men also like shopping

58 Pages·2017·5.87 MB·Norwegian

MEN ALSO LIKE SHOPPING
REDUCING GENDER BIAS AMPLIFICATION
USING CORPUS-LEVEL CONSTRAINTS
1,3 1 2,4 1 1,3
Jieyu Zhao , Tianlu Wang , Mark Yatskar , Vicente Ordonez , Kai-Wei Chang
1 University of Virginia 2 University of Washington 3 UCLA 4 Allen Institute for AI
( me )
1
Dataset Gender Bias
33% 66%
Male
Female
imsitu.org
2
Model Bias After Training
16% 84%
Male
Female
imsitu.org
3
Why does this happen? Good for accuracy
4
Algorithmic Bias in Grounded Setting
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cooking
dusting
faucet
fork
World Dataset Model
Algorithmic Bias in Grounded Setting
woman cooking
} }
cooking
dusting
faucet
fork
World Dataset Model
Algorithmic Bias in Grounded Setting
woman cooking man fixing faucet
} }
cooking
dusting
faucet
fork
World Dataset Model
Algorithmic Bias in Grounded Setting
} }
cooking
dusting
faucet
fork
Dataset
World Model RBA
Algorithmic Bias in Grounded Setting
Reduce amplification ~50%
Negligible loss in performance
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cooking
dusting
faucet
fork
Dataset
World Model RBA
Contributions
imSitu vSRL COCO MLC
(events) (objects)
High dataset gender bias
data
38% (objects) 47% (events) exhibit strong bias
Models amplify existing gender bias
model
~70% objects and events have bias amplification
Reducing bias amplification
RBA
~50% reduction in amplification
Insignificant loss in performance