Lightnews — Scholar-powered news

dan bateyko @dbateyko.bsky.social · Sep 2

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dan bateyko @dbateyko.bsky.social · Sep 2

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dan bateyko @dbateyko.bsky.social · Sep 2

It's remarkable how early Ford Foundation was to law and technology in the midcentury

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dan bateyko @dbateyko.bsky.social · Sep 2

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dan bateyko @dbateyko.bsky.social · Sep 1

this is what you see moments before going down a cyberspace and law rabbit hole

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dan bateyko @dbateyko.bsky.social · Aug 30

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dan bateyko @dbateyko.bsky.social · Aug 1

it finally happened (my 3090 overheated and emergency shut off)

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Reposted by dan bateyko

DeathNerd Killer @daphnek.bsky.social · Aug 1

Here’s the article. I’ve had more positive feedback on it than things I spent a year on.

Apparently describing a problem that thousands of Trust and Safety people are seeing but also see the world ignoring is a good way to win hearts and minds :)

www.lawfaremedia.org/article/the-...

The Rise of the Compliant Speech Platform

Content moderation is becoming a “compliance function,” with trust and safety operations run like factories and audited like investment banks.

www.lawfaremedia.org

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dan bateyko @dbateyko.bsky.social · Jul 18

“The possibilities of the pole” @hoctopi.bsky.social

dan bateyko @dbateyko.bsky.social · Jul 18

Eliza asking after an anti-suffering future of reproductive technology (the piece looks incredible)

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dan bateyko @dbateyko.bsky.social · Jul 18

@kevinbaker.bsky.social “The rules appear inevitable, natural, reasonable. We forget they were drawn by human hands.”

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dan bateyko @dbateyko.bsky.social · Jul 18

At the Kernel 5 issue launch!

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Reposted by dan bateyko

Emma Harvey @emmharv.bsky.social · Jul 14

After having such a great time at #CHI2025 and #FAccT2025, I wanted to share some of my favorite recent papers here!

I'll aim to post new ones throughout the summer and will tag all the authors I can find on Bsky. Please feel welcome to chime in with thoughts / paper recs / etc.!!

🧵⬇️:

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Reposted by dan bateyko

jay @kuppermann.xyz · Jul 15

i am launching a magazine with @kevinbaker.bsky.social and the rest of the reboot collective on thursday at gray area! you should be there!

open.substack.com/pub/reboothq...

Kernel Magazine
Issue 5: Rules Launch Party
July 17
6:30-9pm
SF
Gray Area

The illustration behind this text is a mix of gold chess pieces and purple snakes on a chessboard pattern

lu.ma/k5-sf

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Reposted by dan bateyko

Emma Harvey @emmharv.bsky.social · Jul 1

I've arrived in the 🌁Bay Area🌁, where I'll be spending the summer as a research fellow at Stanford's RegLab! If you're also here, LMK and let's get a meal / go on a hike / etc!!

A close-up view of the Golden Gate Bridge in the fog

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Reposted by dan bateyko

James Grimmelmann @jtlg.bsky.social · Jul 2

Well, this was a nice surprise. Download it while it’s hot!

lsolum.typepad.com/legaltheory/...

Grimmelmann, Sobel, & Stein on Generative AI and Legal Interpretation

James Grimmelmann (Cornell Law School; Cornell Tech), Benjamin Sobel (Cornell University - Cornell Tech NYC), & David Stein (Vanderbilt University - Vanderbilt Law School) have posted Generative Misin...

lsolum.typepad.com

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Reposted by dan bateyko

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.

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Reposted by dan bateyko

Emma Harvey @emmharv.bsky.social · Jun 23

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.

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Reposted by dan bateyko

Allison Koenecke @allisonkoe.bsky.social · Jun 22

🎉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.

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Reposted by dan bateyko

michael veale @michae.lv · Jun 23

I am at FAccT 2025 in Athens, feel free to grab me if you want to chat.

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Reposted by dan bateyko

Anna Neumann @annaneumann.bsky.social · Jun 23

Please come see us at the RC Trust Networking Event!
You can sign up with the QR Codes around the venues and get some free drinks! 🙂‍↕️

#FAccT2025

ACM FAccT @facct.bsky.social · Jun 23

Reminder that we have multiple social events happening this evening, all happening *outside* the conference venue!

Social: RC-Trust Networking Reception for the FAccT Community

Social: Generative AI Risks + Red-Teaming

Social: AI Workers' Inquiry

facctconference.org/2025/sociale...

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Reposted by dan bateyko

Princeton Center for Information Technology Policy @princetoncitp.bsky.social · Jun 6

New paper available - "Bureaucratic Backchannel: How r/PatentExaminer Navigates #AI Governance" which investigates how examiners navigate dual roles through a qualitative analysis of a Reddit community where U.S. Patent & Trademark Office employees discuss their work🔗📜👇

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