Jennah Gosciak
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jennahgosciak.bsky.social
Jennah Gosciak
@jennahgosciak.bsky.social
64 followers 100 following 17 posts
PhD Student @Cornell IS
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Reposted by Jennah Gosciak
urban-data.bsky.social
#30DayMapChallenge day 19: projections

@jennahgosciak.bsky.social created a gif that visualizes NYC neighborhoods under different map projections. While most atrocities are clear only at national or global levels, her maps show interesting local deformities!
November 24, 2025 at 2:42 PM
Reposted by Jennah Gosciak
isabelcorpus.bsky.social
@jennahgosciak.bsky.social showed how LLM assistance can improve government caseworker accuracy in the context of SNAP eligibility questions. It was really cool to see Jennah + team get ahead of the ever-shifting technical capacity of LLMs by varying chatbot accuracy.
November 18, 2025 at 2:54 PM
Reposted by Jennah Gosciak
isabelcorpus.bsky.social
Had a great time at CODE@MIT this weekend, and wanted to highlight a few (of the many) cool talks!
November 18, 2025 at 2:54 PM
Reposted by Jennah Gosciak
urban-data.bsky.social
For #30DayMapChallenge day 11, a minimal map from @kennypeng.bsky.social.

Kenny extracts minimal elements from a not-as-minimal-as-it-seems object: the Manhattan street grid. "I show how Manhattan’s numbered grid of streets and avenues is more complicated than you might realize," he says.
November 17, 2025 at 2:43 PM
Reposted by Jennah Gosciak
urban-data.bsky.social
#30DayMapChallenge day 9 asked us to get off our screens. @annaloganmc.bsky.social's "analog" map is a hand-painted postcard! 📫

"I chose to paint a postcard of a map of Ann Arbor where I currently live showing the Huron River!" she says
November 14, 2025 at 8:42 PM
Reposted by Jennah Gosciak
urban-data.bsky.social
Day 7 of #30DayMapChallenge asked us to think about accessibility. @gsagostini.bsky.social considers two metrics of access simultaneously: distance to a Subway and distance to the subway.
November 13, 2025 at 2:27 PM
Reposted by Jennah Gosciak
urban-data.bsky.social
#30DayMapChallenge day 6: dimensions

Atmika Pai dove into the ArcGIS Unity SDK to see how architects & designers plan in 3D. She explored 15 features from geocoding to viewshed analysis, all in an immersive game engine! Here's a video teaser of her map.
November 12, 2025 at 6:40 PM
Reposted by Jennah Gosciak
urban-data.bsky.social
Last week, we presented the work of our group at the EAAMO conference. So, for our delayed #30DayMapChallenge day 4 (my data) we created a map showing flows from some EAAMO contributors and attendees!
November 11, 2025 at 4:05 PM
jennahgosciak.bsky.social
For day 5 of the #30daymapchallenge, I compared the original ecology of New York City with data from the Welikia Project to the present-day.
urban-data.bsky.social Urban Data @urban-data.bsky.social · 13d
We might be a few days delayed on the #30DayMapChallenge, but our day 5 submission spans almost 250 years of history! Our "Earth" map comes from @jennahgosciak.bsky.social, who compared the original ecology of New York City to present day variables.
November 11, 2025 at 5:38 PM
Reposted by Jennah Gosciak
urban-data.bsky.social
We might be a few days delayed on the #30DayMapChallenge, but our day 5 submission spans almost 250 years of history! Our "Earth" map comes from @jennahgosciak.bsky.social, who compared the original ecology of New York City to present day variables.
November 11, 2025 at 5:01 PM
Reposted by Jennah Gosciak
urban-data.bsky.social
#30daymapchallenge day 1: Points

For our first map, we had to visualize spatial data as dots. @gsagostini.bsky.social worked on a dot density map showing dog density in New York City.

1 dot = 1 dog 🐕🐩
Dot density map of dogs in New York City. Includes a scatter plot of the canine population vs. human population per ZIP code.
November 6, 2025 at 5:18 PM
Reposted by Jennah Gosciak
urban-data.bsky.social
#30DayMapChallenge day 2: Lines

Atmika Pai interpreted lines in multiple ways. Her map shows how the lines we stand in to wait for the tramway (line) generate flows lines!
November 6, 2025 at 5:33 PM
Reposted by Jennah Gosciak
urban-data.bsky.social
We are slowly catching up to the #30DayMapChallenge!

In our day 3: polygons submission, @zhixuanqi.bsky.social questioned the boundaries and fuzziness of polygons with an animated map that invites us to think about the (not-so-well-defined) idea of neighborhoods.
November 7, 2025 at 5:20 PM
Reposted by Jennah Gosciak
urban-data.bsky.social
To get this feed started, our group will be participating in the #30daymapchallenge! On (most) days of November we will be posting a new map, by a new author, on a new theme. First batch of maps incoming... 🗺️

30daymapchallenge.com#maps-stats
30DayMapChallenge
Daily mapping challenge happening every November!
30daymapchallenge.com
November 6, 2025 at 4:47 PM
Reposted by Jennah Gosciak
dmshanmugam.bsky.social
I am on the job market this year! My research advances methods for reliable machine learning from real-world data, with a focus on healthcare. Happy to chat if this is of interest to you or your department/team.
October 14, 2025 at 3:45 PM
Reposted by Jennah Gosciak
travislloydphd.bsky.social
I'm at Seattle 4S! I'll be part of the "Risks of 'Social Model Collapse' in the Face of Scientific and Technological Advances" panel Friday morning, discussing online community governance of AI-generated content. Would love to meet others studying AI's impact on the info ecosystem!
#STS #4S
September 3, 2025 at 2:12 PM
jennahgosciak.bsky.social
🏙️ Join us this semester if you are interested in urban data science. Our group is looking for new members!! 🌇
September 3, 2025 at 3:09 PM
Reposted by Jennah Gosciak
gsagostini.bsky.social
Are you a researcher using computational methods to understand cities?

@mfranchi.bsky.social @jennahgosciak.bsky.social and I organize an EAAMO Bridges working group on Urban Data Science and we are looking for new members!

Fill the interest form on our page: urban-data-science-eaamo.github.io
Urban Data Science & Equitable Cities | EAAMO Bridges
EAAMO Bridges Urban Data Science & Equitable Cities working group: biweekly talks, paper studies, and workshops on computational urban data analysis to explore and address inequities.
urban-data-science-eaamo.github.io
September 3, 2025 at 3:05 PM
Reposted by Jennah Gosciak
allisonkoe.bsky.social
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
June 24, 2025 at 3:40 PM
Reposted by Jennah Gosciak
aparnabee.bsky.social
Excited to share our work on the impact of reporting delays on disparity audits in healthcare 😄 Project started at the Stanford RegLab Summer Institute last year, and continued with an amazing group of collaborators! @jennahgosciak.bsky.social will present this at FAccT -- do drop by if attending!
jennahgosciak.bsky.social Jennah Gosciak @jennahgosciak.bsky.social · Jun 24
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. Figure contrasting a conventional approach to conducting disparity assessments, which is static, to the analysis we conduct in this paper. Our analysis (1) uses comprehensive health data from over 1,000 primary care practices and 5 million patients across the U.S., (2) timestamped information on the reporting of race to measure delay, and (3) retrospective analyses of disparity assessments under varying levels of delay.
June 24, 2025 at 3:37 PM
jennahgosciak.bsky.social
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. Figure contrasting a conventional approach to conducting disparity assessments, which is static, to the analysis we conduct in this paper. Our analysis (1) uses comprehensive health data from over 1,000 primary care practices and 5 million patients across the U.S., (2) timestamped information on the reporting of race to measure delay, and (3) retrospective analyses of disparity assessments under varying levels of delay.
June 24, 2025 at 2:51 PM
Reposted by Jennah Gosciak
emmharv.bsky.social
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.
June 23, 2025 at 2:45 PM
Reposted by Jennah Gosciak
allisonkoe.bsky.social
🎉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). Figure showing that three different LLMs (GPT-4o, Qwen-1.5, and Taiwan-LLM) may answer a prompt about pineapples differently when asked in Simplified Chinese vs. Traditional Chinese. Figure showing that LLMs disproportionately answer questions about regional-specific terms (like the word for "pineapple," which differs in Simplified and Traditional Chinese) correctly when prompted in Simplified Chinese as opposed to Traditional Chinese. Figure showing that LLMs have high variance of adhering to prompt instructions, favoring Traditional Chinese names over Simplified Chinese names in a benchmark task regarding hiring.
June 22, 2025 at 9:16 PM
Reposted by Jennah Gosciak
travislloydphd.bsky.social
It was a pleasure to present our (@jennahgosciak.bsky.social @tungdnguyen.bsky.social @informor.bsky.social) large-scale study of Reddit community's AI rules in the AI Ethics and Concerns session at #CHI25! The paper is now available open access in the ACM library: dl.acm.org/doi/10.1145/....
April 30, 2025 at 1:08 AM
Reposted by Jennah Gosciak
andreawwenyi.bsky.social
[New preprint!] Do Chinese AI Models Speak Chinese Languages? Not really. Chinese LLMs like DeepSeek are better at French than Cantonese. Joint work with
Unso Jo and @dmimno.bsky.social . Link to paper: arxiv.org/pdf/2504.00289
🧵
April 9, 2025 at 8:28 PM