Technology Science is an open access, peer-reviewed publication for any original material dealing primarily with a social, political, personal, or organizational benefit or adverse consequence of technology. Studies that characterize a technology-society clash or present an approach to better harmonize technology and society are especially welcomed. Papers can come from anywhere in the world.

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May 23, 2025

Trustworthy AI Data Communities (AI-DCs) : Unlocking the benefits of AI for all, using U.S. manufacturing as a case study  

Latanya Sweeney, Leonie Beyrle Barker, and Joshua Shank

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This paper introduces the AI Data Community—a privacy-preserving ecosystem that empowers small and medium-sized enterprises (SMEs) to securely share data and collaboratively develop AI-driven solutions. (Diagram: courtesy of the Purdue ManuFuture Today team.)

  • The AI Data Community (AI-DC) is a novel framework that enables small and medium-sized enterprises (SMEs) to safely participate in various data commons configurations and co-develop AI tools, overcoming limits in data access, expertise, and privacy.
  • Built on the MyDataCan privacy infrastructure, AI-DCs let SMEs control and combine their data using integrated privacy-preserving technologies while enabling collective analytics.
  • Standard data commons models are enhanced when executed within an AI-DC, offering consortium benefits like cost-sharing (in SaaS and DaaS), revenue opportunities (via Data Brokers), and safe participation in open innovation efforts (like Grand Challenges), while solving traditional limitations related to privacy, and scalability.

April 22, 2025

Measuring Mistakes: A Data-Driven Assessment of Voter List Maintenance and Erroneous Deletions in Ohio’s 2024 Election

Latanya Sweeney and Josh Visnaw

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This study introduces the Erroneous Removal Rate (ERR) to assess the accuracy of Ohio’s 2024 voter roll maintenance and finds that 1 to 1.5 percent of removed voters were wrongly deleted.

  • In June 2024, Ohio officials publicly announced the scheduled removal of 158,857 voter registrations as part of its routine list maintenance process.
  • This study finds that at least 1 percent of those voters were erroneously removed and had to take corrective action to remain registered—introducing a statewide Erroneous Removal Rate (ERR) of 1 percent.
  • Geographic disparities exist, with municipalities like Warren and Girard showing error rates of 7 to 9 percent—far above the statewide average.
  • These findings underscore the critical need for continuous, data-informed monitoring to protect the integrity of voter rolls and eligible voters from erroneous disenfranchisement.

April 22, 2025

A Systematic Review of the 109,119 Voter Registration Drops in Ohio During the Federal Law’s 90-Day Quiet Period

Latanya Sweeney

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This study found that 109,119 voter registrations were removed in Ohio during the federally restricted 90-day quiet period before the 2024 election—13% of which lacked a legally valid explanation.

  • Ohio election officials removed 109,119 voter registrations during the federally restricted 90-day “quiet period” leading up to the 2024 general election.
  • While this study found 87% of the removals justified, no legally permissible explanation was found for 13%—or 14,539 registrations.
  • These unexplained removals occurred in every Ohio county and may be tied to third-party challenges.
  • The findings raise legal concerns and underscore the need for greater transparency and ongoing data monitoring to safeguard elections.

April 22, 2025

Efficacy of Text Messaging in Activating Hard-to-Reach Voters in Rhode Island and Ohio

Latanya Sweeney and Josh Visnaw

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This study tested whether one text message could prompt inactive voters to update their registration—and found modest but statistically significant improvements in Ohio.

  • This study tested whether a single text message could prompt inactive voters to update their registration, using voter-provided numbers in Rhode Island and data-broker-sourced numbers in Ohio.
  • In Rhode Island, at least 10% of texted voters updated their registration vs. 9% who didn’t—showing a performance boost.
  • In Ohio, 5% of texted voters updated their registration vs. 4% of non-recipients, proving statistically significant outreach worked even with brokered data.
  • These findings show that low-cost texts can effectively boost engagement and registration accuracy among hard-to-reach voters.

February 25, 2025

Risk Perception on Buy-now, Pay-later Platforms

Zoe Brewer and Alexander Arber

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This study assessed how consumers perceived financial risks when using two popular buy-now, pay-later services, Affirm and Afterpay, that are offered at the point of sale by online vendors. Above, a few of Affirm’s merchant partners at the time of the study in 2022.

  • This study investigates consumer understanding of financial risks associated with BNPL services, focusing on Affirm and Afterpay in 2022.
  • BNPL services disproportionately target financially vulnerable consumers, potentially exacerbating debt cycles.
  • In an experiment, most users struggled to comprehend key loan terms, such as payment frequency, number of installments, and the potential impact on credit scores.
  • Authors recommend further research to assess the impact of the May 2024 expansion of Truth in Lending regulations on consumer financial outcomes.

February 25, 2025

Facebook’s Search Interventions: Bad in English, Peor en Español

Julia High

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Facebook’s search feature displays interstitial message screens when a user searches using certain terms related to violence, nudity, sexual activity, or the sale of drugs. This study found that English-language searches were much more likely than searches in Spanish to trigger a pop-up intervention of the type shown here.

  • This study explores the following question: How effective are Facebook’s search interventions at responding to harmful queries in non-English languages?
  • Facebook’s search feature is instrumental in separating users from harmful content on the platform, and it is an essential part of the platform’s content moderation ecosystem.
  • The study found that 49.1% of English language words that should violate Meta’s Community Standards triggered search interventions, whereas only 21.1% of similar Spanish words did.
  • When searching in Spanish, it was far easier to access content that violates the Community Standards, including violent, sexually explicit, and harmful content. Furthermore, searches in Spanish rarely triggered the support or deterrence resources offered in response to comparable English searches.
  • Facebook’s search feature allows researchers a straightforward way of exploring the disparity between English and non-English languages without sowing harmful content onto the platform.

February 25, 2025

Linguistic Inequity in Facebook Content Moderation

Yi-Ting Lien and Maya Vishwanath

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Translating Facebook content from Mandarin to English using automated tools resulted in significant discrepancies in users’ perception of the content’s harmfulness. In a survey, subjects were presented objectionable (green) and neutral (blue) content in English translation and the original Mandarin and asked whether the content violated Facebook’s community guidelines.

  • Automated Translation and Content Moderation: Facebook's reliance on automated translation tools for moderation of non-English content can lead to significant inaccuracies. This impacts the platform's ability to accurately judge the harmfulness of non-English content, often resulting in wrongful removal or improper oversight of problematic content.
  • Disparities in Perception: The study found that English-speaking reviewers perceived the harmfulness of translated non-English content differently than native language speakers. This may create a linguistic inequity where non-Western users are disproportionately affected by Facebook's moderation practices.
  • Participatory Approach Recommendation: The research suggests that Facebook incorporates a participatory content moderation approach, where local language users can contribute to moderation decisions via surveys. This aims to mitigate the inaccuracies caused by automated translations.
  • Global Challenges in Content Moderation: Facebook faces challenges in moderating content in multiple languages, especially in regions like India and the Mandarin-speaking world. Automated tools and outsourced moderation practices often fail to capture local nuances, leading to further inequity.
  • Survey Results: In the survey, we compared English speakers’ perceptions of translated content with Mandarin speakers' perceptions of the same content in Mandarin. There was a statistically significant difference in how the two groups viewed the permissibility of the content, highlighting the risks of relying on automated translation tools.

November 10, 2021

The Inaccessibility of Many State Voter Registration Websites

Jinyan Zang, Alyx van der Vorm, and Juan Guzman

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A Map Demonstrating the Accessibility Scores of Each State Voter Registration Website Tested Using VoiceOver on a Mac.

  • Testing with a screen reader tool, VoiceOver on MacOS, revealed that 8 state voter registration websites were inaccessible to a visually impaired user: Alaska, Minnesota, Nebraska, Nevada, New Jersey, New Mexico, North Dakota, Oregon, Kansas, and Mississippi.
  • In addition, 14 state voter registration look-up websites—sites that can be used to look up a voter registration and other election information—were inaccessible with a screen reader: Alaska, Connecticut, Georgia, Mississippi, New Hampshire, New Jersey, New Mexico, North Dakota, Oregon, South Dakota, South Carolina, Texas, Vermont, and Wisconsin.
  • The 5 states that passed all our accessibility tests – California, Maryland, Oklahoma, Virginia and Washington – had strict web accessibility standards as requirements for official state websites.
  • However, 13 out of the 18 states that had either an inaccessible voter registration or voter registration look-up website also had strict web accessibility standards as requirements for state websites.
  • A few simple fixes, such as adding more labels to web forms, next page buttons, and submission buttons, would address many of the problems that made the websites we tested inaccessible when using a screen reader.

October 19, 2021

How Facebook’s Advertising Algorithms Can Discriminate By Race and Ethnicity

Jinyan Zang

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As more stereotypically African-American attributes were added to a customer list used to create a Lookalike audience of North Carolina voters, the sample shares of African-American voters in the corresponding Lookalike audiences offered to Facebook advertisers became more biased, increasing to 93% in 2020 and 94% in 2021.

  • This study examines racial and ethnic biases in the targeting of ads on Facebook’s advertising platform in January 2020 and January 2021, before and after a major July 2020 boycott of Facebook by advertisers over issues of misinformation and civil rights.
  • In 2021, Facebook’s new “African-American Culture” ad targeting option contained 75% fewer White users than the old “African American (US)” option removed the previous year.
  • Facebook’s tools to help advertisers find users similar to their existing customers generated biased target lists that included either more African-Americans or more Whites, depending on which racial group was dominant in an advertiser’s submitted customer list. This was true for both the Lookalike Audience tool and the Special Ad Audience tool that Facebook designed to explicitly not use sensitive demographic attributes when finding similar users.
  • The degree of bias toward African-Americans or Whites in the composition of a Lookalike or Special Ad audience was greater when customer lists of individuals with racially stereotypical names or ZIP codes were used as the basis for each tool.
  • Similarly, lookalike audiences can also be biased toward Asians. An audience generated by using a customer list of Asians with stereotypical names and ZIP codes as the source list was 100% Asian. Lookalike audiences based on source lists of Hispanics also overrepresented Hispanics.

December 17, 2019

Deepfake Bot Submissions to Federal Public Comment Websites Cannot Be Distinguished from Human Submissions

Max Weiss

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Example Deepfake Text generated by the bot that all survey respondents thought was from a human.

  • Publicly available artificial intelligence methods can generate an enormous volume of original, human speech-like topical text "Deepfake Text") that is not based on conventional search-and-replace patterns
  • I created a computer program (a bot) that generated and submitted 1,001 deepfake comments regarding a Medicaid reform waiver to a federal public comment website, stopping submission when these comments comprised more than half of all submitted comments. I then formally withdrew the bot comments
  • When humans were asked to classify a subset of the deepfake comments as human or bot submissions, the results were no better than would have been gotten by random guessing
  • Federal public comment websites currently are unable to detect Deepfake Text once submitted, but technological reforms (e.g., CAPTCHAs) can be implemented to help prevent massive numbers of submissions by bots