David Lazer

@davidlazer.bsky.social

computational social scientist

Google didn't just add AI to Google Earth—it briefly turned one of the world's most trusted sources of evidence into a generator of plausible fakes. shirin anlen & Sam Gregory from WITNESS on a foreseeable fiasco, and why tech firms must consult civil society experts before launch, not after.

Google Earth AI Fiasco Underscores Why Tech Firms Must Listen to Outside Experts

It's just one more parable in a long line of product releases by AI firms with potentially dangerous consequences, write shirin anlen and Sam Gregory.

techpolicy.press

Our new JCMC paper - led by Hsuen-Chi Chiu - looks at privacy attitudes when chatting with AI companions. Based on interviews with 15 chatbot users, we argue that there is a paradox - people trust their AI friends but distrust the corporations that run them! academic.oup.com/jcmc/article...

Chatting with confidants or corporations? Privacy management with AI companions

Abstract. AI companion chatbots feel like intimate confidants but exist as part of corporate data systems. Drawing on Communication Privacy Management (CPM

academic.oup.com

Google didn't just add AI to Google Earth—it briefly turned one of the world's most trusted sources of evidence into a generator of plausible fakes. shirin anlen & Sam Gregory from WITNESS on a foreseeable fiasco, and why tech firms must consult civil society experts before launch, not after.

Google Earth AI Fiasco Underscores Why Tech Firms Must Listen to Outside Experts

It's just one more parable in a long line of product releases by AI firms with potentially dangerous consequences, write shirin anlen and Sam Gregory.

techpolicy.press

My boiling hot take on this is that, if tech companies are going to fund this kind of research (looking at Schmidt too), then the money needs to be given *no strings* to an independent arm's length research body, pooled with other funding, and treated as a donation not as direct funding.

Hetan Shah@hetanshah.bsky.social · 5d ago

Anthropic AI research fund offers $5m - $30m for social science research on themes of AI impact on workers; transition initiatives; income support; building worker stakes; and wider evidence on public investments. Total fund of $200m available www.anthropic.com/news/economi...

Proprietary models/platforms may block the replicability part. However, reliability and robustness are a measurement validity problem; social scientists are trying to find a consensus on it. I will be presenting on this issue tomorrow, and my work is auditing the instruments we use to audit models.

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(feel free to complain to me to) And for the #IC2S2 crowd, I'll also point to the work of the Coalition of Independent Technology Research in helping protect CSS research & researchers, and encourage you to join the organization. independenttechresearch.org

Home - Coalition for Independent Technology Research

independenttechresearch.org

J. Nathan Matias@natematias.bsky.social · last wk.

I hope folks have a great time at #ic2s2 this week! So say hi to @davidlazer.bsky.social if you're interested in thinking about how to make computational social science more repeatable, and feel free to complain to me about any errors in our work: citizensandtech.org/2026/07/comm...

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New at the Strength In Numbers Data Lab: How pollsters weight by party ID is influencing Democrats' midterm standing. Different methodologies give a margin anywhere between D+7 and D+10 (among registered voters — likely even higher among likely voters). www.gelliottmorris.com/p/2026-07-23...

Democrats' edge in midterms polling depends on how pollsters weight by party

What would our Strength In Numbers polling say if we weighted our samples by different targets for party identity?

gelliottmorris.com

On Courage book tour has begun!! Dates and locations below. I’ll also share links prior to each event, but wanted to list the whole schedule for anyone interested.

July 7 - Porter Square Books, Boston MA
July 8 - P&T Knitwear, New York City NY
July 9 - Politics and Prose, Washington DC
July 15 - The Carter Center, Atlanta GA
July 22 — The Onion HQ, Chicago IL
July 28 — The Hammer, Los Angeles CA
July 29 — Manny’s, San Francisco CA
July 30 — Local Economy, Oakland CA
Aug 3 — Powell’s Books, Portland OR

The free version of ChatGPT automatically searches the web. The prompt "What are the most relevant sources for the claim that LLMs hallucinate?" returns the following. All those link buttons in the output are live hyperlinks that were found, not generated. If this isn't a search engine, what is it?

The foundational paper (introduced the term in the LLM context)
Ji et al. (2023)

Ji, Ziwei, et al. “Survey of Hallucination in Natural Language Generation.” ACM Computing Surveys 55, no. 12 (2023): 248.

This is the standard survey article.

It defines hallucination as generated content that is nonsensical or unfaithful to the provided source/input.

It distinguishes intrinsic hallucinations (contradict the source) from extrinsic hallucinations (unsupported by the source).

Useful when you need a general definition and overview of causes, detection, and mitigation strategies.

The paper most often cited for why hallucinations occur
Maynez et al. (2020)

Maynez, Joshua, et al. “On Faithfulness and Factuality in Abstractive Summarization.” Proceedings of ACL (2020).

Shows that neural generation systems often produce statements that are not supported by the source text.

Helped establish factuality/faithfulness as a core evaluation problem for generative models.

Frequently cited as evidence that hallucination is not an edge case but a structural property of neural text generation.

The key benchmark paper
TruthfulQA

Lin, Stephanie, Jacob Hilton, and Owain Evans. “TruthfulQA: Measuring How Models Mimic Human Falsehoods.” ACL (2022).

Evaluates whether models give truthful answers to questions that commonly elicit misconceptions.

Larger models often performed worse on truthfulness than smaller ones, because they better imitate patterns in web text.

This is one of the strongest empirical demonstrations that scaling alone does not eliminate hallucinations.

The classic “stochastic parrots” critique
Bender et al. (2021)

Bender, Emily M., et al. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” FAccT (2021).

Does not use the term “hallucination” as the central concept.
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Laura K. Nelson@lauraknelson.bsky.social · 3w ago

I'm once again begging everyone to update their priors. "ChatGPT is not a search engine." Yes, yes it is. It actively searches the web based on your prompt, and returns active links relevant to your search/prompt. It's a search engine. It is. (Obvs don't use it that way if that's not your thing)

Text as Data has been a wonderful, long-running, non-archival workshop for empirical research at the intersection of AI and social science (especially work involving text). After a few years off, it will be happening again this year as a one-day event in early October!

Denis@dpeskoff.bsky.social · 3w ago

Do you work across computational methods, social sciences, and the humanities? Submit to Text as Data 2026! 📄 One-page submissions 🔓 Non-archival ⏰ Due August 1 📍 October 5 @UCBerkeley tada2026.org

Excellent post from @pamherd.bsky.social and @donmoyn.bsky.social on NYC’s new PIT Crews—and on what becomes possible when technology serves inclusive, democratic policy aims like affordability, rather than becoming the aim itself. Rooting for the PIT Crews!

Don Moynihan@donmoyn.bsky.social · 3w ago

New w @pamherd.bsky.social: Mamdani just announced PIT Crew — an NYC tech unit that will build digital public products. Takeaway: Mamdani is reclaiming a vision of tech in govt for progressives, one not driven by consultants and which solves real problems. 🧵 donmoynihan.substack.com/p/mamdani-in...