TY - GEN
T1 - Biased Embeddings from Wild Data
T2 - Measuring, Understanding and Removing
AU - Sutton, Adam
AU - Lansdall-Welfare, Thomas
AU - Cristianini, Nello
PY - 2018/10/5
Y1 - 2018/10/5
N2 - Many modern Artificial Intelligence (AI) systems make use of data embeddings, particularly in the domain of Natural Language Processing (NLP). These embeddings are learnt from data that has been gathered "from the wild" and have been found to contain unwanted biases. In this paper we make three contributions towards measuring, understanding and removing this problem. We present a rigorous way to measure some of these biases, based on the use of word lists created for social psychology applications; we observe how gender bias in occupations reflects actual gender bias in the same occupations in the real world; and finally we demonstrate how a simple projection can significantly reduce the effects of embedding bias. All this is part of an ongoing effort to understand how trust can be built into AI systems.
AB - Many modern Artificial Intelligence (AI) systems make use of data embeddings, particularly in the domain of Natural Language Processing (NLP). These embeddings are learnt from data that has been gathered "from the wild" and have been found to contain unwanted biases. In this paper we make three contributions towards measuring, understanding and removing this problem. We present a rigorous way to measure some of these biases, based on the use of word lists created for social psychology applications; we observe how gender bias in occupations reflects actual gender bias in the same occupations in the real world; and finally we demonstrate how a simple projection can significantly reduce the effects of embedding bias. All this is part of an ongoing effort to understand how trust can be built into AI systems.
KW - Fairness in AI
KW - Bias in data
KW - Artificial intelligence
KW - Natural language processing
KW - Word embeddings
UR - https://arxiv.org/abs/1806.06301
U2 - 10.1007/978-3-030-01768-2_27
DO - 10.1007/978-3-030-01768-2_27
M3 - Conference Contribution (Conference Proceeding)
SN - 9783030017675
T3 - Lecture Notes in Computer Science
SP - 328
EP - 339
BT - Advances in Intelligent Data Analysis XVII
PB - Springer, Cham
ER -