriptive speech technology (SPST) systems

doi.org

Martin Saveski@msaveski.bsky.social · 8mo ago

New paper in Science: In a platform-independent field experiment, we show that reranking content expressing antidemocratic attitudes and partisan animosity in social media feeds alters affective polarization. 🧵

Today, social media platforms hold the sole power to study the effects of feed-ranking algorithms. We developed a platform-independent method that reranks participants’ feeds in real time and used this method to conduct a preregistered 10-day field experiment with 1256 participants on X during the 2024 US presidential campaign. Our experiment used a large language model to rerank posts that expressed antidemocratic attitudes and partisan animosity (AAPA). Decreasing or increasing AAPA exposure shifted out-party partisan animosity by more than 2 points on a 100-point feeling thermometer, with no detectable differences across party lines, providing causal evidence that exposure to AAPA content alters affective polarization. This work establishes a method to study feed algorithms without requiring platform cooperation, enabling independent evaluation of ranking interventions in naturalistic settings.
Allison Koenecke@allisonkoe.bsky.social · 8mo ago

Check out @isabelcorpus.bsky.social's fantastic thread on our paper studying the effects of a "write with AI" button on change.org! ✍️ Spoiler: the effects of AI aren't always positive.

Isabel Silva Corpus@isabelcorpus.bsky.social · 8mo ago

Excited to share a new working paper! What happened when Change.org integrated an AI writing tool into their platform? We provide causal evidence that petition text changed significantly while outcomes did not improve. 1/ arxiv.org/abs/2511.13949

Emma Pierson@emmapierson.bsky.social · 8mo ago

We have a new paper in Science Advances proposing a simple test for bias: Is the same person treated differently when their race is perceived differently? Specifically, we study: is the same driver likelier to be searched by police when they are perceived as Hispanic rather than white? 1/

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Kate Donahue@kpaxdonahue.bsky.social · 9mo ago

I’m recruiting students this upcoming cycle at UIUC! I’m excited about Qs on societal impact of AI, especially human-AI collaboration, multi-agent interactions, incentives in data sharing, and AI policy/regulation (all from both a theoretical and applied lens). Apply through CS & select my name!

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Allison Koenecke@allisonkoe.bsky.social · 9mo ago

It was fantastic to collaborate across Cornell and Apple for our EMNLP paper auditing LLMs for dialectal biases in multiple choice benchmark datasets: arxiv.org/abs/2510.00962. Anna @annaseogyeongchoi.bsky.social (who's on the job market this year!) did a great job presenting this work today!

Analyzing Dialectical Biases in LLMs for Knowledge and Reasoning Benchmarks

Large language models (LLMs) are ubiquitous in modern day natural language processing. However, previous work has shown degraded LLM performance for under-represented English dialects. We analyze the ...

arxiv.org

AAnna Seo Gyeong Choi@annaseogyeongchoi.bsky.social · 9mo ago

🧵Excited to present our work at #EMNLP2025 “Analyzing Dialectal Biases in LLMs for Knowledge and Reasoning Benchmarks”! Paper 📄 arxiv.org/abs/2510.00962 w/ Eileen Pan, Skyler Seto, @allisonkoe.bsky.social @maartjeterhoeve.bsky.social

Research poster showing LLMs perform up to 20% worse on non-standard English dialects. Includes tables comparing performance across 6 dialects (African American, Appalachian, Chicano, Indian, Singaporean, Southern) and bar charts showing three grammar rules (existential it, zero copula, y'all) explain roughly half the accuracy decrease
Allison Koenecke@allisonkoe.bsky.social · last yr.

@jennahgosciak.bsky.social just gave a fantastic talk on this paper about temporally missing data at @ic2s2.bsky.social 🎉 -- find us this afternoon if you want to chat about it!

Jennah Gosciak@jennahgosciak.bsky.social · last yr.

I am presenting a new 📝 “Bias Delayed is Bias Denied? Assessing the Effect of Reporting Delays on Disparity Assessments” at @facct.bsky.social on Thursday, with @aparnabee.bsky.social, Derek Ouyang, @allisonkoe.bsky.social, @marzyehghassemi.bsky.social, and Dan Ho. 🔗: arxiv.org/abs/2506.13735 (1/n)

"Bias Delayed is Bias Denied? Assessing the Effect of Reporting Delays on Disparity Assessments" Conducting disparity assessments at regular time intervals is critical for surfacing potential biases in decision-making and improving outcomes across demographic groups. Because disparity assessments fundamentally depend on the availability of demographic information, their efficacy is limited by the availability and consistency of available demographic identifiers. While prior work has considered the impact of missing data on fairness, little attention has been paid to the role of delayed demographic data. Delayed data, while eventually observed, might be missing at the critical point of monitoring and action -- and delays may be unequally distributed across groups in ways that distort disparity assessments. We characterize such impacts in healthcare, using electronic health records of over 5M patients across primary care practices in all 50 states. Our contributions are threefold. First, we document the high rate of race and ethnicity reporting delays in a healthcare setting and demonstrate widespread variation in rates at which demographics are reported across different groups. Second, through a set of retrospective analyses using real data, we find that such delays impact disparity assessments and hence conclusions made across a range of consequential healthcare outcomes, particularly at more granular levels of state-level and practice-level assessments. Third, we find limited ability of conventional methods that impute missing race in mitigating the effects of reporting delays on the accuracy of timely disparity assessments. Our insights and methods generalize to many domains of algorithmic fairness where delays in the availability of sensitive information may confound audits, thus deserving closer attention within a pipeline-aware machine learning framework.
Allison Koenecke@allisonkoe.bsky.social · last yr.

For folks at @ic2s2.bsky.social, I'm excited to be sharing this work at this afternoon's session on LLMs & Bias!

Allison Koenecke@allisonkoe.bsky.social · last yr.

🎉Excited to present our paper tomorrow at @facct.bsky.social, “Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese”, with @brucelyu17.bsky.social, Jiebo Luo and Jian Kang, revealing 🤖 LLM performance disparities. 📄 Link: arxiv.org/abs/2505.22645

"Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese" Abstract: While the capabilities of Large Language Models (LLMs) have been studied in both Simplified and Traditional Chinese, it is yet unclear whether LLMs exhibit differential performance when prompted in these two variants of written Chinese. This understanding is critical, as disparities in the quality of LLM responses can perpetuate representational harms by ignoring the different cultural contexts underlying Simplified versus Traditional Chinese, and can exacerbate downstream harms in LLM-facilitated decision-making in domains such as education or hiring. To investigate potential LLM performance disparities, we design two benchmark tasks that reflect real-world scenarios: regional term choice (prompting the LLM to name a described item which is referred to differently in Mainland China and Taiwan), and regional name choice (prompting the LLM to choose who to hire from a list of names in both Simplified and Traditional Chinese). For both tasks, we audit the performance of 11 leading commercial LLM services and open-sourced models -- spanning those primarily trained on English, Simplified Chinese, or Traditional Chinese. Our analyses indicate that biases in LLM responses are dependent on both the task and prompting language: while most LLMs disproportionately favored Simplified Chinese responses in the regional term choice task, they surprisingly favored Traditional Chinese names in the regional name choice task. We find that these disparities may arise from differences in training data representation, written character preferences, and tokenization of Simplified and Traditional Chinese. These findings highlight the need for further analysis of LLM biases; as such, we provide an open-sourced benchmark dataset to foster reproducible evaluations of future LLM behavior across Chinese language variants (this https URL).
Allison Koenecke@allisonkoe.bsky.social · last yr.

This Thursday at @facct.bsky.social, @jennahgosciak.bsky.social's presenting our work at the 10:45am "Audits 2" session! We collaborated across @cornellbowers.bsky.social, @mit.edu, & @stanfordlaw.bsky.social to study health estimate biases from delayed race data collection: arxiv.org/abs/2506.13735

Bias Delayed is Bias Denied? Assessing the Effect of Reporting Delays on Disparity Assessments

Conducting disparity assessments at regular time intervals is critical for surfacing potential biases in decision-making and improving outcomes across demographic groups. Because disparity assessments...

arxiv.org

Jennah Gosciak@jennahgosciak.bsky.social · last yr.

I am presenting a new 📝 “Bias Delayed is Bias Denied? Assessing the Effect of Reporting Delays on Disparity Assessments” at @facct.bsky.social on Thursday, with @aparnabee.bsky.social, Derek Ouyang, @allisonkoe.bsky.social, @marzyehghassemi.bsky.social, and Dan Ho. 🔗: arxiv.org/abs/2506.13735 (1/n)

"Bias Delayed is Bias Denied? Assessing the Effect of Reporting Delays on Disparity Assessments" Conducting disparity assessments at regular time intervals is critical for surfacing potential biases in decision-making and improving outcomes across demographic groups. Because disparity assessments fundamentally depend on the availability of demographic information, their efficacy is limited by the availability and consistency of available demographic identifiers. While prior work has considered the impact of missing data on fairness, little attention has been paid to the role of delayed demographic data. Delayed data, while eventually observed, might be missing at the critical point of monitoring and action -- and delays may be unequally distributed across groups in ways that distort disparity assessments. We characterize such impacts in healthcare, using electronic health records of over 5M patients across primary care practices in all 50 states. Our contributions are threefold. First, we document the high rate of race and ethnicity reporting delays in a healthcare setting and demonstrate widespread variation in rates at which demographics are reported across different groups. Second, through a set of retrospective analyses using real data, we find that such delays impact disparity assessments and hence conclusions made across a range of consequential healthcare outcomes, particularly at more granular levels of state-level and practice-level assessments. Third, we find limited ability of conventional methods that impute missing race in mitigating the effects of reporting delays on the accuracy of timely disparity assessments. Our insights and methods generalize to many domains of algorithmic fairness where delays in the availability of sensitive information may confound audits, thus deserving closer attention within a pipeline-aware machine learning framework.
Allison Koenecke@allisonkoe.bsky.social · last yr.

For folks at @facct.bsky.social, our very own @cornellbowers.bsky.social student @emmharv.bsky.social will present the Best-Paper-Award-winning work she led on Wednesday at 10:45 AM in the "Audit and Evaluation Approaches" session! In the meantime, 🧵 below and 🔗 here: arxiv.org/abs/2506.04419 !

A Framework for Auditing Chatbots for Dialect-Based Quality-of-Service Harms

Increasingly, individuals who engage in online activities are expected to interact with large language model (LLM)-based chatbots. Prior work has shown that LLMs can display dialect bias, which occurs...

arxiv.org

Emma Harvey@emmharv.bsky.social · last yr.

I am so excited to be in 🇬🇷Athens🇬🇷 to present "A Framework for Auditing Chatbots for Dialect-Based Quality-of-Service Harms" by me, @kizilcec.bsky.social, and @allisonkoe.bsky.social, at #FAccT2025!! 🔗: arxiv.org/pdf/2506.04419

A screenshot of our paper's: Title: A Framework for Auditing Chatbots for Dialect-Based Quality-of-Service Harms Authors: Emma Harvey, Rene Kizilcec, Allison Koenecke Abstract: Increasingly, individuals who engage in online activities are expected to interact with large language model (LLM)-based chatbots. Prior work has shown that LLMs can display dialect bias, which occurs when they produce harmful responses when prompted with text written in minoritized dialects. However, whether and how this bias propagates to systems built on top of LLMs, such as chatbots, is still unclear. We conduct a review of existing approaches for auditing LLMs for dialect bias and show that they cannot be straightforwardly adapted to audit LLM-based chatbots due to issues of substantive and ecological validity. To address this, we present a framework for auditing LLM-based chatbots for dialect bias by measuring the extent to which they produce quality-of-service harms, which occur when systems do not work equally well for different people. Our framework has three key characteristics that make it useful in practice. First, by leveraging dynamically generated instead of pre-existing text, our framework enables testing over any dialect, facilitates multi-turn conversations, and represents how users are likely to interact with chatbots in the real world. Second, by measuring quality-of-service harms, our framework aligns audit results with the real-world outcomes of chatbot use. Third, our framework requires only query access to an LLM-based chatbot, meaning that it can be leveraged equally effectively by internal auditors, external auditors, and even individual users in order to promote accountability. To demonstrate the efficacy of our framework, we conduct a case study audit of Amazon Rufus, a widely-used LLM-based chatbot in the customer service domain. Our results reveal that Rufus produces lower-quality responses to prompts written in minoritized English dialects.