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the claim

Data collected by technology and AI is based on societal norms, values, and assumptions.

the verdict
SUPPORTED
the evidence backs this
Recorded sources
3 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

Is it true that data collected by technology and AI is based on societal norms, values, and assumptions?

Yes. Data collected and utilized by artificial intelligence systems heavily reflects underlying societal norms, assumptions, and human biases embedded within the data-generating process.

Verdict: SUPPORTED · 3 sources for, 0 against · Last checked

The analysis

The claim is specific, empirical, and contestable, passing Step 0. The retrieved literature supports the notion that technology and AI models are shaped by societal structures, norms, and human contexts, leading to biases when these societal factors are unexamined or unrepresentative (papers 1, 5, 10).

Evidence for · 3
A Tale of Two Social Credit Systems
2023
Discusses how machine learning adoption relies heavily on social compatibility and specific societal contexts.
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Liu, C. (2023). A Tale of Two Social Credit Systems. doi.org. https://doi.org/10.1093/oxfordhb/9780197653609.013.26

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More for · 2
AI inclusivity and the centrality of Africa: a systematic review of representation, innovation, and governance in global artificial intelligence.
2026
Demonstrates that data diversity and representation in AI development are deeply tied to cultural and societal contexts, leading to bias when underrepresented.
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R, M. (2026). AI inclusivity and the centrality of Africa: a systematic review of representation, innovation, and governance in global artificial intelligence.. doi.org. https://doi.org/10.3389/frai.2026.1881299

A Microsimulation-Based Approach for Mitigating Societal Bias in Chronic Kidney Disease Data.
2026
Shows how healthcare data generation mechanisms reflect underlying societal norms and race-based criteria that require mitigation.
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A, F., F, A., G, B., MM, C., RL, P., A, B., AS, A., & S, R. (2026). A Microsimulation-Based Approach for Mitigating Societal Bias in Chronic Kidney Disease Data.. doi.org. https://doi.org/10.1177/0272989x261432162

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