Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender
Authors: Ahmed Sabir
Lluís Padró
Venue: EMNLP 2023 Findings (2023)
Keywords: image captioning
visual grounding
gender bias

Abstract: In this paper, we investigate the impact of objects on gender bias in image captioning systems. Our results show that only gender-specific objects have a strong gender bias (e.g., women-lipstick). In addition, we propose a visual semantic-based gender score that measures the degree of bias and can be used as a plug-in for any image captioning system. Our experiments demonstrate the utility of the gender score, since we observe that our score can measure the bias relation between a caption and its related gender; therefore, our score can be used as an additional metric to the existing Object Gender Co-Occ approach.

Women Wearing Lipstick: Measuring the Bias Between an Object and Its Related Gender
Ahmed Sabir Lluis Padró
Universitat Politècnica de Catalunya, TALP Research Center, Barcelona, Spain asabirêcs. upc. edu, padroêcs. upc. edu
Abstract
To close the full picture of gender bias in image captioning, in this work, unlike other works, we ex- amine the problem from a visual semantic relation perspective. We therefore propose a Gender Score via human-inspired judgment named Belief Revi- sion (Blok et al., 2003) which can be used to (1) discover bias and (2) predict gender bias without training unbalancing the dataset. We conclude our contributions are as follows: (1) we investigate the gender object relation for image captioning at the word level (i.e. object-gender) and the sentence level with captions (i.e. gender-caption); (2) we propose a Gender Score that uses gender in relation to the visual information in the image to predict the gender bias. Our Gender Score can be used as plug-in for any out-of-the-box image captioning system. Figure shows overview of the pro- posed gender bias measure for image captioning.
this paper, we investigate the impact of objects on gender bias in image captioning systems. Our results show that only gender- specific objects have a strong gender bias (e.g. noomen-lipsticke In addition, propose vi- sual semantic-hasee gender score that measures the degree of bias can used a plug-in for any image captioning system. Our exper- iments demonstrate the utility of the gender score, since observe that our score can mea- sure the bias relation between a caption and its related gender; therefore, our score can be used additional metric the existing Oh- ject Gender Co-Occ approach. Code and data are publicly available https: thub. con/ ahmedssabir Scoee
1 Introduction
Visual understanding of image captioning is an im- portant and rapidly evolving research topic (Karpa- thy and Fei-Fei, 2015; Anderson et al., 2018). Recent approaches predominantly rely Trans- former (Huang al., 2019; Cho et al., 2022) and the BERT based pre-trained paradigm (Devlin et al., 2019) to learn cross-modal representation (Li et al., 2020; Zhang et al., 2021; Li et al.. 2022, 2023).
Visual Context Information
In this work, we investigate the relation between gender bias and the objects that are mainly used in image captioning systems, and more precisely, the widely used manually annotated image cap- tion datasets: Flickr30K (Young et al., 2014) and COCO dataset (Lin et al., 2014). However, we fo- cus mainly the COCO dataset the most used dataset in gender bias evaluation. COCO Captions is unbalanced gender bias dataset with 1:3 tio bias towards men (Zhao al., 2017; Hendricks et al., 2018; Tang et al., 2021). The dataset contains around 120K images, and each image is annotated with five different human-written captions.
While image captioning models achieved no- table benchmark performance in utilizing the cor- relation between visual and co-oceurring labels to generate an accurate image description, this often results in gender bias that relates to specific gender, such as confidently identifying a woman when there is a kitchen in the image. The work of (Zhao et al., 2017) tackles the problem of gender bias in visual semantic role labeling by balancing the distribution. For image captioning, Hendricks et al. (2018) consider ameliorating gender bias via balance classifier. More recently, Hirota al. (2022) measured racial and gender bias amplifica- tion in image captioning via a trainable classifier on an additional human-annotated annotated existing caption dataset for the gender bias task (Zhao et al., 2021).
To obtain the visual context from each image 1, we use out-of out-of-the-boo bhx classifiers to extract the image context information o(I). Specifically, fol- lowing (Sabir al., 2023), the objects extracted from all pre-trained models are obtained by ex- tracting the top-3 object class/category (excluding person category) from each classifier after filtering out instances with (1) the cosine distance between

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Sereence Firbedding
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Langrage Model
Longuage 30ode
Gender
(a)
(b)
[c)
[d)
Figure An overview of the proposed gender bias measure image captioning. (a) word level with word embedding, (b) sentence level with semantic embedding. (c) our proposed gender score that relies visual bias
revision (with GPT-2 for initialization P (gg) (common observation as initial bias without visual) and with BERT for similarity score), and (d) the proposed MASK gender estimation (prediction), also with visual bias revision.
objects (voting between classifiers of top-k) and (2) a low confidence score via a probability threshold 0.2. We next describe each of these classifiers:
i.e. caption y from the image and its related gen- der a' E {man, woman, person). In this context, we refer to the Cosine Distance as the closest dis- tance (i.e. semantic relatedness score) between the gender-to-object via similarity measure e-g. cosine similarity. We apply our analysis to the most widely used human- annottedd datasets image captioning tasks: Flikr30K and COCO Captions datasets. We employ several commonly used pre-trained models: (1) word-level: GloVe (Pennington et al., 2014), fasttext (Bojanowski et al., 2017) and word2vec (Mikolov et al., 2013) out-of-the- box as baselines, then we utilize Gender Neutral Glo (Zhao et al., 2018), which balances the gender bias; (2) sentence-level: Sentence-BERY (Reimers and Gurevych, 2019) tuned on Natural Language Infer- ence (NLI) (Conneau et al., 2017), (2b) SimCSE (Gao et al., 2021) contrastive learning supervised by NLI, and an improved SimCSE version with additional Information aggregation via masked lan- guage model objective InfoCSE (Wu et al., 2022). In particular, we measure the Cosine Distance from the gender a' to the object in Figure (a, b) (e.g. how close the gender vector of a' is to the object o umbrella). Table shows the top most object-gender frequent count with the standard and gender-balanced GloVe, respectively. Gender Bias Ratio: we follow the work of (Zhao et al., 2017) to calculate the gender bias ratio to- wards men
(Biased)
GloVe Dist (Balanced)
person
person
woman
face
walking
lipatick
can
police
police
hand
cowboy
dishwasher
police
white
black
ballplayer
gorilla
sleeping
go
walking
white
mailbox
teddy
harse
walking
black
book
ballplayer
bathtub
black
car
walking
Table 1: Most frequency count of object + gender in the COCO Captions training dataset via Cosine Distance with Ve and balanced Gender Neutral GN-GloVe.
ResNet-152 (He et al., 2016): A residual deep net- work that is designed for ImageNet classification tasks, which relies heavily on batch normalization.
CLIP (Radford et al., 2021): (Contrastive Language-Inan Image Pre-training) is a pre-trained model with contrastive loss where the pair of image- text needs to be distinguished from randomly se- lected sample pairs. CLIP uses Internet resources without human annotation on 400M pairs.
-reptiionResNee FRCNN (Huang et al., 2017): is improved variant of Faster R-CNN, with a trade-off of better accuracy and fast inference via high-level features extracted from Inception- ResNet. a pre-trained model that is trained on COCO categories, with 80 object categories.
caption y as shown
3 Gender Object Distance
The main component of our Gender Score (next section) is the semantic relation between the ob- ject and the gender. Therefore, in this section, we investigate the semantic correlation between the object and the gender in the general dataset (e.g. wiki). More specifically, we assume that all images contain a gender man, woman or gender neutral person, and we aim to measure the Cosine Dis- tance between the object o, objects with context
s(obj, m)
biasto-m s(obj. m)) + sobj, w)
where m and refer the gender in the image, and the is our proposed gender-to-object semantic relation bias score. In our case, we also use the score to compute the ratio to gender neutral person:
s(obj. m/w) person)
Ration-n

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COCO Captions
FK
Avg: Gender Object Distance
Ratio
Avg: Gender Object Distance
Ratio
Model
person
lo-w
person
wommn
to-m
In-w
Verd2Vee (Mikolov et al., 2013)
0.101
0.116
0.124
0.48
0.51
0.116
0.142
0.154
0.47
0.52
Vo (Pennington et al., 2014)
0,146
0.175
0.169
0.50
0,49
0.131
0.170
0.168
0,50
0.49
Fasttext (Bojanowski et al., 2017)
0.180
0.200
0.191
0.51
0.48
0.146
0.196
0.191
0,50
0.49
GN-GloVe (Zhao et al. 2018)
0.032
0.055
0.054
0.50
0.49
0.024
0.085
0.088
0.49
0.50
SBERT (Reimers and Gurevych, 2019)
0.124
0.155
0.128
0.54
0.45
0.121
0.167
0.129
0.56
0.43
SinCSSE-RoBERT (Gao al, 2021)
0.194
0.137
0.093
0.59
0.40
0.189
0,140
0.107
0.56
0.43
2022)
0.199
0.222
0.211
0.51
0.48
0.228
0.265
0.241
0.52
0.47
Table 2:
Result of Average Cosine Distance between gender and object in the COCO Captions and Flikr3KK training
datasets. The ratio is the gender bias rate towards men/vomen. The results show there is a slight bias toward men. GloVe and Ve (balanced) show identical results COCO Captions, which indicate that not all objects have a strong bias toward specific gender. In particular, regarding non-biased objects, both models exhibit a low/simlaar bias ratio e.g. bicycle-gender (Glo Ve: m=0.31 w=0.27, ratio=0.53) and (GN-Glo Ve: m=0.15 w=0.13, ratio=0.53).
Avg: Gender Score
Bius Ratio
Leakage
Model
person
+ man
woman
m
w
to-m
w
Human
930
291
Gender Object Distance
Transformer (Vaswani et al., 2017)
0.075
0.062
0.033
0.82
0.44
0.65
0.85
1.40
AoANet (Huang ct al., 2019)
0.079
0.061
0.047
0.77
0.59
0.56
0.82
1.29
Vilbert (Lu et ul., 2020)
0.089
0.070
0.056
0.78
0.62
0.55
0.75
1.06
OSCAR (Li al.. 2020)
0.076
0.054
0.044
0.71
0.57
0.58
0.93
1.40
BLIP (Li et al., 2022)
0.068
0.050
0.040
0.73
0.58
0.55
0.83
1.32
TraCLIPS- Roward (Cho et al., 2022)
0.069
0.053
0,040
0.76
0.57
0.56
1.30
BIJIP-2 (1.i 2023)
0.073
0.049
0.043
0.67
0.58
0.53
0.73
1.22
Gender Score
Transformer (Vaswani et al. 2017)
0.201
0.194
0.187
0.96
0.93
0.50
0.85
1.40
AoANet (Huang et al., 2019)
0.217
0.210
0,201
0.96
0.92
0.51
0.82
1.29
Vilbert (Lu et al., 2020)
0.225
0.217
0,207
0.96
0.92
0.51
0.75
1.06
OSCAR (1.á al., 2020)
0.196
0.188
0.181
0.95
0.92
0.50
1.40
BLIP (Li al., 2022)
0.226
0.220
0.213
0.97
0.94
0.50
0.83
1.32
CruCLIPS-RReair (Cho et al., 2022)
0.198
0.194
0.185
0.97
0.93
0.51
0.82
1.30
BLIP-2 (Li al., 2023)
0.224
0.215
0.210
0.95
0.93
0.50
0.73
1.22
Table 3: Result of the Average Cosine Distance and Gender Score, between gender and object information, on the Karpathy test split. Our score balances the bias better than direct Cosine Distance, primarily because not all objects exhibit strong gender bias. The leakage is comparison between human-annotated caption and the classifier output that uses the gender-related object to influence the final gender prediction, such as associating women with food.
4 Gender Score
where P(gy) is the hypothesis probability of y. D is the predicted captions with the gender a, and P(c.) is the probability of the evidence that causes hypothesis probability revision i.e. visual bias revi- sion via object context o from the image I, o(T): Hypothesis: P(gy) (caption y with the gender a)
In this section, we describe the proposed Gender Score that estimates gender based on its semantic relation with the visual information extracted from the image. Sabir et al. (2022) proposed a caption re-ranking method that leverages visual semantic context. This approach utilized Belief Revision (Blok et al., 2003) to convert the similarity (i.e. Cosine Distance) into probability measure. Belief Revision. To obtain likelihood bias revisions based on similarity scores (i.e. gender, object), need three parameters: (1) Hypothesis (g): caption y with the associated gender E {man, woman), } (2) Informativeness (c): image object information o confidence and (3) Similarities: the degree of relatedness between object and gender sim o).
Informativenes: 1 - P(co) (object context 0)
Similarities: a 1 [-sim(y,o) 1-P(co)
(visual bias)
1=sim(y,0)
The visual context P(c.) will revise the caption with the associated gender P(gy) (i.e. gender bias) if there is semantic relation between them sim(y, o). We discuss each component next:
Hy pothesis initial bias: In visual-based belief revi- sion, one of the conditions is to start with an initial hypothesis and then revise it using visual context and a similarity score. Therefore, we initialize the caption hypothesis P(gy) with a common observa- tion P(g))) such as a Language Model (LM) (we
(v)
1 D
Σ
P ( co) = P(g,)"
(3)
(D.D)DD

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Bins Ratio Toward Men
Bias Ratio Toward Women
Model
skateboard
kitchen
moooorcycle
baseball
skateboard
kitchen
mootorcycle
baseball
Object Gender Co-Oce (Zhao al., 2017)
Transformer (Vaswani et al., 2017)
0.96
0.50
0.83
0.75
0.05
0.50
0.16
0.25
AoANer (Huang et al., 2019)
0.97
0.85
0.81
0.02
0.14
0.18
Vilhem aI et al., 2020)
0.96
0.47
0.84
0.66
0.03
0.52
0.15
0.33
OSCAR (1á et al,, 2020)
0.97
0.58
0.82
0.90
0.02
0.41
0.18
0.09
BLIP (Li ot al., 2022)
0.96
0.52
0.88
0.97
0.03
0.47
0.11
0.02
TraCLIPS-Rewad (Cho al., 2022)
0.89
0.48
0.93
0.50
0.10
0.51
0.06
0.50
BLIP-2 (Li al.,, 2023)
0.94
0.57
0.88
0.90
0.05
0.42
0.11
0.10
Gender Score
Transformer (Vaswani et ol., 2017)
0.96
0.51
0.83
0.61
0.03
0.48
0.16
0.38
AnANet (Huang et al., 2019)
0.97
0.46
0.84
0.82
0.02
0.53
0.15
0.17
Vilber (La et al., 2020)
0.96
0.65
OSCAR (Li et al., 2020)
0.98
0.42
0.78
0.83
0.01
0.57
0.21
0.16
BLIP (Li et 2012)
0.96
0.50
0.86
0.98
0.03
0.49
0.13
0.01
TraCLIPSS- Reward (Cho et al., 2022)
0.88
0.43
0.92
0.50
0.11
0.56
0.07
0.49
RI.IP-2 (1.i 2023)
0.89
0.10
Table 4: Example of the most common gender bias objects in COCO Captions, Karpathy test split. The result shows that our score (bias ratio) aligns closely with the existing Object Gender Co-Occ approach when applied to the most gender-biased objects toward men. Note that TraCLIPS-Rewarr (CLIPS+CIDEr) inherits biases from RL-CLIPS. resulting in distinct gender predictions and generates caption w/o specific gender i.e. person, baseball player, etc.
consider this as an initial bias without visual). We employ (GPT-2) (Radford et al., 2019) with mean token probability since it achieves better results. Informativeness of bias information: As the vi- sual context probability P(c,)) approaches and in consequence is less informative (very frequent ob- jects have discriminative power since they may co-occur with any gender)
Transformer (Vaswani al., 2017) with bottom-up top-down features (Anderson et al., 2018), AoANet (Huang et al., 2019), Vilbert (Lu et al., 2020), OSCAR (Li et al., 2020), BLIP (Li et al., 2022).
Transformer with Reinforcement Learning (RL) CLIPS+CIDEr as image+text similarity Reward (TraCLIPS-Rens Reward) (Cho et al., 2022) and Large LM2.7B based BLIP-2 (Li et al., 2023). Note that for a fair comparison with other pre-trained mod- els, OSCAR uses a cross-entropy evaluation score rather than the RL-based CIDEr optimization score. Data. Our gender bias score performed on the (82783 x 5 human annotations) COCO Captions dataset. For baselines (testing), the score is used to evaluate gender-to-object bias on the standard 5K Karpathy test split images (Karpathy and Fei-Fei, 2015) (GT is the average of five human bias ratios),
P(c,) approaches zero, causing a to get closer to I, and thus, a smaller revision of P(dy)) Therefore, as we de- scribed in the Visual Context Information Section 2, we leverage a threshold and semantic filter visual context dataset from ResNet, Inception-Restee ResNet v2 based Faster R-CNN object detector, and CL.IP to extract top-k textual visual context information from the image. The extracted object is used to measure the gender-to-object bias direct relation. Relatedness between hypothesis and bias infor- mation: Likelihood revision occurs if there is a close correlation between the hypothesis and the new information. As the sim o) (gender, object), gets closer to 1 (higher relatedness) a gets closer O, and thus hypothesis probability is revised (i.e. gender bias) and raised closer to 1. Therefore, the initial hypothesis will be revised or backed off to I (no bias) based on the relatedness score. In our case, we employ Sentence- BERT to compute the Cosine Distance (Section 3) by using object o as context for the caption y with associated gender a.
Our experiments apply visual semantics between the object object with context i.e. caption and its related gender information to predict object-to- gender related bias. The proposed iisuallgender scores are (1): Cosine Distance, which uses the sim- ilarity score to try to estimate the proper gender as shown in Table 2; (2) Gender Score, which carries out Belief Revision (visual bias likelihood revision) to revise the hypothesis initial bias via similarity e.g. Cosine Distance (gender, object). For our base- line, adopt the Object Gender Co-Oce metric (Zhao al., 2017) for the image captioning task.
5 Experiments
In this work. we hypothesize that every image has a gender C (man, woman} or gender neutral (i.e. person), and our rationale is that cach model suffers from Right for the Wrong Reasons (Hen-
Caption model. We examine seven of the most recent Transformer state-of ofte-oo-tth--e the caption models:

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Visual: tennis
Gender
Bins Ratio
Caption: < MASK > hitring tennis hall tenmis court
Model
man
woman
to-m
to-w
Gender Object Distance:
Object Gender Co-Oco (Zhao et al,, 2017)
man 0.44
woman 0.46
Transformer
792
408
0.66
0.34
Gender Score:
AoANet
770
368
0.67
0.32
man 0.45
woman 0.45
Vilbert
702
311
0.69
0.30
Visual: umbrella
OSCAR
845
409
0.67
0.32
Caption: < MMSK > holding
BLIP
775
0,66
0.33
umbrella in
TraCLIPS Reward
769
381
0.66
0.33
Gender Object Distance:
BLIP-2
695
356
0,66
0.33
woman 0.20
Gender Score (Gender Score Estimation)
Gender Score: woman 0.13
Transformer
616
217
0.73
0.26
AnANet
527
213
0.71
0.28
Visual: paddle
Vilbert
526
161
0.76
0.23
Caption: <MMMM riding wave
OSCAR
630
237
0.72
0.27
on top of a surfboard
BLIP
554
0.69
0.30
Gender Object Distance:
TraCLIPS Reward
537
251
0.68
0.31
man 0.16 woman 0.11
BLIP-2
498
239
0.67
0.32
Gender Score:
man 0.35
woman 0,30
Table 5: Comparison results between Object Gender Co-
Examples of Gender Score Estimation and Cosine Distance. The result shows that (Top) the score balances the bias (as men and women have similar bias for the sport tennis), (Middle) a slight bias object um- brella toward women, and (Bottom) men strong object bias relation (paddle, surfboard), the model adjusts the women bias while preserving the object gender score.
Figure 2:
Occ (predicted gender-object results) and our gender score estimation on Karpathy test split. The proposed score measures gender bias more accurately, particularly when there a strong object to gender bias relation.
is to measure the amplified gender-to-object bias in pre-trained models. Table 5 shows that the fill-in gender score has more bias towards men results than object-gender pair counting. The rationale is that the model can estimate the strong gender object bias as shown in Figure 2, including the false positive and leakage cases by the classifier. Discussion. Our approach measures the amplified gender bias more accurately than the Object Gen- der Co-Oce (Zhao et al., 2017) in the following two scenarios: (1) where the gender is not obvious in the image and is misclassified by the caption base- line, and (2) when there is leakage by the classifier. In addition, unlike the Object Gender Co-Oce, our model balances the gender to object bias and only measures a strong object to gender bias relation as shown in Figure 2. For instance, the word "hitting" in a generated caption (as a stand-alone without context) is associated with bias toward men more than women, and it will influence the final gender- to-caption bias score. However, our gender score balances the unwanted bias and only measures the pronounced gender to object bias relation.
dricks et al., 2018) or leakage (Wang et al., 2019) (see Table 3), such as associating all kitchens with women. Therefore, we want to explore all the cases and let the proposed distance/score decide which gender (i.e. bias) is in the image based on visual bias. In particular, inspired by the cloze probabil- ity last word completion task (Gonzalez-Marquez, 2007), we generate two identical sentences but with a different gender, and then we compute the like- lihood revisions between the sentence-gender and the caption using the object probability. Table shows that GloVe and GN-GloVe (balanced) have identical results on COCO Captions dataset, which indicate that not all objects have a strong bias to- ward specific gender. In addition, Table shows that our score balances the bias of the Cosine Dis- tance and demonstrates, on average, that not all ob- jects have strong gender bias. Also, our approach detects strong specific object-to-gente has a similar result to the existing Object Gender Co-Occ method on the most biased object toward men, as shown in Table 4. TraCLIPS-Reww herits biases from RL-CLIPS and thus generates caption w/o specific gender (e.g. person, guy, etc). Therefore, we adopt the combined CLIPS+CIDLr Rewards, which suffer less gender prediction error.
bias and
Conclusion
in-
In this work, we investigate the relation between objects and gender bias in image captioning sys- tems. Our results show that not all objects exhibit a strong gender bias and only in special cases does an object have a strong gender bias. We also propose a Gender Score that can be used as an additional met- ric to the existing Object-Gender Co-Oce method.
Gender Score Estimation. this experiment, we MASK the gender and use the object with context to predict the gender as shown in Figure 2. The idea

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Limitations
Sergey Blok, Douglas Medin, and Daniel Osherson. 2003. Probability from similarity. In AAAI
The accuracy of our Gender Score heavily relies on the semantic relation (i.e. Cosine Distance) be- tween gender and object, high or low degree of similarity score between them can influence the final bias score negatively. Also, our model relies on a large pre-trained model(s), which inherently encapsulate their own latent biases that might im- pact the visual bias revision behavior in gender- to-object bias scenarios. Specifically, in cases when there are multiple strong bias contexts (i.e. non-objects context) with high similarity scores toward a particular gender present within the cap- tion. This can imbalance the final gender-to-object score, leading errors in gender prediction and bias estimation. In addition, the false positive ob- ject context information extracted by the visual classifier will result in inaccurate bias estimation.
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. TACL.
Jaemin Cho, Seunghyun Yoon, Ajinkya Kale, Franck Dernoncourt, Trung Bui, and Mohit Bansal. 2022. Fine-grained image captioning with clip reward. arXiv preprint arXiv:2205 131155
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loic
Barrault, and Antoine Bordes. 2017. Supervised learning of universal sentence representations from natural language inference data. arXiv preprint arXiv:1705.02580
Jacob Devlin, Ming-Was Weng Chang. Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understand- ing. In NAACL-HLT.
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. SimCSE: Simg of sentence embeddings. arXiv preprint arXiv:2104.08821.
Ethical Considerations
Monica Gonzalez- 2007. Methods in cognitive linguistics, volume 18. John Benjamins Publishing.
We rely upon existing range of well-known pub- liely available caption datasets crawled from the web and annotated by humans that assume bi- nary conceptualization of gender. Therefore, it is important to acknowledge that within the scope of this work, we are treating gender as strictly binary (i.e. man and woman) oversimplifies a complex and multifaceted aspect of human identity. Gen- der is better understood as a spectrum, with many variations beyond just two categories, and should be addressed in future work. Since all models being trained on these datasets, we anticipate all models contain other biases (racial, cultural, etc.). For example, an observation Table 1, when we remove gender bias, we notice the emergence of another bias, such as racial bias. For example, vec- tors representing Black person women are closer together than those representing other colors, like white. Moreover, there is another form of bias that has received limited attention in the literature, the propagation of gender and racial bias via RL (e.g. RL-CLIPS). For instance, in Figure I the model associates gender with race, as "asian woman".
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Reviews:
[{"review_id": "Kw7poadNn2", "reviewer": "Reviewer_vgnb", "summary": "", "questions": "", "limitations": "", "rating": 2, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are grateful to the reviewer for the constructive comments. Please find the response to your comments below:\n\n- (Q1,2)  Regarding the comments about the framework diagram that should be included in the main text, small font sizes, and lack of formal expressions for the mathematical equations.\n\n(A1,2) We thank the reviewer for pointing this out. We will improve the presentation of the paper, and we will add the framework diagram to the main text from the appendix.\n\n- (Q3) Regarding the comment: the paper only compares the proposed gender score with the cosine distance.\n\n(A3) As the reviewer mentioned, our work only incorporates cosine distance in the proposed framework. However, the sole viable approach to compare against the likelihood revisions is the cosine distance (_i.e._ measuring the degree of the gender to object semantic relation bias before the object to gender bias likelihood revisions (gender score)). Additionally, in the attached appendix (Table 6 and Table 9), we report additional results comparing our approach against general baselines as a stand-alone, such as Language Model (GPT-2) and Mask Language Model (BERT).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1) The paper addresses an important and timely issue of gender bias in image caption systems, particularly focusing on the impact of gender-specific objects. \n2) The introduction of a visual semantic-based gender score is a novel contribution that offers a practical and quantifiable metric for measuring the degree of gender bias in generated captions. This score can be easily integrated as a plug-in into existing image captioning systems, providing a standardized approach for bias evaluation.\n3) The paper demonstrates the utility of the proposed gender score by showing its effectiveness in measuring the bias relationship between captions and gender. Moreover, the gender score serves as an additional metric to complement the existing Object Gender Co-Occ approach, enhancing the comprehensiveness of bias evaluation.", "reasons_to_reject": "1) The main body of the paper appears too short, and several important content elements, such as the framework diagram and experimental control groups, should be included in the main text. Additionally, the paper lacks formal expressions for the mathematical equations, which could diminish the clarity and rigor of the presented methods.\n2) Some tables have too small font sizes, and the lack of bottomlines reduces the readability and visual appeal of the paper. A clear presentation of results with appropriate font sizes and table formatting is essential for conveying findings effectively.\n3) The paper only compares the proposed gender score with the cosine distance, without exploring more suitable or updated comparison methods， which would weaken the credibility and generalizability of the proposed gender score.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper investigates the impact of objects on gender bias in image caption systems. The main problem addressed is the presence of gender bias in the generated captions, particularly when gender-specific objects are involved. The paper contributes to the understanding of gender bias in image captioning systems by investigating the gender object relation and proposing a visual semantic-based gender score. The gender score offers a practical tool for measuring bias and can be easily incorporated into image captioning models for improved evaluation and mitigation of gender bias.", "excitement": 2, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "h8m4jQrfbF", "reviewer": "Reviewer_EYYR", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the reviews and questions. Please find the response to your questions below:\n\n- (Q1,2) Regarding the questions: What is difference between gender of word and caption? and comparing our methods with other works?\n\n(A1,2)  In the gender-to-object approach, we investigate the amplified gender-to-object bias relation for image captioning at the word level and without the word context. In contrast, the gender-to-caption relation method delves into the bias relation at the sentence (caption) level with the surrounding word context that might influence the final gender bias score. For instance, the word “sitting” in a generated caption  (as a stand-alone without context) is associated with a bias toward “women” more than “men”, and it will influence the final gender-to-caption bias score. However, our gender score balances the unwanted bias and only measures the pronounced gender to object bias relation. Additionally, compared to other works, our semantic-based gender score can predict gender bias without unbalancing the dataset and can be easily incorporated into any off-the-shelf image captioning system.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Table 2 shows that the method balances bias better than the cosine distance metric. \n\nSimilarly, Table 4 also shows that the gender bias is more accurate especially for classifiers that misrecognise gender..", "reasons_to_reject": "Pg 1, right col, line no 047, what is difference between gender of word and caption?", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper looks at the bias between and object and its gender in a caption. They make use of pre-trained model Inception for object segmentation. They also use a pre-trained language model such as MASK.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "How does the method compare with other image segmentation metrics. For example, see ‘Gaussian correction for Adversarial learning of Boundaries.’ 2022 Signal Processing-Image Communication"}, "extra_text": {}}, {"review_id": "4yJH0Z53Z3", "reviewer": "Reviewer_tUEX", "summary": "", "questions": "Under what circumstances do you recommend one to use Gender Score instead of Object Gender Co-Occ? Or, when would one need Gender Score in addition to Object Gender Co-Occ?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the reviews and question. Please find the response to your question below:\n\n- (Q1) Regarding the question: Under what circumstances do you recommend one to use Gender Score instead of Object Gender Co-Occ? \n\n(A1) Our approach measures the amplified gender bias more accurately than the Object Gender Co-Occ in the following two scenarios: (1) where the gender is not obvious in the image and is misclassified by the caption baseline, and (2) when there is leakage by the classifier. In addition, unlike the Object Gender Co-Occ, our model balances the gender to object bias (reduces the bias as shown in Figure 6 and Figure 7) and only measures a strong object to gender bias relation. Therefore, our approach can be used in the aforementioned scenarios as a stand-alone metric or as an additional metric alongside the existing Object Gender Co-Occ approach. The trade-off is that our model relies on a large pre-trained model(s), which carry their own inherent biases that might influence the bias score. However, as the reviewer recommended, we will add a dedicated discussion section for direct comparison and trade-off between the Gender Score and Object Gender Co-Occ.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper explores the use of cosine distance in measuring gender bias of image captioning and presents Gender Score as the new gender bias metric. The paper shows that the new method performs better than the prior art Object Gender Co-Occ as the measure of the gender bias of image captioning systems.", "reasons_to_reject": "It wasn't clear to me the specific points that this new method complements the weaknesses of Object Gender Co-Occ. In Table 4, it is mentioned that the Gender Score measures the bias more accurately. It will be more clear to the reader how/why that might be possible. It was also unclear the tradeoffs one would make to choose Gender Score over Object Gender Co-Occ either. \n\nUPDATE: Given that the authors will add a separate discussion section to go in more details of the comparison, this may be OK. Authors have provided a high level comparison in the response too.", "questions_to_authors": "Under what circumstances do you recommend one to use Gender Score instead of Object Gender Co-Occ? Or, when would one need Gender Score in addition to Object Gender Co-Occ?", "paper_topic_and_main_contributions": "The paper proposes a new gender bias metric called Gender Score for image captioning task.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes an analysis of how objects may be implicitly associated with genders in image captioning models. The analysis recovers traditional gendered associations, e.g., lipstick with women. The paper also presents a metric that can be used to evaluate gender bias in object representations. I would suggest adding some discussion of the limitations of studying gender as a binary category and of autoamtic gender classification in general.
Decision: Accept-Findings