Algorithmic bias has the capacity to amplify and perpetuatesocietal bias, and presents profound ethical implications for society. Gen-der bias in algorithms has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to ad-dress such issues have involved testing learned associations, integrating concepts of fairness to machine learning , and performing more rigorous analysis of training data. Mitigating bias when algorithms are trainedon textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning.The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact in the context of search and recommender systems .
Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society. Gender bias has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning.The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact .
R
Algorithmic bias
Artificial Intelligence
R
perpetuatesocietal bias,
perpetuate societal biases
R
Gen-der bias in algorithms
Gender bias
R
ad-dress
address
D
,
R
trainedon
trained on
D
in the context of search and recommender systems