Don’t ask if artificial intelligence is good or fair, ask how it shifts power

By Pratyusha Kalluri. 2020

218 citations recorded by the source corpus.

Subfields: Health Informatics, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health Informatics

1. Irene Solaiman
2. Ben Shneiderman
3. Abeba Birhane
4. Marie-Therese Png

In Safety, Robustness & Interpretability

1. Maarten Sap
2. Yejin Choi
3. Noah A. Smith
4. Swabha Swayamdipta
5. Yulia Tsvetkov
6. Xuhui Zhou
7. Irene Solaiman
8. Zeerak Talat

Citations in the corpus

• Evaluating the Social Impact of Generative AI Systems in Systems and Society (2023, background): “Long-term societal inequity, power imbalances, and systemic injustices [265] feed training data [242], influence system development and deployment [237], and shape social impact [121].”
• The Gradient of Generative AI Release: Methods and Considerations (2023, background): “They must be empowered to shape systems that also benefit them, or to opt out of interaction with AI entirely [43].”
• The Values Encoded in Machine Learning Research (2021, background): “Similarly, Kalluri [33] denotes that the core values of ML are closely aligned with the values of the most privileged and outlines a vision where ML models are used to shift power from the most to the least powerful.”
• Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection (2021, background): “Additionally, we urge people to embrace that each design decision has socio-political implications (Green, 2020; Cambo, 2021), and encourage them to develop technologies to shift power to the targets of oppression (Blodgett et al., 2020; Kalluri, 2020; Birhane, 2021).”
• Bridging the Gap Between Ethics and Practice (2020, background): “While governments and corporations often make strong positive statements about their commitment to serving stakeholder needs, when leaders have to make difficult decisions about power and money, they may favor their personal needs, political pressures, and stockholder expectations [58].”
• The Forgotten Margins of AI Ethics (2022, background): “In “Don’t ask if artificial intelligence is good or fair, ask how it shifts power”, Kalluri [40] argues that genuine efforts to ethical thinking are those that bring about concrete material change to the conditions of the most marginalized, those approaches that shift power from the most to the leas”