Semantics derived automatically from language corpora contain human-like biases
@article{Caliskan2017SemanticsDA, title={Semantics derived automatically from language corpora contain human-like biases}, author={A. Caliskan and J. Bryson and A. Narayanan}, journal={Science}, year={2017}, volume={356}, pages={183 - 186} }
Machines learn what people know implicitly AlphaGo has demonstrated that a machine can learn how to do things that people spend many years of concentrated study learning, and it can rapidly learn how to do them better than any human can. Caliskan et al. now show that machines can learn word associations from written texts and that these associations mirror those learned by humans, as measured by the Implicit Association Test (IAT) (see the Perspective by Greenwald). Why does this matter… CONTINUE READING
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References
SHOWING 1-10 OF 77 REFERENCES
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
- Computer Science, Mathematics
- NIPS
- 2016
- 981
- PDF
Extracting semantic representations from word co-occurrence statistics: A computational study
- Mathematics, Medicine
- Behavior research methods
- 2007
- 609
- PDF
Distributed Representations of Words and Phrases and their Compositionality
- Computer Science, Mathematics
- NIPS
- 2013
- 20,826
- PDF
From Frequency to Meaning: Vector Space Models of Semantics
- Computer Science
- J. Artif. Intell. Res.
- 2010
- 2,499
- PDF