Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantifying Societal Bias Amplification in Image Captioning   We study societal bias amplification in image captioning. Image captioning
models have been shown to perpetuate gender and racial biases, however, metrics
to measure, quantify, and evaluate the societal bias in captions are not yet
standardized. We provide a comprehensive study on the strengths and limitations
of each metric, and propose LIC, a metric to study captioning bias
amplification. We argue that, for image captioning, it is not enough to focus
on the correct prediction of the protected attribute, and the whole context
should be taken into account. We conduct extensive evaluation on traditional
and state-of-the-art image captioning models, and surprisingly find that, by
only focusing on the protected attribute prediction, bias mitigation models are
unexpectedly amplifying bias.

35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc