| scholarly article | Q13442814 |
| P819 | ADS bibcode | 2017Sci...356..183C |
| P818 | arXiv ID | 1608.07187 |
| P356 | DOI | 10.1126/SCIENCE.AAL4230 |
| P8608 | Fatcat ID | release_tx326v534bhnpolxs7cd3rojx4 |
| P698 | PubMed publication ID | 28408601 |
| P50 | author | Arvind Narayanan | Q16442100 |
| Joanna Bryson | Q47493123 | ||
| Aylin Caliskan | Q87832196 | ||
| P2860 | cites work | GloVe: Global Vectors for Word Representation | Q22827276 |
| Distributed Representations of Words and Phrases and their Compositionality | Q24731579 | ||
| Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination | Q30050262 | ||
| National differences in gender-science stereotypes predict national sex differences in science and math achievement | Q30437573 | ||
| Extracting semantic representations from word co-occurrence statistics: a computational study | Q38394264 | ||
| Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings | Q44794248 | ||
| Math = male, me = female, therefore math not = me. | Q44888631 | ||
| Measuring individual differences in implicit cognition: the implicit association test. | Q52564449 | ||
| Harvesting implicit group attitudes and beliefs from a demonstration web site | Q53464849 | ||
| Fairness through awareness | Q56812996 | ||
| P433 | issue | 6334 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | automation | Q184199 |
| bias | Q742736 | ||
| word embedding | Q18395344 | ||
| P1104 | number of pages | 4 | |
| P304 | page(s) | 183-186 | |
| P577 | publication date | 2017-04-01 | |
| P1433 | published in | Science | Q192864 |
| P1476 | title | Semantics derived automatically from language corpora contain human-like biases | |
| P478 | volume | 356 |
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