Pratyusha Kalluri

All Publications

• People who share encounters with racism are silenced online by humans and machines, but a guideline-reframing intervention holds promise. Proceedings of the National Academy of Sciences of the United States of America Lee, C., Gligorić, K., Kalluri, P. R., Harrington, M., Durmus, E., Sanchez, K. L., San, N., Tse, D., Zhao, X., Hamedani, M. G., Markus, H. R., Jurafsky, D., Eberhardt, J. L. 2024; 121 (38): e2322764121

  Abstract

  Are members of marginalized communities silenced on social media when they share personal experiences of racism? Here, we investigate the role of algorithms, humans, and platform guidelines in suppressing disclosures of racial discrimination. In a field study of actual posts from a neighborhood-based social media platform, we find that when users talk about their experiences as targets of racism, their posts are disproportionately flagged for removal as toxic by five widely used moderation algorithms from major online platforms, including the most recent large language models. We show that human users disproportionately flag these disclosures for removal as well. Next, in a follow-up experiment, we demonstrate that merely witnessing such suppression negatively influences how Black Americans view the community and their place in it. Finally, to address these challenges to equity and inclusion in online spaces, we introduce a mitigation strategy: a guideline-reframing intervention that is effective at reducing silencing behavior across the political spectrum.

  View details for DOI 10.1073/pnas.2322764121

  View details for PubMedID 39250662

• AI generates covertly racist decisions about people based on their dialect. Nature Hofmann, V., Kalluri, P. R., Jurafsky, D., King, S. 2024

  Abstract

  Hundreds of millions of people now interact with language models, with uses ranging from help with writing1,2 to informing hiring decisions3. However, these language models are known to perpetuate systematic racial prejudices, making their judgements biased in problematic ways about groups such as African Americans4-7. Although previous research has focused on overt racism in language models, social scientists have argued that racism with a more subtle character has developed over time, particularly in the United States after the civil rights movement8,9. It is unknown whether this covert racism manifests in language models. Here, we demonstrate that language models embody covert racism in the form of dialect prejudice, exhibiting raciolinguistic stereotypes about speakers of African American English (AAE) that are more negative than any human stereotypes about African Americans ever experimentally recorded. By contrast, the language models' overt stereotypes about African Americans are more positive. Dialect prejudice has the potential for harmful consequences: language models are more likely to suggest that speakers of AAE be assigned less-prestigious jobs, be convicted of crimes and be sentenced to death. Finally, we show that current practices of alleviating racial bias in language models, such as human preference alignment, exacerbate the discrepancy between covert and overt stereotypes, by superficially obscuring the racism that language models maintain on a deeper level. Our findings have far-reaching implications for the fair and safe use of language technology.

  View details for DOI 10.1038/s41586-024-07856-5

  View details for PubMedID 39198640

  View details for PubMedCentralID 10338337

• Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale Bianchi, F., Kalluri, P., Durmus, E., Ladhak, F., Cheng, M., Nozza, D., Hashimoto, T., Jurafsky, D., Zou, J., Caliskan, A., ASSOC COMPUTING MACHINERY ASSOC COMPUTING MACHINERY. 2023: 1493-1504

  View details for DOI 10.1145/3593013.3594095

  View details for Web of Science ID 001062819300123

• Don't ask if artificial intelligence is good or fair, ask how it shifts power NATURE Kalluri, P. 2020; 583 (7815): 169

  View details for Web of Science ID 000546778100001

  View details for PubMedID 32636520

• Learning Controllable Fair Representations Song, J., Kalluri, P., Grover, A., Zhao, S., Ermon, S., Chaudhuri, K., Sugiyama, M. MICROTOME PUBLISHING. 2019

  View details for Web of Science ID 000509687902022