%0 Journal Article %1 Radford_2020 %A Radford, Jason %A Joseph, Kenneth %D 2020 %I Frontiers Media SA %J Frontiers in Big Data %K Machine-learning fairness theory %R 10.3389/fdata.2020.00018 %T Theory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science %U https://doi.org/10.3389%2Ffdata.2020.00018 %V 3

@article{Radford_2020, added-at = {2020-06-02T23:47:57.000+0200}, author = {Radford, Jason and Joseph, Kenneth}, biburl = {https://www.bibsonomy.org/bibtex/2b596e5e607db5712b06fb3bfa415236b/mstrohm}, description = {Frontiers | Theory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science | Big Data}, doi = {10.3389/fdata.2020.00018}, interhash = {f50223945a3ce68cf057e2c4079a9288}, intrahash = {b596e5e607db5712b06fb3bfa415236b}, journal = {Frontiers in Big Data}, keywords = {Machine-learning fairness theory}, month = may, publisher = {Frontiers Media {SA}}, timestamp = {2020-06-02T23:47:57.000+0200}, title = {Theory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science}, url = {https://doi.org/10.3389%2Ffdata.2020.00018}, volume = 3, year = 2020 }