Epistemological Bias As a Means for the Automated Detection of Injustices in News Media
Authors: Kenya S. Andrews
Lamogha Chiazor
Venue: Submitted to ICLR 2023 (2023)
Keywords: testimonial injustice
character injustice
framing bias
epistemological bias
news media

Abstract: Injustice occurs when someone experiences unfair treatment or their rights are violated. In the context of news media, injustices represent a form of bias through which discriminatory narratives can arise and spread. The automated identification of injustice in text has received little attention, due in part to the fact that underlying stereotypes are rarely explicitly stated and that instances often occur unconsciously due to the pervasive nature of prejudice in society. Here, we leverage the combined use of a fine-tuned BERT-based bias detection model, two stereotype detection models, and a lexicon-based approach to show that epistemological biases (i.e., words, which through their use, presupposes, entails, asserts, hedges, or boosts text to erode or assert a person's capacity as a knower) can assist with the automatic detection of injustice in text.

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EPISTEMOLOGICAL BIAS As A MEANS FOR THE AU- TOMATED DETECTION OF INJUSTICES IN TEXT
Anonymous authors
Paper under double-blind review
ABSTRACT
Injustice occurs when someone experiences unfair treatment or their rights are vio- lated. The automated identification of injustice in text has received little attention, due in part to the fact that underlying stereotypes are rarely explicitly stated and that instances often occur unconsciously due to the pervasive nature of prejudice in society. Here, we leverage the combined use of a fine-tuned BERT-based bias detection model, two stereotype detection models, and lexicon-based approach to show that epistemological biases (i.e., words, which presupposes, entails, as- serts, hedges, or boosts text to erode assert a person's 's capacity as knower) can assist with the automatic detection of injustice in text. The news media has many instances of injustice (i.e. discriminatory narratives), thus it is use case here.
INTRODUCTION
The most basic duty of the media is knowledge sharing. Yet, the tool necessary for wide-spread knowledge sharing is influence. With this influence, the media is able to shape how one will under- stand the intricacy of story-line in a news story with little effort. This often results in the use of epistemological biases which involves propositions that are presupposed. entailed, asserted, hedged, or boosted in text (Recasens et al. (013) to erode assert person's 's capacity to framing issues and injustice within the text. Particularly, some of these word choices lead to testimonial injustice and what define as character injustice. Character assassination is "the delib- erate destruction of an individual's reputation or credibility" (Icks et al., 2019). This often leads to character injustice, which is an unjustified attack on a person's 's character that results in an unfair crit- icism or inaccurate representation of them. Testimonial injustice occurs when modified believability assigned to the statement of subject based on widely known stereotypes (Fricker 2007). Both of these types of injustices can lead to affirming or perpetuating stereotypes concerning the subject. Though it has always been harsh reality with various ringing consequences, in recent years we have publicly witnessed how the affirmation of stereotypes can lead to physical violence, prejudice, and negative self-image (Harrison & Esqueda 1999), (Gover et al., 2020), (Kuykendall] [189)]. These experiences are harmful and dangerous, explicitly for the victims but for all members of our society. We also consider framing injustice. According to (Entman) 2007) framing bias happens when the use of subjective, one-sided words that reveals the stance of an author occurs. Which means that an individual's choice from a set of options is influenced more by how the information is worded, rather than by the information itself. We recognise that news content can be positively or negatively framed to influence the narratives. In this paper, we aim to show how negative cases, when it affects particular subjects or individuals, can lead to framing injustice occurring toward those subjects.
knower, leading
In this work, we seek to make room for subjects of text, even in creative writing, to not have their credibility shot or character assumed due to well-known stereotypes which are harmful and un- founded. Our proposed framework will be used to detect character, testimonial, and framing injus- tices. The framework includes a fine-tuned BERT model based on work from (Pryzant et al. 2020b) to automatically tag words associated with epistemological bias from an input text, use the (K (Kwon & Gopalan 2021) CO-STAR model and (Sap et al. (020) Social-Bias Frames to find stereotypes and the concepts of those stereotypes associated with the input text, and show when the tagged words (associated with some epistemological bias) or less credibility of a person are correlated with a stereotype which causes injustice. Though we could examples from various fields (e.g. politics, marketing, medicine, etc...), we will use news media as a use-case throughout this work. Thus, we present the following contributions:

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1. We develop a novel framework that uses the results of 3 models to detect character, testi- monial, and framing injustices in News Media.
2. We produce a fine-tuned tagger model to automatically detect epistemological bias.
3. We develop a User-Interface for journalists and editors to submit text to and receive output and explanations surrounding the tagged word, referred to in this work as tagger-UI.
4. We produce empirical evidence showing how epistemological bias can translate to injus- tices.
A goal of this work is to give journalists and editors a tool which will help them easily and quickly learn to avoid character, testimonial, and framing injustices in their work. This will be accomplished by showing users which words they use that produce epistemological bias, showing them the poten- tial stereotype associated with the tagged words and text, offering the user explainability with the help of the stereotype concepts as defined by (Kwon & Gopalan [021], and offering the user re- sources to reference literature on the particular epistemological bias type(s) identified in their input text.
im
2 BACKGROUND
Many works have established it is difficult for the common person to identify biased word in sentence and establish the need for computational agents to take on this charge. Section 2.1, 4.1, and 6.1 and Table 8 of (Pryzant et al., (2200) shows humans have low ability to detect bias and show humans perform worse than their detection model. Recassns et al. (2013) shows in Table 4 that the AMT (amazon mechanical turk)
accuracy of Humans annotators a single detected biased word. The difficulty arises due to us holding our own biases as facts and lack of education on sentence construction. However, there are specific word choices which opistemologically biased and can lead to injustices occurring. We will focus on these word choices throughout this work.
not more than 37.39% for
Great efforts have been put towards identifying potential words in text materials that could encour- age epistemological bias [Recasens et al., 2013). (Hube& & Fetahu 2019), (Pryzant al. 2020b ]. Following on from the work of (Pryzant et al. (020b) we tune- their tagger model to automat- ically tag words associated with epistemological bias from an input text. Identifying words which cause epistemological bias is a step towards awareness of social harms. This begs the question, what do we do with our new found knowledge and awareness? What kinds of implications does our use of these words impact society? What communities are affected by these word choices? These are the questions we explore in this work.
Authors Kwon & Gopalan 22021) have trained a model to detect widely-known stereotypes and the concept of those stereotypes in text materials. We leverage the results of their CO-STAR model and the Social Bias Frames model (Sap et al.] 2020) to offer some explainability of the word choices by the model. Associating a particular text with a stereotype and the concept of that stereotype is critical step towards awareness of social harms that might cause character injustice
particular individual or group. Lack of identifying the words in a sentence which imply and promote these stereotypes leaves us with the undirected burden and question of: how can we address these harms? This will be further discussed in the methods section of the paper.
Beach al. 202T) identify words in text that cause testimonial injustice in medical records of Black patients. Detecting such testimonial injustice is helpful in seeing the unjust realities of our society. They conclude the testimonial injustice that persists in these medical records has high potential of causing disparity in the quality of health care for Black patients, which correlates with findings that Black patients receive worse quality of health care (Odonkor et al.) 2021). Identifying testimonial injustice in text materials is vital to creating an environment of accountability. For accountability to be applied we must include education, which is often unexplored or left up to the user. Many users do not know where to find resources for such things, thus we provide them in our framework.
Raza et al. (002)) developed a pipeline which takes in news articles, detects and masks words that biased, and suggests words with more neutral text. Whilst the pipeline and library designed by the authors are very good and useful, they however do not consider the linguistic and epistemologi-
cal bias features discussed in (Recasens al. 2013) and used by (Pryzant et al. 2020b). Unlike
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work discussed in this paper, their pipeline produces way of distinguishing and highlight- ing epistemological bias types of any identified potential biased word in the sentence. Also, their framework does not attempt to relate potential stereotypes and stereotype concepts that might be associated with causing injustice to person or group.
METHODOLOGY
Hamborg et al. (2019) concluded that various forms of media bias is already analysed Social Sciences field and can be implemented in an automated fashion by primarily using Natural Language Processing (NLP) techniques. We leverage research methods using NLP and deep learning whilst also using analysis concepts by researchers from social sciences, who have studied media bias for decades.
We propose novel technical framework (Figure I which includes NLP models for detecting po- tential epistemological biased words and potential stereotypes along with their concepts, which we semantically link given sentence, in case, news article headline. We do this with aim of showing how the automated detection of epistemological bias can help us quickly find injustices in text. This framework made up of, but not limited to, the attributes discussed here.
"Not enough context
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Lexicon Look up
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Figure 1: Testimonial Injustice Technical Framework
Figure shows the steps required to scale the framework analyse multiple headlines.
Crawl for News Articles
Pre-process Data
Remove punctuation
Upload Framework
(csv, ploud based D8, etc...
Use Spacy to get POS Add TMI feature
Figure 2: Process of Getting Data to the Framework
3.1 TAGGER MODEL
We leverage the detection component of the modular model discussed in (Pryzant al. 2020b and refer to this as the tagger model for the rest of this paper.
The tagger model (see Figure 6 in the Appendix) takes input sentence(s) (e.g. headline text, a collection of headlines, etc...), and predicts the probability of each word the sentence(s) been biased (see Equation T and Figure T). Then it returns the word with the highest probability as the
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tagged biased word.
P = ob; + ee, + +b)
(1)
Where by E Rp is word's w semantic meaning and are the experts features as proposed by Recasens et al. (2013) and based on Equation
C R.
The detection model itself is a BERT neural sequence tagging model that has been fine-tuned to include the expert linguistic and epistemological bias features from (Recasens et al.. 2013). An example of the expert feature as listed in Table 3 of (Recasens et al.] (013) is the pari of speech (POS) tag of each word in the sentence. Another example of an expert feature from vetted experts in linguistics is "assertive verbs". The model leverages the semantic meaning of each word in the given sentence via the BERT (Kenton & Toutanova 2019) contextualised word vector. We extend the detection model further by including a feature on whether or not the sentence contained too much information (TMI). We hypothesised here that the TMI information can be considered from an English linguistic standpoint to be sentence instance which has more than 2 describing words (i.e, adjectives and adverbs) in it that do not add to its understanding but seeks to cloud the judgement of the readers. We created methods in python which makes use of the core NLP dependency tree and a tree traversal algorithm to go through sentences starting from the root node, then count the number of adjectives and adverbs in the sentence to determine if there is "TMI" or "no TMI" based on the hypothesis. TMI is not a known indicator of epistemological bias, but we acknowledge it might contribute causing doubt which leads injustice. This is why we introduce this new feature to see if it will be a contributing feature for the tagger model to detect epistemological biases. We conducted an ablation study to see the effects of adding this feature, whilst training the tagger model in Section
3.2 CO-STAR MODEL AND SOCIAL BIAS FRAMES (SBF)
The CO-STAR (COnceptualisation of Stereotypes for Analysis and Reasoning) framework allows input from the user and generates outputs of stereotypes and stereotype concepts. The Social Bias Frames model generates and classifies stereotypes associated with an inputted text. These outputs are quite simple to understand but have very complex history. The accuracy of the baseline SBF model was analyzed by looking for the demographic group the statement targeted and the implied stereotype from the statement. The accuracy was measured with BLEU-2 (group- 83.2%, stereotype - 68.2%) and Rouge-L (group- - 49.9%, stereotype 43.5%). The authors of the CO-STAR model manually evaluated their model, but did not specify the results of their evaluation. However, manually analyzed how each of these models performed on our sentences and found them to generate stereotypes which well fitted to the sentences submitted.
Since testimonial injustice occurs because of widely known stereotypes, the outputs of these models will help us inform our users of any potential stereotypes that are exacerbated by their inputted text. The potential stereotype can also be used to determine if a person's 's character has been unjustly targeted. This is because stereotypes are not based the actual truth of the particular person they are attached to and stigmatises the individual. Their presence amidst character assassination is evidence of character injustice. Stereotype concepts will offer explainability as why a certain word was tagged as being epistemologically biased.
We submit news article headlines to the CO-STAR and SBF models and receive an output of po- tential stereotypes and stereotype concepts the sentence is related to. Semantic similarity describes how closely related two items (e.g. 2 words or 2 sentences) are terms of meaning. We use se- mantic similarity to encode the list of generated sentences and the original headline sentence, then using distance metrics on the encoded vectors, c.g. using a cosine similarity metric from sentence transformers, to get the distance scores. We rank the outputs based on their semantic similarity to the headlines. The stereotype and stereotype concept most correlated with the sentence is output to the user the potential stereotype and concept which casts doubt the subject of the inputted text.
3.3 LEXICON LOOKUP
Once the tagger model has returned the top tagged word, we then proceed to automatically look up and semantically search the tagged word in the epistemological lexicons from social sciences to
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discover the epistemological bias types it is associated with. These lexicons are from the collection of datasets we discuss in the dataset section 3.5 When a tagged word is not found in the lexicons, we lemmatize said word (considering its context) find its base word and find the stem word (removing the prefixes and suffiees). We then search for the lemmatized and stemmed words in the lexicons.
3.4 INTERACTIVE INTERFACE FOR LEARNING ABOUT BIAS
We propose the use of an interactive interface for editors of media content to learn more about and mitigate any potential bias types and associated stereotypes, prior to them publishing it.
During the analysis, we first leverage the nltk python library to remove any stop words (i.e. the, etc..). We then check the sentence length. If the sentence is less than 3 words, we determine there not enough context to analyze the text for potential stereotypes. However, if we have enough context, the content is sent to the epistemological tagger model to find the word with the highest probability of bias (see Figure 7 in the Appendix). The conditions from the framework (Figure) are then implemented.
Tech Justice Bias Text Analysis Tool News Articlo
Hosdlis
C Camplca
Bin
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Claccuss
lee lew
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Figure 3: The interactive interface showing the tagged words, their associated probability for bias, the potential stereotype and stereotype concept and some explainable data created from the lexicon search.
We display the types of associated epistemological bias detected to the user and provide links to resources which explains more on the specific types of biases as shown in Figure See examples of these outputs in table and table/ojot the appendix section.
Sentence
Hendline
Subject
Meghan Markle spent a staggering 138.000 on her clothes for charity trip
Meghan
Kate Middletons 1100,000 Astonishing value the (and hus hung in wardrobe for eight years) Meghan Markles beloved avocado linked human rights abuse drought, millen- nial shame
Prince's
Meghan
Kates morning sickness cure? Prince William gifted with an avocado for prognant
Kate
Duchess
Table I: Example headline sentences used. For a full list of the examples, see
3.5 DATASET SOURCE USED
We leverage and used the bias data corpus as created by (Pryzant et al. 2020b). It contains a Wikipedia neutrality corpus showing Wikipedia articles that were annotated for neutrality by editors on Wikipedia, who adhere to NPOV (neutrality point of view) guidelines
Lexicons used in the lexicon lookup were from social science research and collated in (Pryzant al., 2020a). We compiled these lexicons into one large dictionary and included some metadata about the
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lexicon ie. source, creators, resources about the epistemological bias type, etc. Solely using an epistemological lexicon look up directly to find the biased words will be limited in its performance, for various reasons e.g. distributional shift. lexicons cannot scale well enough without requiring regular manual auditing, updating of the lexicon databases when newer forms of subtle biased words arise, etc. There are various studies which show the limitations of using just lexicon based approach alone, for example in (Cryan et al., 2020),, the authors discuss on page 8 and show in Table how lexicon approaches perform less accurately than an end end approach using BERT to detect gender based stereotypes. Therefore, it is beneficial to not solely lean on lexicons and we include it as a contributor in detecting injustices in our framework.
When it comes to Meghan Markle and Kate Middleton, the media depicts several aspects of their lives which they share differently and often can be reflective of framing or character injustices We use actual headlines (as seen in Table I from such depictions in our comparative test to further illustrate the detection and harms of these injustices. The results this comparative test are further discussed in our results section (Section
4 RESULTS AND DISCUSSIONS
4.1 TAGGER MODEL ABLATION STUDY
We performed an ablation to analyse the fine-tuned tagger model performance when there is no expert feature as proposed by Recasens et al. (2013)and when they are included. Part of the training experiment was also to determine if there are any benefits from including the TMI feature into the input data. We carry out the training on 23,000 training samples from the bias-data training set and 700 validation and 1,000 test samples. Training learning rate of 3e-5 and initially train for 10 epochs.
done using 4 CPUs and GPU. We used
Tagger Model Ablation Experiments
Kind
Evaluation Accuracy(S)
Evaluation Loss
72.54
0.0758
basic
72.39
0.0766
73.13
0.0851
75.04
0.0745
expert
74.16
0.0734
features
72.83
0.0867
74.79
0.0730
tmi
*7466
*0.0703
74.63
0.0822
Table 2: Ablation study results showing values for the first 3 epochs. Note: A high score is better for accuracy and a lower score is better for loss. The numbers highlighted in bold are the values for when the evaluation loss less than the previous step.
As shown in Table 2 and Figure 4 we observe lower training loss value between epoch 3 & 6 during the initial training using the dataset which included the TMI feature as compared to when do not. TMI also gives a better evaluation accuracy and evaluation loss compared to the others before any overfitting is observed (which causes the evaluation loss to exponentially increase from about epoch 3). This overfitting occurs result of using too many epochs during the initial training of the tagger model. When fine-tuning a BERT model, good practice as suggested by the authors of Bert is to use between 2 and 4 epochs. In light of this, and from the observations made from the initial training experiments, we save and use as the best point the tagger model after epoch 2.
4.2 COMPARATIVE TEST
We performed a comparative test illustrate how our framework shows injustices e.g. character and framing injustice. Particularly, we capture the type of epistemological bias attached to the tagged word in each entry, observe if a relevant stereotype is associated with the inputs, and observe the depiction of different subjects. In this study, we look at Meghan Markle and Kate Middleton

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Training Loss
Evaluation Accuracy
Evaluation Loss
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0.36
0.7
1.2
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0.24
0.6
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0.12
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0.4
8.88
0.0
0.3
0.05
0.04
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0.2
0.0
6
8
2
10
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Figure 4: Training and Validation Results for the Tagger Model using 10 epochs. no features the expert features as mentioned in section 3.1 was not added, with expert features combine the expert features as mentioned in section 3.1 and with expert + tmi features combine the expert features whilst using the data samples containing TMI information class no tmi or tmi
as our subjects of interest. Meghan Markle an American, Black woman member of the British royal family, by marriage, who has recently resigned from her royal duties due to abuse from the media and royal family (e.g. the Firm). Kate Middleton is a British, White woman member of the British royal family, by marriage, who has recently been promoted to Princess of Wales. Even before Meghan Markle had resigned from her royal duties, the media has sought to minimize her experience and diminish her comments and character. We often see instances of Meghan as the subject of an article and the media using sarcasm and criticism towards her. Such acts have led readers to having tainted images of Meghan and even not believing her statements or actions, thus testimonial injustices. We also see this leading to her character being questioned, thus character injustice. Yet, for the same and similar topics of concern, media members speak charmingly about Kate, thus leading to framing injustices shown. It is important to note here, that Kate Middleton and Meghan Markle shared similar positions, interests, and abilities. With this, we see them as good subjects to observe in our comparative test.
No
Subject
Tagged Bias Word
Potential Bias Type
Sentence Sentiment
1
Meghan
['staggering') ['astonishing]
['subjactives']
negative
Kate
[posiiive', 'subjectives"] ['positive,, 'sabjectives'] I'positive'. 'subjectives"]
neutral
3
Meyhan
['beloved']
negative
4
Kate
['gifted"]
neutral
Table 3: A subset of output result. (It shows the words tagged as potentially biased, the lexicon that type of bias is associated with, and the overall sentence sentiment for each headline sentence depicted in table 3 & full list of the output results table For full list of columns, please refer to the appendix A
In Figure 5 we plotted results of an experiment where we ran 10 article headlines of Meghan Markle and 10 articles of Kate Middleton through our framework. Each article chosen because it dis- cusses a topic which is common between each of the two subjects and allows us to see the differences in how the two subjects are spoken about concerning that topic. It has been acknowledged by several outlets that these particular articles which we have mentioned are unjus( Majority of these articles seen in the aforementioned articles to show these comparisons, but also few additional articles were hand chosen by our team. You can see the headlines for each subject in Table The complete result output and table column descriptions are given Appendix A In this plot, we capture the subject of the headline (Meghan/Kate), sentiment of the entire headline sentence, and the associated epistemological bias types for each headline as shown in table 3 The light blue circles represent the overall sentiment of each headline, we will refer to them as the sentiment headline level. We use the categories of positive, neutral, and negative sentiments. Within each sentiment headline level, we capture the subject of the headline, we will call this the subject level. Note that in Figure 5) Kate takes up the entire positive sentiment space, there were no examples that were detected to have positive sentiments for Meghan. Within each subject level, we capture the epistemological bias types associated with the tagged words in the headlines for each subject; we will refer to this as the bias-type level. This illustration shows how much space is filled by each subject.
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ve Sentiment
Fane
impicattess
posthorls
Sentiment
Megattive Sent ment
Heghan
eital Iatamma
lusdy sur
Sull ectives
regulhr
tegu A7
subeectiv
Full ectives
sutjectues
Figure 5: Epistemological Bias Types Comparative Test
From this plot, we can see the headline sentiment level, the negative sentiment circle mostly filled with articles about Meghan and the most common bias type is positively subjective. The intuition here is that many articles about Meghan contain sarcasm, though this is not the only indicator. There was one article about Kate which had negative sentiment and each of the bias types in her circle were identified in the headline, this is why they are all equal in size. In looking at the headline sentiment level, the positive sentiment circle is completely filled with articles about Kate and the most common bias type is implicatives. The intuition here is that, in articles which have common topics between Meghan and Kate, only Kate talked about a suggestively positive way. In looking at the headline sentiment level, the neutral sentiment circle is mostly filled with articles about Kate and the most common bias type is positively subjective and regular. Recall, regular is the epistemological bias type assigned to detected words which do not appear, nor do similar words appear the lexicon lookup. We can also observe, the articles which appear for Meghan with neutral sentiment negatively subjective in nature, informing have negative suggestions. For Kate, she is talked about in a suggestively positive way, even in the neutral sentiments. It is clear from this experiment when there topic that Meghan and Kate share the media uses words for Meghan which are sarcastic and cause framing and character injustices her subject. Both of which can contribute someone taking Meghan less seriously, thus causing testimonial injustice.
they likely not sarcastic but
No.
Subject
Stereotype(S)
Distanee(S)
Stercotype Coneept(SC)
Distanco(CS)
Meghan
personal spending hahits
0.3457
women spend money
2
Kate
should be dressed like brides
0.3259
property
0.2278
3
Meghan
feminism
0.3663
sexism
0.2523
Mighan
family law
0.3244
arab folks protected
0.2951
10
Kate
women should wear dresses
0.3131
women should wear dresses
0.3131
Megham
noyally bousekeeping are guld diggers
royally house queens are gold diggers
20
Mephan
women should dress modestly
0.3840
women should wear dresses that cover themselves
0.4047
Table 4: Table showing the potential top ranked stereotype and stereotype concept (only those with semantic distance > 0.3 to the headline shown in this table. The No column represents the headline number from Table For a full list of result see Table 7 Appendix A
Looking the potential stereotypes generated with semantic similarity distance greater than 0.3 to the headline, as shown in table appendix table 7 for the full table), we observe that 5 out of 7 of such entries related to Meghan the subject and only 2 related Kate. More interestingly,
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observed that one of the potential stereotypes relating to Meghan (personal spending habits) aimed at personal attribute. Which can be seen as an indication of a potential character assassination, thus character injustice.
4.3 POTENTIAL ISSUES ELEVATED DUE THE PROPOSAL AND MEANS TO RESOLVE THEM
A critical area for bias in systems and models' design often stem from a given human's 's intrinsic biases. They are usually a reflection of ourselves. One possible solution to this problem is to ensure that a diverse group of individuals are involved from the inception of the solutions design to the testing phase of such technical solutions- this is one reason why we gathered a diverse group of authors to be involved in the discussions presented in this paper.
A second area for bias surrounds the definition of terms and assumptions biases. A way we attempt to resolve this is by considering the existing consensus definitions of epistemological bias from the social sciences, where media bias has been studied for decades.
A third potential area for bias can occur in the training and validation datasets, as well in the models used for implementing the proposed solution. For example, the sourced datasets do not contain enough semantic information that is very reflective of the injustices considered. One possible way to combat this is to investigate ways to build more robust training datasets or leverage models that do not require lots of training examples to generalise properly.
Broader problems may arise in any situation where technology is naively applied to solve a societal issue. As envisaged, our framework should be applied as a means help people working in the media improve their output with respect to bias and injustices. However, as warned by Goodhart's s law (Manheim & Garrabrant 2018). if the measures and metrics suggested here become targets, they will cease to be useful. For example, in situations where experts deliberately bias their content the tool can become beneficial to the readers instead, so that they are aware of potential biases when reading article. However, main purpose of our proposed tool and concept is to help journalists who are aware that they might use biased terms but do so unintentionally. On the other hand, we cannot control the adoption of our tool. It will help the editors who checking for bias using manual means to quickly detect such biases.
5 CONCLUSIONS
We propose a framework which uses an epistemological tagging model, a stereotype detection model and social bias frame model, combined with semantic searching and lexicon lookup of epistemolog- ical biased word to detect character, testimonial, and framing injustice. These forms of injustices are often subtle and hard quickly detect or be aware of. We also provide empirical evidence as justification for using this framework in media settings to detect such injustices. Further, we propose an interactive interface which can aide editors and journalists of news text content by automatically detecting and explaining potential bias types and injustices that might be present in their content. We anticipate this interactive interface will encourage them to take necessary, preventative steps in avoiding unjust acts before their content is released.
6
FUTURE WORK
More Comparative Test data:- Our goal was to conduct empirical qualitative research study which shows the framework (see Figure T can be implemented to detect injustices. However, recognise that limitation of the comparative test in this paper is in using only 20 headlines, and we hope to show the scalability of the work in future studies.
Learning Better Patterns for Testimonial injustice:- Identifying a single word which poten- tially cause bias in a given sentence is only start. We acknowledge that multiple words or phrases in a given sentence might be the culprit and the ability to tag multiple words or phrases will take this work further.
Veridicality Assessment:- We intend to incorporate veridicality assessment in our frame- work to assess if a statement is actually factual, which will allow for a more accurate analysis.

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7 REPRODUCIBILITY STATEMENT
We discuss how we leverage the models, our framework and the outputs of the interactive interface of this work in section 3
To access the CO-STAR and SBF models discussed in this work direct you to the GitHub repository (Kwon & Gopalan 2021) have created https: / //git hgg.tu...... cow/kwonathan/cCooooooooo
STAR The datasets used in our work are discussed in section 3.5 Each dataset is publicly available, except one which is proprietary and used to tune the tagger model. However, the tagger model can be fine-tuned to a dataset which is relevant, ethically gathered, and appropriately constructed.
REFERENCES
Mary Catherine Beach, Somnath Saha, Jenny Park, Janiece Taylor, Paul Drew, Eve Plank, Lisa A Cooper, and Brant Chee. Testimonial injustice: linguistic bias in the medical records of black patients and women. Journal of general internal medicine, 3666):1008-1714 2021.
Jenna Cryan, Shiliang Tang, Xinyi Zhang, Miriam Metzger, Haitao Zheng, and Ben Y. Zhao. De- tecting gender stereotypes: Lexicon vs. supervised learning methods. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1-11. Association for Comput- ing Machinery, 2020. doi: 1.211455/311331.33766653 URL pottps://ett wttp:oo.gg.1111.....
331 3811 3376498
Robert M Entman. Framing bias: Media in the distribution of power. Journal of communication, 57 11):1631173, 2007.
Miranda Fricker. Epistemic injustice: Power and the ethics of knowing. Oxford University Press, 2007.
Angela R Gover, Shannon B Harper, and Lynn Langton. Anti-asian hate crime during the covid-19 pandemic: Exploring the reproduction of inequality. American journal of criminal justice, 45(4): 647-667, 2020.
Felix Hamborg, Karsten Donnay, and Bela Gipp. Automated identification of media bias in news articles: an interdisciplinary literature review. International Journal on Digital Libraries, 20(4): 391-415, 2019.
Lisa A Harrison and Cynthia Willis Esqueda. Myths and stereotypes of actors involved in domestic violence: Implications for domestic violence culpability attributions. Aggression and Violens Behavior, 4(2)::29-133) 1999.
Christoph Hube and Besnik Fetahu. Neural based statement classification for biased language. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp- 195-203. Association for Computing Machinery, 2019. doi: 10.1145/3289600.299
Martijn Icks, Eric Shiraev, Jennifer Keohane, and Sergei A Samoilenko. Character assassination: Theoretical framework. In Routledge handbook of character assassination and reputation man- agement, pp. 11-24. Routledge, 2019.
Jacob Devlin Ming- Wei Chang Kenton and Lee Kristina Toutanova. Bert:
Pre-training of deep bidirectional transformers for language understanding. In Proceedings of naacI-HLLT, pp. 4171- 4186, 2019.
Crystal Kuykendall. Improving black student achievement by enhancing students' self-image. 1989.
Teyun Kwon and Anandha Gopalan. Co-star: Conceptualisation of stereotypes for analysis and reasoning. arXiv preprint 12Xiv:21 2021.
David Manheim and Scott Garrabrant. Categorizing variants goodhart's law. arXiv preprint 2018
Charles A Odonkor. Rachel Esparza, Laura E Flores, Monica Gutteere-. Miguel X Escalon, Ryan Solinsky, and Julie Silver. Disparities in health care for black patients in physical medicine and rehabilitation in the united states: a narrative review. PM&R, 13(2):180 180-203. 2021.
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Reid Pryzant, Richard Dichl Martinez, Nathan Dass, Sadao Kurohashi, Dan Jurafsky, and Diyi Yang. Neutralizing biased text lexicons. bttps://qitaure com/ zpryzant/
2020a. Accessed: 2022-06-20.
Reid Pryzant, Richard Diehl Martinez, Nathan Dass, Sadao Kurohashi, Dan Jurafsky, and Diyi
Yang. Automatically neutralizing subjective bias text. In Proceedings of the aaai conference artificial intelligence, volume 34, pp-
2020b.
Shaina Raza, Deepak John Reji, and Chen Ding. Dbias: Detecting biases and ensuring fairness in news articles. International Journal of Data Science and Analytics, 2022.
Marta Recasens, Cristian Danescu- NialeesuuMuls and Dan Jurafsky. Linguistic models analyz- ing and detecting biased language. In Proceedings of the 51 Annual Meeting of the Association for Computational Linguistics (Volume I: Long Papers), pp. 1650-1659, 2013.
Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah Smith, and Yejin Choi. Social bias frames: Reasoning about social and power implications of language. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5477-5490. Association for Computational Linguistics, 2020.
A APPENDIX
In Figure 6 we have diagram showing how the tagger model computes logits y/ using discrete feature fi and BERT embedding b, Pryzant et al. 2020b)..
W
W
Discrete featuures
b
BERT
W
Figure 6: The tagger model computes logits y; using discrete feature f: and BERT embedding b;
In Figure 7 we have screenshot of the UI showing immediately after a user has submitted a text for analyzing. The word suspected of causing injustice is highlighted with the certainty score, an explanation of why the word was tagged. When the user clicks Show Details they will see more details as displayed in Figure
Tech Justice Bias Text Analysis Tool Article
Canolltee
Rupurtaing daine Fozibook sttoiin wordd
Ynulpe
cantance hine
Be rear 50 regutbecrusa right
exch/
"Mo the dei give the cancaats secoa tocecoo anelnc arlonte 1p000M..
Tooperseea prreeid the a warks smalt l worwod velo.ee www.://wweeeeeemm www. Ba 00 www.:/wwww..aaaaoooooooooomo Mareshh fewtrii Fruswiin
Figure 7: The interactive interface showing the highlighted tagged word and associated information for sentence.
In Table 5 have all the headlines used in the comparative study between Meghan Markle and Kate Middleton. Several news outlets have also noted that these articles are used unjustly to depict person differently than the other shared topic, playing stereotypes about the person.
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Sentence
Headline
Subject
No.
1
Meghan Markle spent staggering f38,000 her clothes for charity trip Kate Middletons E100,000 Astonishing value the dress that won Prince's
Meghan Kate
2
heart (and has hung in wardrobe for eight years)
3
Meghan Markles beloved avocado linked human rights abuse and drought, millennial shame
Meghan
4
Kates morning sickness cure? Prince William gifted with an avocado for preg-
Kate
nant Duchess
5
Kate and William 'packed up the kids' search of 'privacy' at new Windsor
Kate
estate
6
Why Prince Harry and Meghan Markle's Potential Plan Protect Their Family
Meghan
is Incredibly Unfair to Archie
7
Prince Harry and Duchess Meghan Think Moving Canada Will Give Archic Normal Upbringing
Meghan
8
Not long to go! Pregnant Kate tenderly cradles her baby bump while wrapping up her royal duties ahead of maternity leave and William confirms she's due 'any minute now'
Kate
9
Why can't 't Meghan Markle keep her hands off her bump? Experts tackle the question that has got the nation talking: Is it pride, vanity, acting - new age bonding technique?
Meghan
10
Kate Middleton Wore Bardot Dress to the 'Top Gun' Premiere
Kate
11
Kate Middleton's homegrown bouquct of lily of the valley follows royal code Royal wedding: How Meghan Markles flowers may have put Princess Charlottes life risk
Kate
12
Meghan
13
Duchess Kate reveals favourite photo of son Prince Louis
Kate
14
How Meghan and Harry Ripped Up Royal Tradition on Birthday Photos of Archie
Meghan
15
Kate Middleton Debuts New Sapphire Earrings That Belonged to Princess Diana and Debunks a Rumor!
Kate
16
Meghan Markle Just Casually Rewore Her $16,500 Royal Wedding Earrings in NYC
Meghan
17
Kate and Wills Inc: Duke and Duchess secretly set up companies to protect their brand just like the Bockhams
Kate
18
A right royal cash in! How Prince Harry and Meghan Markle trademarked over 100 items from hoodies socks SIX MONTHS before split with monarchy with new empire worth up to f400m
Meghan
19
Kate Middleton deviated from royal "clcavage protocol' with daring neckline Meghan Markle Just Wore Plunging Gown With a Thigh-High Slit the Red
Kate
20
Meghan
Carpet
Table 5: Comparative Hcadline Sentences Used
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Table in this section shows the full output of tagging each headline sentence used in the compara- tive test. The No. column corresponds to the sentence number as shown in table Subject refers to the person subject the headline relates to, Taggerout Bias represents the tagged word from the sentence that could potentially he biased, Taggerout Prob refers to the probability of the tagged word been biased, Taggerout in lexicon refers to the results of checking whether the corresponding tagged word is observed in the lexicon of associated epistemological biased words. This offers some explanability for the potentially biased word. A True value for this column indi- cates that the word is indeed in the lexicon, whilst a False value indicates otherwise. Bias Type indicates what kind of epistemological bias type is associated with the tagged word. A regular bias type word indicates that the tagged word was not found in the epistemological bias lexicon. The Sentence Sent ment columns represents the overall sentiment associated with the headline sentence (which can be obtained using any NLP processing tool e.g. spacy).
No.
Subject
Taggerout Bias
Taggerout
Taggerout in Lexi-
Bias Type
Sentence Sentiment
Prob
con
Meghan
''saggerrng"" ]
0.999498
True
['subjectives]]
negative
Kate
['sstonishing]]
0.999342
True
ppssiiive', 'subjectives")
neutral
Meghan
['elooed"]
0.997946
Truc
positivee, 'subjectives'
negative
Kate
ggitted']
0.877285
True
ppossitvve, 'subjectives''
neutral
Kate
packed"]
0.478948
False
l'regular']
neutral
Meghan
Tincredibly'1 1
0.998493
True
possiiivee, 'subjectives']
negative
Meghan
normallI
0.689049
True
I'subjectives"]
neutral
Kate
Toonfirms]]
0,997673
True True True
f'entaimentt', ''eportt"""""
negative
Meghan
[vanity']
0.599388
n'eaattvve' "subjectives"]
negative
Kate
['top']
0,933422
e'entailentt', ppssiivee. 'subjectives))
neutral
Kate
Thomegr') ]
0,820782
False
('regular']
neutral
Meghan
Troyal')
0.551708
Fulse
('egular')
neutral
Kate
favourite')
0,838293
False
[regular']
neutral
Meghan
[ripped']
0.890869
True
I'eggative]
neutral
Kate
Thunks"]
0.758531
True
n'eegative.
'subjectives"]
negative
Meghan
"easually']
0.967407
False
I'regular'I
neutral
Kate
['just']
0.645452
True
''subjectives']
negative
Meghan
|markle']
0.583964
False
I'regular']
neutral
Kate
('daring']
0.856008
True
I'entailments'. 'implicatives', 'posi-
positive
tive', "subjectives"]
20
Meghan
['just']
0.565576
True
('subjectives']
neutral
Table 6: Table showing the output of passing each headline through the model that tags the potentially biased word in the given sentence.
In Table 7 the Stereotype (S) column refers the closest potentially associated stereotype. After passing each headline through the CO-STAR and Social Bias Frame models as discussed in section 3.2 we semantically ranked the list of potential stereotype to the headline sentence, and then selected the top ranked potential stereotype and stereotype concept as seen in the column Stereotype Concept (SC). The Distance and distance columns shows the se- mantic similarity distance of the stereotype and stereotype concepts to the headline sentence.
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No. Stercotype (S)
Distance(S) Stereotype Concept(SC))
DSst
personal spending habits
0.3457
women should spend money clothes
0.4914
women should be dressed like brides
0.3259
women are property sexism
0.2278
3
feminism
0.3663
0,2523
4
british women are marginalized for joke
0.2083
pregnancy
0.3918
5
N/A
N/A
N/A
N/A
family law
0.3244
arab folks not protected racial hierarchy
0.2951
7
arab folks should live a constant state
0.1618
0.0999
of worry
8
gender hierarchy
0.1178
women often subjected to terms like "babies" and "preg- nant"
0.2632
9
women are sexual objects women should wear dresses
0,2602
misogyny
0,2574
10
0.3131
women should wear dresses
0,3131
11
women property of men
0.1226
women property
0.1494
12
objects sexual abuse
0.1676
women vulnerable
0.2495
13
0.0746
saudi arabians pedophiles
0.1226
sexual assault
0.2091
royalty house guests are pac- dophiles
0.4691
15
women are sex objects black folks want to marry rich people
0.1637
women property
0.1504
16
0.2266
racial marriage
0.0869
17
women are property
0.2026
secret business part- ners
0.3732
18
royalty housekeeping are gold diggers
0.3741
royalty house queens are gold diggers
0,3657
19
sexism
0.1927
gender hierarchy
0.2284
20
women should dress modestly
0.3840
women should wear dresses that cover themselves
0.4047
Table 7: Table showing the potential top ranked stereotype and stereotype concept. We passed each sentence through the CO-STAR and social bias frames model. After which the potential stereotype and stereotype concepts where semantically ranked to the sentence itself
14


Reviews:
[{"review_id": "wV9K6YaLk_", "reviewer": "Reviewer_zugw", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The work used news media dataset to detect character, testimonial, and framing injustices. The models  employed are fine-tuned BERT model, CO-STAR model and Social-Bias Frames model. The framework is able to automatically detect epistemological bias using a fine-tuned tagger and a lexicon lookup. The result demonstrated 10 article headlines of Meghan Markle and 10 articles of Kate Middleton through the framework. In essence, journalists can use the system to detect potential bias types and injustices that might be present in their headlines before publishing. ", "summary_of_the_review": "This is a good paper and if the weaknesses are addressed, it will show good reproducibility and would benefit the community immensely in other domains away from the news media. ", "strength_and_weaknesses": "Strengths: \n1. The paper was well written; the flow and arrangements of the sections are excellent \n2. The approaches discussed and used are current and detailed to some extent \n3. The presentation of the overall result is impressive and relevant \n\nWeaknesses: \n1. The framework developed was not shown - a schematic diagram or an algorithm would have sufficed. \n2. The approach of the development of the tagger-UI was not presented - what software development methods were employed?\n3. The tagger-UI was not presented in the work - a screenshot would have sufficed. \n4. No mathematical model was demonstrated to prove the theory behind the entire process. \n5. How the sentiments (pos, neg) were derived was not shown clearly ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "jVxyr-nv2V", "reviewer": "Reviewer_VEMe", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes a pipeline that detects epistemologically biased words (e.g., ripped - negative) and stereotypes (e.g., women should be dressed like brides) in texts. The authors develop an interactive user-interface to show the detected output with a goal of helping journalists and editors. The backend of this interface (i.e., the detection part) consists of three previous models. The paper shows a study about two subjects, Meghan Markle and Kate Middleton, demonstrating the output from the system about them. ", "summary_of_the_review": "Although the topic of the paper is very interesting and important, I think the paper needs more work - little technical contribution and no deeper discussion/study about the efficacy of the proposed interactive interface. In addition, no technical details are provided to let readers understand how those biased words are detected. ", "strength_and_weaknesses": "Strengths:\n- I find the topic is interesting.\n- The paper throws big questions (in Sec 2), e.g., what kinds of implications does our use of these words impact society?\n- It shows how the pipeline is from the input text to the output. \n\nWeaknesses:\n- The paper doesn't describe the motivation in a convincing way.\n- The technical details are missing. It is difficult to know how the actual detection part works unless we read the previous papers. \n- There is no discussion about the accuracy of those models.\n- It seems that the propose pipeline uses existing models (with a small modification in the first model, which I was not really convinced of), and the interactive interface is a contribution. However, there is no screenshot of or an emphasis on the interface. The paper just describes what it does, and that's all.\n\nDetailed comments are below. Major and minor comments are mixed.\n- use citations in a right format for readability. In addition, there are many incomplete sentences. \n- \"give journalist and editors a tool which will help them learn\" <= They are experts, and they sometimes/often use the biased words intentionally to present their \"opinions\" and influence the public. In this case, how could your tool be helpful?\n- The paper seems to assume that the existing models give correct output, and there is no discussion about the accuracy of those models.\n- \"it is difficult for common people to identify a biased word\" <= Then, how was the training data for the existing models created? Was there any training involved?\n- \"difficultly\" => difficulty\n- expert linguistic and epistemological bias features in the tagger model<= Were these features manually extracted/annotated by experts? what kinds of features are they? \n- When you extend the model by including TMI, is this also manual feature extraction?\n- I'm confused with the TMI features because they look like they are already indicating biases, i.e., the output. So, the input are bias features, and the output are the probabilities indicating biases? It feels like the features are part of the intended output.\n- Why did you make a debiasing weight 1.3 as opposed to previous work? Any particular reason?\n- In 3.2, \"We rank these outputs based on their semantic similarity to the headlines.\" <= what is the semantic similarity between streotypes and new headlines? I'm not sure if the term \"semantic similarity\" is correct here. \"The stereotype and stereotype concept most correlated with the sentence\" <= how do you compute this? \n- Does the tagger model take into account contexts where a word is used? How is the output of this model different from using the epistemological lexicon directly to find biased words? It seems that the words that are not in this lexicon are not included for the study (the output) anyway. \n- interactive interface seems like the biggest contribution of this paper, but no screenshot or any study regarding that?\n- Table 1 uses a lot of space, but I'm not sure what the point is here showing all those. This space should be used for describing more technical details. \n- need a brief description of \"Meghan Markle and Kate Middleton\". not all the readers know them. \n- data: Is the data of Mechan Markle and Kate Middleton from the bias data corpus (Pryzant et al., 2020b)? How many headlines? <= Section 6 says it was only 20. How did you get this data?\n- \"if the measures and metrics suggested here become targets, they will cease to be useful.\" <= what does this mean?\n- I think it would be more helpful to design/interpret the current comparative study to show interesting/meaningful findings (e.g., in social sciences). ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "Kyt2rbl4ty", "reviewer": "Reviewer_gnJX", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "Authors propose a framework to detect epistemological biases and stereotypes in text and use this to assist with detecting injustice in text. They fine-tune a BERT based model for bias detection and Co-STAR and Social Bias Frames to detect stereotypes and stereotype concepts. They also propose an interactive interface that can be used by editors and journalists to learn about potential bias in the news media text they are publishing.\n\n", "summary_of_the_review": "Please see the sections above ", "strength_and_weaknesses": "Strengths:\n1. Authors tackle an important and interesting problem\n2. They propose an approach with a real-world application in mind and propose a framework that can be used in production to detect bias in text\n\nWeakness:\n\n1. I fail to see why the methodology has not been proposed as a generic approach to detect epistemological bias but has been restricted to news media.\n2. The problem that the authors are trying to capture is slightly confusing. Are they trying to detect epistemological biases? Are they trying to detect injustice in text? How are the two different?\n3. Authors mention detecting TMI but I couldn't find the process that they are using for the same\n4. The comparative test seems to have a scope of only 20 headlines which doesn't seem enough", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "Jo_udMl26dE", "reviewer": "Reviewer_P7BJ", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The authors propose a framework for epistemological bias detection, by combining different (existing) tools and features from previous work. By doing so, they claim to detect three types of bias: character, testimonial, and framing injustices. This bias is associated to single words, while exploiting context on the sentence level. Once a word is tagged it is linked to an entry in an epistemological lexicons from social sciences. This helps to explain the decision of the system to a user and to associate it to potential stereotypes. The authors see journalists and editors as the main target group of their tool, since it can be used to reduce unwanted bias in their articles.\nThe model is qualitatively evaluated on a data set with 20 headlines to reveal sentiment and bias type.", "summary_of_the_review": "I believe the paper's contribution is to push NLP more towards fundamental concepts from social and communication science. However, the lack of algorithmic novelty or statistical empirical evidence on the performance of the model makes it not yet ready for acceptance.", "strength_and_weaknesses": "+ The text analysis tasks proposed in this paper go beyond most established tasks with respect to the linguistic complexity required to solve the task. \n+ The application area is very timely and tackles a pressing societal issue.\n+ The authors are aware of the limitations of the work and make them explicit.\n- The paper is hard to access from a computer science oriented perspective, since a lot concepts from social sciences are used without definition or intuitive examples.\n- The technical contribution is restricted to the conceptual framework. No novel algorithmic contributions are provided, nor quantitative empirical insights.\n- While the task of bias detection is being addressed in a more fundamental way than before, the paper misses a larger vision, like using the tool to analyze bias quantitatively on large scale media data.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject