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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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Investigating the Effects of Google’s Search Engine Result Page in Evaluating the Credibility of Online News Sources
Published in ACM Web Science Conference (WebSci), 2018
Recommended citation: Emma Lurie and Eni Mustafaraj. (2018). Investigating the Effects of Google's Search Engine Result Page in Evaluating the Credibility of Online News Sources. ACM WebSci.
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How the Interplay of Google and Wikipedia Affects Perceptions of Online News Sources
Published in Computation + Journalism, 2019
Recommended citation: Annabel Rothschild, Emma Lurie, and Eni Mustafaraj. (2019). How the Interplay of Google and Wikipedia Affects Perceptions of Online News Sources. Computation + Journalism.
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Opening Up the Black Box: Auditing Google’s Top Stories Algorithm
Published in AAAI FLAIRS Conference, 2019
Recommended citation: Emma Lurie and Eni Mustafaraj. (2019). Opening Up the Black Box: Auditing Google's Top Stories Algorithm. AAAI FLAIRS.
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The Challenges of Algorithmically Assigning Fact-checks: A Sociotechnical Examination of Google’s Reviewed Claims
Published in Undergraduate Thesis, Wellesley College, 2019
Recommended citation: Emma Lurie. (2019). The Challenges of Algorithmically Assigning Fact-checks: A Sociotechnical Examination of Google's Reviewed Claims. Undergraduate Thesis, Wellesley College.
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Considering Contestability in Automated Fact-Checking Systems
Published in Contestability in the Wild Workshop at CSCW'19, 2019
Recommended citation: Emma Lurie. (2019). Considering Contestability in Automated Fact-Checking Systems. Workshop at CSCW'19.
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The Case for Voter-Centered Audits of Search Engines During Political Elections
Published in ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2020
Recommended citation: Eni Mustafaraj, Emma Lurie, and Claire Devine. (2020). The Case for Voter-Centered Audits of Search Engines During Political Elections. ACM FAccT.
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Investigating Causal Effects of Instructions in Crowdsourced Claim Matching
Published in Computation + Journalism, 2020
Recommended citation: Emma Lurie, Lucy Li, Sofia Dewar, Masha Belyi, Daniel Rincón, John Baldwin, and Rajvardhan Oak. (2020). Investigating Causal Effects of Instructions in Crowdsourced Claim Matching. Computation + Journalism.
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Crowdworkers Are Not Judges: Rethinking Crowdsourced Vignette Studies as a Risk Assessment Evaluation Technique
Published in Fair and Responsible AI Workshop at CHI'20, 2020
Recommended citation: Emma Lurie and Deirdre K. Mulligan. (2020). Crowdworkers Are Not Judges: Rethinking Crowdsourced Vignette Studies as a Risk Assessment Evaluation Technique. Workshop at CHI'20.
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‘Highly Partisan’ and ‘Blatantly Wrong’: Analyzing News Publishers’ Critiques of Google’s Reviewed Claims
Published in Truth and Trust Online Conference, 2020
Recommended citation: Emma Lurie and Eni Mustafaraj. (2020). 'Highly Partisan' and 'Blatantly Wrong': Analyzing News Publishers' Critiques of Google's Reviewed Claims. Truth and Trust Online Conference.
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Reconfiguring Diversity and Inclusion for AI Ethics
Published in AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2021
Recommended citation: Nichole Chi, Emma Lurie, and Deirdre K. Mulligan. (2021). Reconfiguring Diversity and Inclusion for AI Ethics. AAAI/ACM Conference on AI, Ethics, and Society (AIES).
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Who needs imagination? Exploring legal professionals’ lack of curiosity about e-discovery tools
Published in Designing Technological Systems with the Algorithmic Imaginations of Those Who Labor Workshop at CHI'21, 2021
Recommended citation: Emma Lurie and Deirdre K. Mulligan. (2021). Who needs imagination? Exploring legal professionals' lack of curiosity about e-discovery tools. Workshop at CHI'21.
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Search quality complaints and imaginary repair: Control in articulations of Google Search
Published in New Media & Society, 2022
Recommended citation: Daniel Griffin* and Emma Lurie*. (2022). Search quality complaints and imaginary repair: Control in articulations of Google Search. New Media & Society.
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Searching for Representation: A Sociotechnical Audit of Googling for Members of Congress
Recommended citation: Emma Lurie and Deirdre K. Mulligan. Searching for Representation: A Sociotechnical Audit of Googling for Members of Congress. Preprint.
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Google Says So(S): An Examination of the Entanglement of Search Engines and Information on Ballot Propositions
Recommended citation: Emma Lurie. Google Says So(S): An Examination of the Entanglement of Search Engines and Information on Ballot Propositions. Preprint.
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The Construction of Doxxing in Judicial Opinions
Recommended citation: Emma Lurie. The Construction of Doxxing in Judicial Opinions. Preprint.
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Longitudinal Monitoring of LLM Content Moderation of Social Issues
Recommended citation: Yunlang Dai*, Emma Lurie*, Danaé Metaxa, and Sorelle A. Friedler. Longitudinal Monitoring of LLM Content Moderation of Social Issues. Preprint.
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Accounting for Stochasticity in Studies of Large Language Model Refusal
Published in EvalEval Workshop at ACL 2026, 2026
Recommended citation: Emma Lurie, Stephanie T. Wang, Danaé Metaxa, and Sorelle A. Friedler. (2026). Accounting for Stochasticity in Studies of Large Language Model Refusal. EvalEval Workshop at ACL 2026.
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What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations) Permalink
Published in AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2026
Code and data: github.com/seerocode/aies-llm-modality-audit
Recommended citation: Ro Encarnación, Tina Behzad, Emma Lurie, and Danaé Metaxa. (2026). What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations). AAAI/ACM Conference on AI, Ethics, and Society (AIES).
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The Beginning of ChatGPT Ads Permalink
Published in AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2026
Project site: ChatGPT Ads Library
Recommended citation: Emma Lurie, Ro Encarnación, Sorelle A. Friedler, and Danaé Metaxa. (2026). The Beginning of ChatGPT Ads. AAAI/ACM Conference on AI, Ethics, and Society (AIES).
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Triangulating Across U.S. Federal AI Transparency Regimes Permalink
Published in AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2026
Project site: govAIfiles.org · Code and data: github.com/triphora/govaifiles.org
Recommended citation: Emma Lurie, Emma Fauser, Qing He, Danaé Metaxa, and Sorelle A. Friedler. (2026). Triangulating Across U.S. Federal AI Transparency Regimes. AAAI/ACM Conference on AI, Ethics, and Society (AIES).
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