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Numbers can't speak for themselves, and data sets - no matter their scale - are still objects of hu
Numbers can't speak for themselves, and data sets - no matter their scale - are still objects of hu
Numbers can't speak for themselves, and data sets - no matter their scale - are still objects of hu
Numbers can't speak for themselves, and data sets - no matter their scale - are still objects of hu
Numbers can't speak for themselves, and data sets - no matter their scale - are still objects of hu
Numbers can't speak for themselves, and data sets - no matter their scale - are still objects of hu
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Kate Crawford:
Biases and blind spots exist in big data as much as they do in individual perceptions and experiencKate Crawford:
While many big-data providers do their best to de-identify individuals from human-subject data setsKate Crawford:
If you're not thinking about the way systemic bias can be propagated through the criminal justice sKate Crawford:
Data will always bear the marks of its history. That is human history held in those data sets.Kate Crawford:
If you have rooms that are very homogeneous, that have all had the same life experiences and educatKate Crawford:
We should have equivalent due-process protections for algorithmic decisions as for human decisions.Kate Crawford:
Data and data sets are not objective; they are creations of human design. We give numbers their voiKate Crawford:
Hidden biases in both the collection and analysis stages present considerable risks and are as impoKate Crawford:
While massive datasets may feel very abstract, they are intricately linked to physical place and huKate del Castillo:
I do not see myself, I never make plans, I never set goals, and I never do that kind of stuff; I do