#  Nbias: A Natural Language Processing Framework for bias Identification in
Text

Shaina Raza<EMAIL_ADDRESS>Muskan Garg<EMAIL_ADDRESS>Deepak John Reji<EMAIL_ADDRESS>Syed Raza Bashir
<EMAIL_ADDRESS>Chen Ding<EMAIL_ADDRESS>Vector Institute
for Artificial Intelligence, Toronto, ON, Canada  Artificial Intelligence &
Informatics, Mayo Clinic, Rochester, MN, USA  Environmental Resources
Management, Bengaluru, Karnataka, India  Toronto Metropolitan University,
Toronto, ON, Canada

###### Abstract

Bias in textual data can lead to skewed interpretations and outcomes when the
data is used. These biases could perpetuate stereotypes, discrimination, or
other forms of unfair treatment. An algorithm trained on biased data may end
up making decisions that disproportionately impact a certain group of people.
Therefore, it is crucial to detect and remove these biases to ensure the fair
and ethical use of data. To this end, we develop a comprehensive and robust
framework Nbias that consists of four main layers: data, corpus construction,
model development and an evaluation layer. The dataset is constructed by
collecting diverse data from various domains, including social media,
healthcare, and job hiring portals. As such, we applied a transformer-based
token classification model that is able to identify bias words/ phrases
through a unique named entity BIAS. In the evaluation procedure, we
incorporate a blend of quantitative and qualitative measures to gauge the
effectiveness of our models. We achieve accuracy improvements ranging from 1%
to 8% compared to baselines. We are also able to generate a robust
understanding of the model functioning. The proposed approach is applicable to
a variety of biases and contributes to the fair and ethical use of textual
data.

###### keywords:

Bias detection , Dataset , Token classification , Nbias

##  1 Introduction

The recent surge in Natural Language Processing (NLP) applications,
encompassing fields from recommendation systems to social justice and
employment screening, has sparked a critical concern - the emergence of bias
within these systems [1]. Instances of racial and gender bias have been
increasingly reported [2], indicating an urgent need for scrutiny. These
biases often originate from the training data used in NLP models, and a
majority of these large datasets harbor inherent biases . Regrettably, many
NLP practitioners lack the necessary awareness or knowledge to effectively
identify and address these biases, highlighting a significant gap in the
field.

Furthermore, there is a notable lack of discussion on data specifics - its
origin, generation, and pre-processing - in many NLP publications. Given these
circumstances, the importance of addressing biases in NLP applications cannot
be overstated. These biases, if unchecked, not only compromise the validity of
the models, but can also have unfavorable and detrimental consequences. The
objective of this research is to provide insights into the detection of bias
in NLP datasets, contributing to the development of more equitable and
unbiased Artificial Intelligence (AI) systems.

Bias in text data is a pervasive and deeply-rooted issue. The bias in data
often stems from cognitive predispositions that influences our dialogues,
views, and understanding of information [3]. This bias can be explicit which
are often seen in discriminatory language targeting certain racial or ethnic
groups [4], as in social media. Implicit bias [5], on the other hand, subtly
perpetuates prejudice through unintentional language use but is equally
harmful.

The necessity for unbiased, trustworthy text data has grown across sectors
like healthcare [6], social media [4, 7], and recruitment [8]. This data is
essential for training NLP models for various downstream tasks, like
formulating healthcare diagnoses and treatment plans, handling discriminatory
language on social media, and promoting fair recruitment practices. Figure 1
illustrates the complexities of biases in text data in various domains,
including job hiring, social media, and healthcare. These biases are primarily
conveyed through lexical choices [9] and demand sophisticated detection
methods, motivating this research. The primary aim of this study is to further
foundational research on the fairness and reliability of the textual data.

Figure 1: Visual Representation of Implicit and Explicit Biases in Textual
Data: Examples from Job Hiring, Social Media, and Healthcare.

Although NLP has advanced much, the state-of-the-art techniques [2, 10, 11]
often concentrate on bias detection in specific domains and lack
generalizability. To address this, our research offers a generalizable bias
detection method proven effective across the various domains. We present
Nbias, a comprehensive framework for detecting bias in text data. This
involves data preparation where bias-indicative terms are marked using a
transformer-based token classification method like Named Entity Recognition
(NER).

Current NER solutions can manage general [12], biomedical [13], and social
media [14] entities, but often neglect BIAS as a separate entity. To address
this, we introduce a new entity type, BIAS, to identify biased terms in text
data. In this context, bias refers to unfair and often harmful favoritism or
prejudice towards a particular group, person, or idea, which can manifest
through profanity, unjustified criticism, or discriminatory language.

A key contribution of this study is the development of the first comprehensive
framework for bias detection in text data. This framework is based on latest
language model technology and incorporates four crucial layers: data
gathering, corpus construction, model development, and rigorous evaluation.
The specific contributions of the work are as follows:

  1. 1.

Development of Annotated Datasets: Acknowledging the scarcity of bias
annotations in text-based data, we designed a solution by generating multiple
annotated datasets. Our work fills a critical gap in the available resources,
thereby providing a solid foundation for future research in the realm of bias
detection.

  2. 2.

Semi-Autonomous Labeling: To alleviate the time-intensive manual annotation
process, we pioneered a novel methodology termed “semi-autonomous labeling”.
This strategy provides a faster and more efficient way of annotating bias-
related terms within textual content. This innovative approach has significant
implications for improving the speed and accuracy of bias detection.

  3. 3.

Unique Entity Type - BIAS: In an effort to enhance the precision of bias
identification within text, we introduced a unique entity type, BIAS. This new
entity type is specifically designed for detecting biased words and phrases
within the text data. This has the potential to dramatically improve the
process of bias identification and quantification in text-based analysis.

  4. 4.

Comprehensive Evaluation Process: We subjected our proposed framework to a
thorough evaluation process, utilizing both quantitative and qualitative
analysis methods. The results confirm the reliability and efficiency of our
approach, making it compatible for its application in real-world scenarios.
This rigorous evaluation sets a benchmark for assessing the efficacy of bias
detection methodologies.

##  2 Related Work

###  2.1 Identifying Bias in NLP

One of the key challenges associated with NLP systems lies in the presence of
bias, a manifestation of unfair and systematic discrimination observed in
their outcomes [15]. Moreover, the past studies [16, 10, 11, 2, 17] have shown
the societal and cultural prejudices are deeply embedded within the training
data due to the presence of bias. As such, the biases, whether explicit or
implicit, can significantly impact the functionality of the NLP systems,
leading to skewed results and perpetuating existing societal biases. Thus, the
detection and mitigation of these biases are crucial to promoting fairness and
inclusiveness within NLP systems [7, 11].

Researchers have proposed and implemented various strategies to identify bias,
including employing statistical methods to discover patterns of bias within
the training data [2, 18]. Under this approach, specific words or phrases that
appear to be disproportionately associated with certain demographic groups,
such as genders or races, are identified. For example, certain adjectives
might be used more frequently in descriptions of women than men [2], or vice
versa. The identification and debiasing of such patterns can highlight areas
of potential bias, providing a starting point for efforts to eliminate these
biases [19].

The field of bias detection in NLP has seen a surge of innovative methods in
recent years, primarily leveraging advanced machine learning techniques. One
such study has considered the combination of a speech detection system with an
explanatory method to identify potential bias [20]. In this method, not only
is the system trained to detect instances of hate speech, but it also provides
a rationale or explanation for its classification decisions. Another area of
research that has attracted considerable attention is the investigation of
bias in event detection datasets and models [21]. Event detection tasks
involve identifying and classifying real-world events within text data. These
tasks can be susceptible to a range of bias-related issues, including data
sparsity, labeling task, and annotations.

Additionally, NLP techniques have been employed to address various aspects of
bias. For instance, in a related study [22] on gender bias and sentiment
towards political leaders in the news were quantified using word embeddings
and sentiment analysis. Another work focused on investigating ableist bias in
NLP systems, particularly at the intersection of gender, race, and disability
[23]. Similarly, a methodology was proposed to eliminate gender bias from word
embeddings [24]. Furthermore, marked attribute bias in natural language
inference was identified and analyzed, with an evaluation of existing methods
for bias mitigation [9]. These studies provide a deep understanding of the
social and cultural factors that contribute to bias identification.

Another work [25] presents bias analysis in NLP beyond demographic bias,
focusing on predicting interpersonal group relationships using fine-grained
emotions. A related study [26] evaluates gender bias in NLP research,
highlighting the lack of explicit gender theorization. In another work,
authors [27] introduce an effective bias-conflicting scoring method and
gradient alignment strategy to identify and mitigate dataset biases. Overall,
these studies underscore the importance of continuous efforts in identifying
and mitigating biases in models to ensure fairness and equity.

###  2.2 Named Entity Recognition (NER)

Named Entity Recognition (NER) is a significant task in NLP that is aimed at
identifying and classifying named entities within textual data. NER is a token
classification task that focuses on identifying and classifying named entities
such as individuals, organizations, and locations within a given text. In the
past, many traditional methods have been employed for NER, each with its
unique characteristics and benefits.

  * 1.

Rule-based methods rely on predefined sets of rules to identify named entities
[28]. This method usually employs regular expressions or dictionary-based
techniques to extract entities. Although rule-based methods can be effective
for well-defined and specific contexts, their performance can decrease in the
face of variability and ambiguity in language usage.

  * 2.

Supervised learning methods leverage annotated data to train a model for NER
[14, 29]. These methods use statistical models such as Support Vector Machines
(SVM), Conditional Random Fields (CRF), and others to classify the named
entities. The performance of supervised learning methods can be impressive,
given sufficient high-quality annotated data.

  * 3.

Deep learning methods, which are more contemporary approaches, utilize complex
architectures like recurrent neural networks (RNNs) and transformer-based
language models to extract named entities [30, 13]. These methods have shown
promising results in NER tasks, owing to their capacity to capture intricate
language patterns and contextual information.

A recent study introduced a contrastive learning-based approach for multimodal
NER [31]. This approach leverages both textual and non-textual data to
identify and classify named entities, harnessing the complementary information
offered by different modalities to improve the model’s performance. Another
research work into event detection from social media posts, evaluating the
effectiveness of a pre-trained NER model followed by graph-based spectral
clustering [32]. The study also explored transformer-based methods to weight
the edges of the graph for event detection, further refining the detection
process. A span-based NER model eliminates the need for label dependency [32].
This approach addresses the issue of cascade label mis-classifications, a
common challenge in traditional NER models that depend on label sequences.

While our work on token classification is inspired by these studies, we
identify a notable gap in the literature: the existing seminal work does not
recognize BIAS as an entity. In this work, we detect biased expressions within
unstructured texts, designating them under the ’BIAS’ entity label.

###  2.3 Data Annotation

Data annotation is a crucial task in NLP as it involves labeling and
categorizing information to extract valuable insights from text data [33]. By
enriching text data with relevant metadata, such as part-of-speech tags, named
entity tags, and sentiment tags, data annotation provides contextual
information that is essential for subsequent analysis [34]. Quality annotated
data enhances model learning, boosting prediction accuracy. In contrast,
inadequate annotations impede learning, resulting in subpar performance.
Various methods of data annotation cater to different requirements of speed,
quality, and computational resources:

  * 1.

Manual annotation is carried out by human annotators who carefully review and
label the data. This method typically yields high-quality results, given the
nuanced understanding that humans have of language. However, manual annotation
is often time-consuming and labor-intensive, and its feasibility may be
limited by the availability of qualified annotators and financial resources
[28].

  * 2.

Semi-automatic annotation combines manual efforts with automated tools to
accelerate the annotation process and minimize human error. These tools can
range from rule-based systems to pre-trained machine learning models [35].
While semi-automatic annotation can improve efficiency, its accuracy may still
depend on the quality of the automated tools and the manual review process.

  * 3.

Automatic annotation leverages machine learning models and algorithms to
annotate text data without human intervention [36]. Although automatic
annotation can process vast amounts of data in a relatively short time, its
accuracy may be compromised, particularly for complex or ambiguous texts.
Therefore, a common practice is to combine automatic annotation with manual
review to ensure data quality.

Various strategies have been developed to address these challenges and
optimize the annotation process. One study presents a comprehensive comparison
of different annotation tools, highlighting their strengths and limitations
[37]. Another research work proposes a method for automatically generating
high-quality labeled data for NER tasks by leveraging existing knowledge bases
[38]. A similar study has developed an annotation framework that combines
statistical machine translation and human annotation to create a parallel
corpus [39]. Other researchers have investigated methods for improving the
reliability and consistency of manual annotations, such as developing
guidelines and protocols for annotation tasks [40]or implementing quality
control mechanisms to ensure data quality.

Ultimately, the choice of annotation method and tools will depend on the
specific requirements of a project, such as the desired level of accuracy, the
available resources, and the nature of the data being annotated.To this end,
we employ a semi-automatic annotation strategy, integrating human proficiency
with semi-supervised learning methodologies.

##  3 Proposed Framework for Bias Identification in Texts

In this section, we present Nbias, an innovative framework designed to detect
biases within textual data, as illustrated in Figure 2. The Nbias framework is
structured into four distinct layers: (i) the data collection layer, (ii) the
corpus construction layer, (iii) the model development layer, and (iv) the
evaluation Layer. Each layer is designed to collaborate seamlessly with the
others, providing an effective and comprehensive approach for detecting biases
in textual content.

Figure 2: Nbias: A Natural Language Processing Framework for Bias
Identification.

###  3.1 Data Layer

The Data Layer serves as the framework’s primary interface with the data for
analysis. It handles data collection, pre-processing and data consolidation
from a variety of sources, such as social media, online articles, and
databases. This layer ensure adaptability and high performance for the entire
framework.

#### Data Gathering

Our study adopts a methodological data collection approach, incorporating
diverse sources from various domains. To analyze biases in medical narratives
and healthcare, we include data from two important clinical text databases:
MIMIC-III [41] and MACCROBAT [37]. The MIMIC-III dataset is a publicly
available database with de-identified health data from over 40,000 ICU
patients. It offers rich clinical narratives, including nursing notes,
radiology reports, and discharge summaries, enabling a deep understanding of
biases in healthcare communication. The textual data were primarily obtained
from the NOTEEVENTS table.

The MACCROBAT dataset provides valuable pediatric critical care data,
including admission notes and medical summaries. It contains 200 original
documents along with corresponding annotated versions centered around clinical
case reports.

To detect bias in news articles and social media streams, we use the BABE
(Bias Annotations By Experts) dataset [10]. This dataset includes 3700
articles and tweets, offering a comprehensive perspective on linguistic bias
in media and public opinion. It features marked statements, enabling
recognition of bias at both granular (word-level) and broader (sentence-level)
scopes, covering diverse topics.

To examine biases in employment practices, we incorporate the Job
Hiring/Recruitment dataset [42], comprising 20,000 job posts with titles,
descriptions, and associated tags from various businesses. Each advertisement
includes job details and manually assigned tags by recruiters, suggesting jobs
to potential candidates with analogous skills.

#### Data Consolidation

After gathering and pre-processing data from various sources, all datasets are
harmonized into a single consolidated dataframe. This dataframe includes the
following columns:

  * 1.

Dataset: Specifies the source dataset, such as MIMIC-III, MACCROBAT, Job
Hiring, or BABE

  * 2.

Text: Contains the actual textual data extracted from the respective datasets,
including clinical notes, case reports, job descriptions, or annotated
statements.

  * 3.

Biased Words: Includes the words or phrases identified as biased in the text,
crucial for granular bias detection.

  * 4.

Aspect of Bias: Denotes the specific type or aspect of bias present in the
text, categorized by gender, racial, or age biases, to understand the nature
of the biases detected.

  * 5.

Label: Indicates whether the text is biased or non-biased, serving as the
target variable for the token classifier and for evaluation purposes.

A sample record in JSON format is shown below:

⬇

{

"Record": {

"Dataset": "MIMIC-III",

"Text": "Clinical notes of patient XYZ indicate a history of superficial
hypertension due to overly emotional personality.",

"BiasedWords": "superficial, overly emotional personality",

"AspectOfBias": "age",

"Label": "biased"

}

}

In the consolidated dataframe, each row represents a unique sample from the
original dataset, supplying information for bias detection and assessment.
Further pre-processing is conducted to prepare the data for subsequent layers
of the Nbias framework, particularly the NLP model performing token
classification.

#### Data Pre-processing

The pre-processing of textual data involves a series of sequential operations
to refine and structure the data for machine learning algorithms. This
includes tokenization, which involves breaking raw text into meaningful tokens
(words or subwords) for semantic understanding and subsequent NLP tasks; text
cleaning, which involves removing punctuation, numbers, special characters,
and converting text to lowercase to ensure uniformity and clarity; and
handling missing values, which involves identifying and appropriately managing
missing data to avoid bias and improve model performance. These pre-processing
steps convert raw text into a clean, structured format, enabling the NLP token
classification model in the subsequent layer.

###  3.2 Corpus Construction

Our group, consisting of three seasoned professionals from the disciplines of
healthcare, computer science, and journalism, was joined by two diligent
students to perform the task of detecting and labeling bias in our dataset.
Their collective role centered around the critical task of carefully
annotating bias within our dataset. This endeavor is important to ensure the
integrity and fairness of any subsequent analysis or research. The foundation
for this task was based on comprehensive guidelines that clearly delineated
the concept of bias in this context.

Bias, as per the instructions, was defined as any terminology or phraseology
that could potentially provoke prejudiced comprehension or induce
stereotyping, as mentioned in most of the literature [11, 7, 10] also. The
factors from which biases could stem were identified as gender, race,
socioeconomic status, age, or disability for this NLP work. Such biases could
inadvertently skew the dataset and, consequently, the results derived from it.
Thus, the identification and annotation of such biases are of high importance
to uphold the accuracy and reliability of our dataset. Highlighting both
explicit and implicit biases was emphasized as a critical part of our work.

#### Annotation Scheme

Under the light of these provided guidelines, our team proceeded by using a
carefully compiled list of terms and phrases, known as “bias-indicative”
lexicons. These lexicons provided a comprehensive guide to potential areas
where bias could lurk within our dataset. A portion of this list is exhibited
in Table 1 for reference. This bias-indicative lexicon served as a
navigational tool for our team to identify and mark “BIAS” entities scattered
within our textual data. These entities can be individual words or phrases
that express or imply bias. This systematic approach ensured that we could
account for most biases that exists in the data.

Table 1: Bias Dimensions and Fewer Sample Biased Words/Phrases Shown Due to Brevity Bias Dimension |  Biased Words/Phrases
---|---
Gender |  ‘hysterical’, ‘emotional’, ‘weak’, ‘bossy’, ‘fragile’, ‘nagging’, ‘man up’, ‘tomboy’
Race |  ‘inner city’, ‘illegal alien’, ‘thug’, ‘exotic’, ‘uncivilized’, ‘model minority’, ‘white trash’
Social Status |  ‘trailer park’, ‘lazy’, ‘freeloader’, ‘welfare queen’, ‘ghetto’, ‘lazy bum’, ‘filthy rich’
Age |  ‘senile’, ‘slow’, ‘old-fashioned’, ‘whippersnapper’, ‘elderly’, ‘young and naive’, ‘generation gap’
Disability |  ‘handicapped’, ‘crippled’, ‘invalid’, ‘sufferer’, ‘differently-abled’, ‘victim’
Religion |  ‘radical’, ‘terrorist’, ‘infidel’, ‘heathen’, ‘fanatic’, ‘holy roller’
Profession |  ‘greedy’, ‘dishonest’, ‘corrupt politician’, ‘crooked lawyer’, ‘greedy CEO’, ‘lazy government worker’
National |  ‘unpatriotic’, ‘alien’, ‘foreigner’, ‘outsider’, ‘immigrant’, ‘nationalist’
Education |  ‘uneducated’, ‘illiterate’, ‘dropout’, ‘underachiever’, ‘overachiever’, ‘smarty-pants’
Body Size |  ‘fat’, ‘slob’, ‘skinny’, ‘lardass’, ‘beanpole’, ‘plus-sized’

We adopted the Inside-Outside-Beginning (IOB) annotation scheme [43] to
classify and annotate ‘BIAS’ entities. This technique categorizes tokens in
the text as the beginning (B), inside (I), or outside (O) of a bias entity.
‘B’ represents the first token of a bias entity, ‘I’ for tokens inside the
entity, and ‘O’ for tokens not part of any bias entity. This approach ensured
consistent and precise annotations, enhancing the reliability and accuracy of
our study.

#### Annotation Approach

We leveraged semi-supervised learning methodologies [35, 13, 33] to enhance
both efficiency and precision of the annotation process. The integration of
BERT (Bidirectional Encoder Representations from Transformers) , known for its
superior text comprehension abilities, substantially improved our approach.

Our annotation process initiated with the manual tagging of 20% of the
complete dataset. This critical yet time-consuming task was strategically
limited to a subset of data, ensuring a balance between accuracy and
efficiency. The “BIAS” entities were carefully annotated in compliance with
our predefined guidelines. This annotated subset then fed into our BERT model,
serving as training data for the token-classification task. Once sufficiently
trained, the model was assigned the task of predicting “BIAS” entities within
the remaining 80% of the data. The extensive dataset was effectively managed
by breaking it down into 20% increments, a process we refer to as “semi-
autonomous labelling”. Expert reviews cross-verified the “BIAS” entities
labelled by the model. This combination of semi-supervised learning with
expert validation enabled us to create an annotation process that is both
optimized and trustworthy.

#### Working Instance

To demonstrate our annotation scheme, we consider the example sentence: “The
overpriced product from the highly successful company was surprisingly
popular”. Table 2 presents the corresponding BIO format annotations for this
sentence. Assuming the term “overpriced” holds potential bias, it would be
tagged as “B” in the BIO scheme, indicating the start of a bias entity. All
other tokens not part of this bias entity would be labeled “O”. This example
shows our extensive annotation process across our dataset. This approach
allows us to quantify and comprehend biases in a consistent manner.

Table 2: Bias Annotation using BIO scheme Word | Bias Annotation
---|---
The | O
overpriced | B-BIAS
product | O
from | O
the | O
highly | B-BIAS
successful | I-BIAS
company | O
was | O
surprisingly | B-BIAS
popular | I-BIAS
. | O

#### Resolving Discrepancies

An integral part of our process was addressing discrepancies between
annotators, a common criteria in multi-person annotation tasks. We implemented
a consensus-driven approach to uphold consistency and reliability in our
annotations. Any disagreement was discussed collectively, considering each
annotator’s viewpoint and reaching a unified decision based on predefined
annotation guidelines. This process ensured collective agreement on all
annotations, minimizing potential bias or error and boosting reliability. This
consensus strategy was uniformly applied across all data sources including the
BABE, MIMIC, MACCROBAT, or Job Hiring datasets.

#### FAIR Principles

After reaching consensus on all annotations, we saved the final annotated data
in the widely-accepted CoNLL-2003 format [44]. This format represents data in
a tab-separated manner, associating each word with its part of speech tag,
chunk tag, and named entity tag. Sentences are separated by an empty line, and
each row corresponds to a token with its annotation.

The CoNLL-2003 format offers multiple benefits. It ensures compatibility with
existing NLP tools and models, facilitating future analysis and model
training. Additionally, it promotes collaboration and peer review by allowing
easy data sharing and comprehension among researchers. Lastly, it enhances the
reproducibility of our study, enabling others to use our data for model
validation and findings replication. By adhering to the FAIR principles, our
dataset is made Findable, Accountable, Interoperable, and Reusable, enhancing
the transparency, accessibility, and reliability of our research.

#### Inter-Annotator Agreement

In our research, we placed considerable emphasis on establishing rigorous
protocols to guarantee the reliability and consistency of the data
annotations. Two independent reviewers were assigned to carefully assess the
annotated data, promoting objective evaluation devoid of influence from
initial annotators. Rather than relying on subjective judgment, we quantified
their agreement through Cohen’s Kappa coefficient—a statistical measure common
in categorical data studies, accounting for potential chance agreement. Scores
over 0.6 denote “substantial” agreement and above 0.8 represent “almost
perfect” agreement. Our reviewers attained a Cohen’s Kappa score of 78%,
demonstrating high concordance on the annotations. This high score
substantiates the uniformity, consistency, and quality of our annotations.
Moreover, it demonstrates the objectivity of the assessment process,
highlighting the well-built nature of our annotated data. This, in turn,
enhances the trustworthiness of prospective findings drawn from this dataset.

###  3.3 Model Development Layer

In this layer, we leverage the BERT language model for token-classification
and adapt it for the task of NER . The choice of BERT is motivated by its
powerful capability of understanding both left and right context of a word,
and its effectiveness in recognizing and classifying multi-word phrases. These
features make it particularly well-suited for the complex task of bias
detection and annotation in our text data.

The advantage of using BERT in Nbias model development lies in its more
effective token-level bias identification. Nbias incorporates enhancements to
the standard BERT architecture, such as modifications in the attention
mechanism, loss function, and fine-tuning approaches, specifically tailored
for better capturing biases in complex text data. The subsequent section
provides a detailed explanation of the model development.

The token classifier architecture (shown in as the middle component in Figure
2) consists of a multi-layer bidirectional transformer encoder that captures
contextual information from both directions. Given an input sequence
$X=\\{x_{1},x_{2},...,x_{n}\\}$, the words are tokenized and embedded as shown
in Equation (1):

$E(X)=\\{e(x_{1}),e(x_{2}),\ldots,e(x_{n})\\}$ (1)

where $E(X)$ represents the set of embedded representations for an input
sequence $X$, $X$ consists of $n$ words $\\{x_{1},x_{2},...,x_{n}\\}$,
$e(x_{i})$ is the embedding function that maps each word $x_{i}$ from the
input sequence to a continuous vector representation. The embedded input
sequence is then passed through the transformer layers.

BERT employs self-attention mechanisms to weigh the importance of different
words in the input sequence, enabling it to better identify and understand
complex relationships between words. The self-attention $Att$ score between
word $i$ and word $j$ is computed as shown in Equation (2):

$Att(i,j)=\text{Softmax}\left(\frac{Q(e(x_{i}))\cdot
K(e(x_{j}))^{T}}{\sqrt{d_{k}}}\right)$ (2)

where $Q$, $K$ are the query and key matrices, and $d_{k}$ is the key
dimension.

Following the transformer encoder, the output after applying self-attention
and passing through the bidirectional transformer encoder is represented, as
shown in Equation (3):

$R(X)=\\{r(x_{1}),r(x_{2}),...,r(x_{n})\\}$ (3)

where $R(X)$ represents the set of contextualized representations for an input
sequence $X$. The function $r(x_{i})$ is the representation function that maps
each word $x_{i}$ from the input sequence to a continuous vector
representation after passing through the transformer encoder.

A linear layer with a softmax activation function is added for entity
classification. This layer transforms the representations generated by the
transformer encoder into a probability distribution over the possible output
classes. To simplify our annotation and prediction task, we have merged the
’B’ (Beginning) and ’I’ (Inside) tags from the standard BIO tagging scheme
into a single ’BIAS’ tag. The ’BIAS’ tag represent any part of a bias entity,
while ’O’ represents non-entity. The probability distribution is calculated as
shown in Equation (4):

$P(y|x)=\text{Softmax}(W\cdot c(x)+b)$ (4)

where $W$ is the weight matrix, $b$ is the bias vector in the softmax
function, and $P(y|x)$ is the probability distribution over the output classes
‘BIAS’ and ‘O’. The final output of the model indicates the presence of biased
words or phrases within the input sequence by labeling them as ‘BIAS’. This
simplification enables our model to recognize biased phrases more effectively,
without differentiating between their start or continuation.

We show an example of the model output on a sample from the test set in Figure
3.

Figure 3: BIAS Annotation on a Piece of Text

The pseudocode algorithm steps for the Nbias model development are given in
Algorithm 1. As seen in Algorithm 1, the Nbias model, built on BERT, tokenizes
and contextualizes input text using transformer encoders. Through self-
attention mechanisms, it weighs relationships between words and classifies
each token as biased or unbiased using a softmax-activated linear layer.

Algorithm 1 Nbias Model Development

1:Text sequence $X=\\{x_{1},x_{2},\dots,x_{n}\\}$

2:Initialize BERT with token-classification architecture

3:Tokenize input sequence $X$

4:Embed input sequence: $E(X)=\\{e(x_{1}),e(x_{2}),\dots,e(x_{n})\\}$

5:for each token in $E(X)$ do

6: Compute self-attention:

7: $Att(i,j)=\text{Softmax}\left(\frac{Q(e(x_{i}))\times
K(e(x_{j}))^{T}}{\sqrt{dk}}\right)$

8:end for

9:Pass $E(X)$ through bidirectional transformer encoder:
$R(X)=\\{r(x_{1}),r(x_{2}),\dots,r(x_{n})\\}$

10:for each token representation in $R(X)$ do

11: Compute probability distribution: $P(y|x_{i})=\text{Softmax}(W\times
c(x_{i})+b)$

12:end for

13:for each token in $X$ do

14: if probability corresponds to BIAS then

15: label as ‘BIAS’

16: else

17: label as ‘O’

18: end if

19:end for

20:return the labeled sequence

###  3.4 Evaluation Layer

The evaluation layer plays a critical role in assessing the performance of our
model. This layer encompasses both quantitative and qualitative evaluation
methods, providing a comprehensive perspective on the model’s performance.

#### Quantitative Evaluation

The quantitative evaluation is typically statistical in nature and involves
the use of various metrics to numerically measure the model’s performance.
Metrics such as F1-score, AUC-ROC and accuracy are commonly used in this
context. F1 score balances precision (the ability of the model to correctly
identify positive instances) and recall (the ability of the model to identify
all relevant instances), providing a single measure of the model’s overall
performance.

#### Qualitative Evaluations

In addition to these numerical measures, we also conduct a qualitative
evaluation. This type of evaluation is more about the quality, relevance, and
usefulness of the model’s output. It involves an expert review of a subset of
the model’s predictions to measure how well the model is performing in
practical terms. Factors such as the model’s ability to correctly identify
complex or subtle bias entities, and the interpretability of its output, are
examined in the qualitative evaluation.

In our study, we focus on qualitative evaluations, specifically assessing
model robustness and conducting perpetuation tests. Our robustness analysis
[45] explores the model’s stability under various conditions including
adversarial inputs and data variations. Perpetuation tests [46] help us
understand if the model inadvertently reinforces or introduces societal
biases. We also consider a human evaluation, to assess the model’s performance
in real-world conditions.

##  4 Experimental Setup

In this section, we detail the settings, evaluation metrics, baselines and
hyperparameters of our experimental design for replication and validation.

###  4.1 Dataset

Our study uses diverse datasets: MIMIC-III [41], MACCROBAT [37], BABE [10],
and Job Hiring [42]. After annotation (detailed in Section 3.1 and 3.2), each
dataset is split into training, validation, and test sets using an 80-10-10
ratio. The division allows for efficient model training, validation, and
testing. Modifications are made for the MACCROBAT dataset to maintain balance
despite its limited entries. Table 3 presents the detailed dataset
information.

Table 3: Dataset Details with Training (train), Development (dev), Test (test) sets and Total Samples Data Source | Domain | train | dev | test | Total
---|---|---|---|---|---
BABE | News/Social Media | 15,300 | 1,700 | 1,700 | 18,700
MIMIC (Clinical) | Healthcare | 1,800 | 200 | 200 | 2,200
MACCROBAT | Healthcare | 160 | – | 40 | 200
Job Hiring | Occupational | 16,000 | 2,000 | 2,000 | 20,000
Total |  | 33,260 | 3,900 | 3,940 | 41,100

###  4.2 Hardware Settings

The experiments conducted in this study were performed on a dedicated research
server with specific hardware configurations. The server was equipped with an
Intel Xeon CPU E5-2690 v4 running at 2.60GHz, 128GB of RAM, and a powerful
NVIDIA GeForce RTX 3090 GPU. The operating system installed on the server was
Ubuntu 18.04 LTS. These hardware settings provided substantial computational
power, enabling us to efficiently execute resource-intensive tasks, such as
training complex machine learning algorithms and deep learning models.

###  4.3 Time measurements

Time measurements during the training, validation, and testing phases were
recorded for our models across the diverse datasets. Utilizing our hardware
setup, we ensured peak performance with minimized hardware-induced delays.
Specifically, the BABE dataset took 4.5 hours for training with 30 minutes
each for validation and testing. The MIMIC dataset required 2 hours of
training, and 10 minutes for both validation and testing. For the smaller
MACCROBAT dataset, training was completed in 0.5 hours, with validation and
testing taking 5 minutes each. Lastly, the Job Hiring dataset took the longest
at 5 hours for training and 40 minutes each for validation and testing.

###  4.4 Baselines

For the comparison of models for token classification model performance, we
consider a range of diverse baseline approaches. These include BiLSTM-CRF,
which combines BiLSTM and CRF [29]; BERT-CRF, a blend of BERT and CRF [47];
RoBERTa, an optimized variant of BERT [48]; BART-NER, an application of the
BART model for NER [49]; CNN-NER, a CNN-based method for capturing named
entities [50]; and TENER, an NER model that utilizes an adapted Transformer
Encoder for character and word-level features [51]. We also consider the few-
shot NER models like [52] and model-agnostic meta-learning (MAML) [53] and
zero-shot named entity typing (NET) [54] model.

The selected baselines represent a collection of different architectures such
as BiLSTM, BERT, RoBERTa, BART, CNN, and Transformer Encoder, each combined
with either the CRF or NER task. These models were chosen because they
represent the state-of-the-art and constitute a robust set of baselines for
comparing token classification model performance.

###  4.5 Hyperparameter Settings

The chosen hyperparameters for our token classifier are provided in Table 4.

Table 4: Hyperparameter Settings and Training Details Parameter/Method |  Details/Value
---|---
Model |  bert-base-uncased
Optimizer |  Adam
Learning Rate |  $1\times 10^{-2}$
Momentum |  0.5
Weight Decay |  0.01
Epochs |  5
Batch Sizes |  4, 8, 16, 32, 64
Batch Size (training) |  16
Input Sequence Length |  128 subword tokens
Dropout |  Applied on input and hidden layers
Convergence Criteria |  Negligible decrease in validation loss
Validation Strategy |  Hold-out
Early Stopping |  Implemented
Training Environment |  Google Colab Pro
Hardware |  NVIDIA Tesla T4 GPU
$\beta_{1}$ |  0.9
$\beta_{2}$ |  0.999
Epsilon |  $1\times 10^{-8}$
Hidden Units |  (Leaky) Rectified Linear Units (ReLUs)

In the comparative experiments with the baselines, the models were optimized
using a learning rate between 1e-5 and 5e-5 over several training epochs,
typically 3 to 10. The batch size varied between 16 and 64, based on memory
constraints, and the input sequence length was limited to 512 tokens. To
prevent overfitting, we used regularization techniques such as dropout and
weight decay. We generally employed the Adam or AdamW optimizer. All
hyperparameters were fine-tuned according to the specific task requirements
and dataset characteristics.

##  5 Results

###  5.1 Overall Performance

Table 5 presents a comprehensive comparison of our proposed method, Nbias,
with various baseline models in the token-classification task across three
distinct categories: Social Media Bias, Health-related, and Job Hiring. Due to
space constraints, we are only reporting the F1-scores in this overall
comparoson, which are the harmonic mean of precision and recall, as it is a
commonly used single metric that combines both precision and recall. The
F1-scores are expressed as percentages, accompanied by the standard deviation
(±) to indicate the variability in scores across five separate runs. The
highest F1-score in each category is highlighted in bold to easily identify
the best performing model.

Table 5: Comparison of Token Classification Models on Three Different Categories: Social Media Bias, Health-related, and Occupational. The performance metric used is F1-score (harmonic mean of precision and recall), expressed as a percentage, accompanied by the standard deviation (±) indicating the variability in scores across 5 runs. The best score is highlighted in bold. Model | Social Media | Health-related | Job Hiring
---|---|---|---
Rule-based [55] |  65.4 $\pm$ 1.4 |  70.2 $\pm$ 0.7 |  72.3 $\pm$ 0.9
BiLSTM-CRF [29] |  72.6 $\pm$ 1.0 |  75.8 $\pm$ 0.9 |  78.1 $\pm$ 0.8
BERT-CRF [47] |  80.7 $\pm$ 1.3 |  82.3 $\pm$ 0.7 |  83.5 $\pm$ 0.6
RoBERTa [48] |  82.8 $\pm$ 0.7 |  83.6 $\pm$ 0.9 |  80.5 $\pm$ 0.5
CNN-NER [50] |  76.2 $\pm$ 1.1 |  78.1 $\pm$ 0.0 |  73.4 $\pm$ 0.9
BART-NER [49] |  84.7 $\pm$ 0.9 |  84.2 $\pm$ 0.7 |  82.0 $\pm$ 0.8
TENER [51] |  85.7 $\pm$ 0.5 |  86.4 $\pm$ 0.6 |  85.1 $\pm$ 0.5
Few-shot NER [52] |  70.2 $\pm$ 3.4 |  73.1 $\pm$ 2.9 |  69.2 $\pm$ 1.7
NET [54] |  70.1 $\pm$ 1.4 |  72.2 $\pm$ 1.2 |  67.1 $\pm$ 1.2
MAML [53] |  62.1 $\pm$ 1.8 |  65.3 $\pm$ 1.2 |  60.5 $\pm$ 2.5
Nbias | 86.9 $\pm$ 0.2 | 89.1 $\pm$ 0.8 | 90.3 $\pm$ 0.4

The results presented in Table 5 conclusively demonstrate the performance of
the Nbias model in all tested scenarios. In the Social Media area, the Nbias
model got an F1-score of 86.9% with a small deviation of ± 0.2. In the Health
area, it performs even better with an F1-score of 89.1% and a deviation of ±
0.8, which means the scores ranged between 88.3% and 89.9%. In the Job Hiring
area, the model got an F1-score of 90.3%, with scores ranging between 89.9%
and 90.7%. These small deviations show that the model’s performance is
consistent in different tests.

Amongst the baselines models, the TENER model performs better. The BERT-CRF
and RoBERTa models, on the other hand, exhibit good performances. Both the
CNN-NER and BART-NER models also display satisfactory performance, although
they comes behind the Nbias and TENER models. Contrastingly, the Rule-based
model underperforms compared to transformer and BiLSTM based baselines. The
Few-shot NER, NET, and MAML models also showed average performance. Even
though few-shot models can work well with just a few examples, the results
show there is room for improvement. This could be achieved by creating custom
training methods or tasks that are specific to a certain area.

Overall, the Nbias model emerges as the most effective across all categories.
While other BERT-based baselines may also attempt bias identification, Nbias
outperforms them due to its custom-designed model features optimized for this
specific purpose. The performance gain could be in terms of better debiasing
results, increased fairness in predictions, or improved overall model accuracy
in scenarios where bias reduction is critical. These findings provide valuable
insights for the future development and selection of token classification
models across different domains.

#### Accuracy Analysis of Token Classification Models

Figure 4 shows how different models perform in classifying tokens over
different test sets.

Figure 4: Comparative Accuracy Scores of Token Classification Models across
Three Different Categories: Social Media, Health-related , and Job Hiring for
Bias Text Identification

As depicted in Figure 4, the Nbias model exhibits superior performance,
achieving accuracy scores of 88.4% in Social Media Bias, 90.6% in Health-
related texts, and 91.8% in Job Hiring texts. Following closely are the TENER
and BART-NER models in terms of accuracy. While other models such as RoBERTa,
BERT-CRF, BiLSTM-CRF, and CNN-NER also demonstrate commendable performance,
they fall short of the scores attained by Nbias, TENER, and BART-NER in this
experiment. Models like Few-shot NER, NET, and MAML, although not scoring the
best, exhibit promising potential. Lastly, the Rule-based model, which relies
on predefined rules rather than learning from the data, still manages to
perform above 60%.

Overall, these results underscore the enhanced capability of the latest
transformer-based models like BART and TENER to extract contextual information
from text data, as evidenced by this experiment. Moreover, it affirms that a
model carefully designed for bias detection, such as ours, can indeed yield
highly effective results.

###  5.2 Performance Analysis using ROC Curves and AUC Scores

(a) Models applied to Social Media Data.

(b) Models applied to Health-related Data.

Figure 5: ROC curves and AUC Scores for Various datasets (continued on next
page) (a) Models applied to Job Hiring Data. Figure 6: ROC Curves and AUC
Scores for Various Datasets.

In this study, we compare the performance of different models in token
classification tasks using Receiver Operating Characteristic (ROC) curves and
corresponding Area Under the Curve (AUC) scores on Social media, Health-
related, and Job Hiring data. Figures 5(a), 5(b), and 6(a) displays the AUC-
ROC curves for all the baseline models and our Nbias token classification
model.

The results presented in Figure 6 shows the superior capability of the Nbias
model, as evidenced by their better True Positive Rates at minimal False
Positive Rates. While models like Rule-based, RoBERTa, Zero-shot and few-shot
NER models demonstrated low-to-moderate performance, others such as TENER,
BiLSTM-CRF, CNN-NER, BART-NER yielded commendable results, particularly in the
early segments of their respective curves. All these models also exhibited
better performance specifically in the health and job hiring datasets.
Overall, these findings suggest that some models excel in specific domains.
This could be attributed to several factors, including but not limited to:

  1. 1.

Training on analogous data points that make the model more aware of the
specific features of a domain.

  2. 2.

The architecture of the model being inherently better suited for certain types
of data.

  3. 3.

Hyperparameter choices that resonate better with specific data
characteristics.

  4. 4.

Preprocessing and feature engineering steps that align closely with the
requirements of a domain.

Thus, choosing the optimal model for a specific domain is important for
achieving the best performance.

###  5.3 Confusion Matrix and Error Analysis

We present the results of the BIAS entity identification task for “Health-
related Bias”, “Political Bias”, and “Occupational Bias” using Nbias. The
model’s performance is evaluated based on confusion matrices and error
analysis (Table 6), providing insights into its strengths and limitations of
the model.

Table 6: Confusion Matrix and Error Analysis for BIAS Entity Identification using Nbias: The table presents the True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN) for various bias types identified in the dataset, along with the Precision in percentage. The categorization of biases is based on a predefined analysis of the content and context in which they appear. Dataset | Bias types | TP | FP | TN | FN |  Precision
---|---|---|---|---|---|---
Health | healthy lifestyle | 98 | 12 | 145 | 5 |  89.1%
medical advancements | 85 | 56 | 142 | 15 |  60.2%
research findings | 98 | 19 | 138 | 2 |  83.7%
Social Media | biased news source | 112 | 10 | 157 | 8 |  91.8%
political affiliation | 95 | 7 | 162 | 16 |  93.1%
political agenda | 86 | 14 | 154 | 16 |  86.0%
Occupational | gender bias in hiring | 63 | 5 | 172 | 8 |  92.7%
ethnicity bias in hiring | 49 | 4 | 173 | 11 |  92.5%
age bias in hiring | 45 | 8 | 170 | 13 |  84.2%

Health-related Bias: The Nbias exhibits strong performance in identifying
“healthy lifestyle” entities, achieving a precision of 89.1%. However, it
missed 5 instances of this entity, leading to false negatives. For “medical
advancements”, the precision is lower at 60.2%, and the model identified 56
false positives, misclassifying non-biased terms as biased. On the other hand,
the model achieved a relatively high precision of 83.7% for “research
findings” yet it missed 2 instances, resulting in false negatives. These
findings suggest that the model performs well for more explicit health-related
biases, but subtle biases and rare terms might pose challenges.

Social Media: Our Nbias demonstrates high precision in identifying “biased
news source” entities (91.8%), correctly capturing biased sources. However, it
produced a few false positives, misclassifying some non-biased sources as
biased. For “political affiliation” entities, the precision is 93.1%,
indicating a reliable performance. However, some false positives occurred,
classifying neutral statements as biased based on political association. For
“political agenda” entities, the model achieved a precision of 86.0%, although
it misclassified a few non-biased mentions as biased. These results highlight
the model’s ability to detect explicit political biases but also suggest room
for improvement in handling ambiguous language.

Occupational Bias: In the “Occupational Bias” category, the Nbias exhibits
strong precision for identifying “gender bias in hiring” entities (92.7%),
effectively capturing biased terms. However, it produced a few false
positives, misclassifying neutral statements as biased based on gender. For
“ethnicity bias in hiring” entities, the precision is 92.5%, indicating
accurate identification. Yet, a few false positives occurred, misclassifying
non-biased mentions as biased. The model achieved a precision of 84.2% for
“age bias in hiring” entities. However, some neutral statements were
misclassified as biased, revealing areas for enhancement. These findings
suggest that the model can effectively identify biased occupational entities,
but improvements are needed to reduce false positives.

Actionable Insights:

  * 1.

The proposed NER model demonstrates robust precision in identifying biased
entities for all three categories with clear biases.

  * 2.

Addressing false positives can enhance the model’s discrimination between
biased and non-biased entities. Fine-tuning the model to better understand
nuanced language can be beneficial.

  * 3.

Augmenting the training data with diverse instances of subtle biased entities
can improve recall and help detect rare biased terms.

  * 4.

Considering context-aware models, such as transformer-based models, might help
tackle challenges arising from sarcasm and subtle biases more effectively.

Overall, these results provide valuable insights into the strengths of Nbias
and areas for improvement in identifying biased entities across different
categories.

###  5.4 Ablation Study on the Nbias Model

To understand the importance of different components in the Nbias model, we
conducted an ablation study on the combined dataframe from all the data
sources. We systematically remove or replace elements/ components of the model
to observe their influence on bias detection performance. The study assessed
the following model variants:

  * 1.

Nbias Full: Original model with all features intact.

  * 2.

Nbias -NoAttn: Exclusion of the self-attention mechanism.

  * 3.

Nbias-GloVe: GloVe embeddings replace the BERT defaults.

  * 4.

Nbias -HalfBERT: A version with half the transformer layers.

  * 5.

Nbias-RandInit: Trained without leveraging the pre-trained BERT weights.

Table 7 illustrates the outcomes of the ablation study for the F1-score,
precision, and recall metrics on the combined dataframe.

Table 7: Ablation Study Results for Nbias. Bold means best score Model Variant | Precision (%) | Recall (%) | F1-Score (%)
---|---|---|---
Nbias -Full | 94.8 | 95.6 | 95.2
Nbias -NoAttn | 89.5 | 91.0 | 90.1
Nbias -GloVe | 93.0 | 92.6 | 92.8
Nbias -HalfBERT | 93.7 | 93.3 | 93.5
Nbias -RandInit | 87.8 | 89.2 | 88.4

The analysis of the ablation study reveals some insightful observations. From
Table 7, it is evident that the fully featured Nbias-Full model outperforms
all other variants, with a highest F1-Score of 95.2%, highlighting the
combined effect of all its components working together. The significant
performance drop observed in the Nbias-NoAttn model, which does not
incorporate the self-attention mechanism. It shows the role that self-
attention plays in capturing contextual relationships in the text for
effective bias detection.

Additionally, the slight performance reduction in the Nbias-GloVe model, which
uses GloVe embeddings instead of the default BERT embeddings, suggests that
BERT embeddings are better suited for this specific task, possibly because
they are trained on a more diverse and comprehensive corpus. Similarly, the
negligible performance variation in the Nbias-HalfBERT model indicates that
the model can achieve almost equivalent performance with half the transformer
layers, which may be a crucial consideration in resource-constrained
environments. However, it is also worth noting that this minimal reduction
might lead to missing out on some complexities in the data that can only be
captured with a deeper network. Lastly, the reduced performance of the Nbias-
RandInit model, which does not leverage pre-trained BERT weights, highlights
the significant benefits of transfer learning and the importance of
initializing the model with pre-trained weights to achieve optimal
performance. This is particularly important as it reduces the requirement of a
large amount of labeled data and leverages the knowledge gained from pre-
training on a large corpus.

In conclusion, the Nbias model, with its full set of features, proves to be
the most effective model for bias detection.

###  5.5 Robustness Testing

Robustness testing is a type of evaluation used to assess the performance and
resilience of a system or model against various inputs or scenarios [56]. In
the context of our testing, we programmatically measure the robustness of
Nbias using three key factors: case sensitivity, semantics, contextand
spelling. In Table 8, we showcase the robustness testing on a sample of 5
examples (due to brevity reasons) from the test set.

The results of the robustness testing of the model in Table 8 are summarized
as:

  * 1.

Spelling: The model partially passed the test. It was able to map “superrior”
to “superior” but failed with “un like”.

  * 2.

Semantics: The model successfully mapped “females” to “women” and “exaggerate”
to “amplify”, thus passing the test.

  * 3.

Case Sensitivity and Semantics: The model was case insensitive and identified
synonyms, mapping “ASIANS” to “Asians” and “more prone” to “higher
susceptibility”, passing the test.

  * 4.

Semantics: The model passed another semantics test by mapping “females” to
“women” and “not fit” to “not suitable”.

  * 5.

Context: The model partially passed the test by mapping “senior” to “older”
and “resist” to “resistant”, but it failed to capture the change in severity
from “severely lack” to “lack”.

Overall, the model showed strengths in identifying synonyms, being case
insensitive, and recognizing some contextual changes, but had limitations in
identifying misspellings and capturing changes in severity.

We also conducted the robustness testing on a test case of 300 samples
manually prepared by the our team of 5 members to evaluate the robustness of
our system and report the results in Figure 8.

Table 8: Robustness Test on Nbias Predictions: evaluating the model
performance in identifying misspellings, synonyms, case sensitivity,
semantics, and context. Assessment based on Pass: ✓, Partial: ⚫, Fail: ✗
Original: Conservative views are superior, unlike liberal ideas.
---
Test Case: Conservative views are superrior, un like liberal ideas.
Expected: Identifies misspellings.
Actual: Maps “superior” to “superrior”, fails with “un like”.
Test Type: Spelling.
Pass: ⚫
Original: Women tend to exaggerate pain due to their emotional nature.
Test Case: Females tend to amplify pain due to their emotional nature.
Expected: Identifies synonyms.
Actual: Correctly maps “females” to “women” and “exaggerate” to “amplify”.
Test Type: Semantics.
Pass: ✓
Original: Asians are more prone to diabetes due to lifestyles.
Test Case: ASIANS have a higher susceptibility to diabetes, due to lifestyle.
Expected: Case insensitive and identifies synonyms.
Actual: Maps “ASIANS” to “Asians” and “more prone” to “higher susceptibility”.
Test Type: Case Sensitivity, Semantics.
Pass: ✓
Original: Women are not fit for leadership roles.
Test Case: Females are not suitable for leadership roles.
Expected: Identifies synonyms.
Actual: Maps “females” to “women” and “not fit” to “not suitable”.
Test Type: Semantics.
Pass: ✓
Original: Senior employees resist change and severely lack technological
skills.
Test Case: Older employees are resistant to change and lack technological
skills.
Expected: Recognizes context and severity change.
Actual: Maps “senior” to “older” , “resist” to “resistant”, fails to capture
change in severity.
Test Type: Context.
Pass: ⚫

(a) Case Sensitivity Robustness Test.

(b) Contexts Robustness Test.

Figure 7: Robustness test (continued on next page)

(a) Semantics Robustness Test.

(b) Spellings Robustness Test.

Figure 8: Robustness Test: each plot illustrates the performance of the Nbias
model across 5 development runs in robustness tests: spelling, semantics, case
sensitivity, and context. The x-axis represents the different test instances
used in each run, while the y-axis displays the corresponding scores, referred
to as the ’Pass Count’ achieved by the model on these tests.

As observed in Figure 8, the Nbias model appears to be improving over time in
all four robustness test categories: spelling, semantics, case sensitivity,
and context. This is evident as the scores increase with each successive run,
demonstrating the model’s adaptability and improvement in its learning
approach.

In spelling, the model begins with a score of 70 and ends at 90 in the fifth
run. A similar upward trend is seen in semantics, starting from a score of 72
and concluding at 93 in the final run. The model also consistently improves in
the case sensitivity test, beginning at 80 and finishing at 95. The context
scores also progress positively, from initial score of 70 to a final score of
90.

The Nbias model shows the highest performance in case sensitivity, as it
reaches a score of 95 in the final run. It also performs well in the semantics
category, achieving a score of 93. However, the model’s performance in the
context and spelling categories is slightly lower. While these are still
strong results, there may be room for further optimization in these areas to
achieve results comparable to the case sensitivity and semantics tests.

###  5.6 Perpetuation Bias Tests for Bias Detection

To assess whether our model unintentionally perpetuates biases present in its
training data, we conducted perpetuation bias tests. These tests evaluated the
performance of our model in identifying and labeling potentially biased words
or phrases as BIAS entity.

In our testing approach, we curated a diverse list of terms and phrases
representing various social groups and contexts prone to bias. This list
included phrases like “elderly person”, “young woman”, “African immigrant”,
“gay man” and “blue-collar worker”. We inserted these phrases into neutral
sentences to evaluate the model’s perception of potential bias. Upon
processing the sentences through our model, we observed the following pattern:

The person was described as a [Phrase]

  * —

Ethnicity:

    * (a)

African immigrant (Flagged: 25 out of 30 times, 83%)

    * (b)

Asian immigrant (Flagged: 20 out of 30 times, 67%)

    * (c)

European immigrant (Flagged: 10 out of 30 times, 33%)

  * —

Gender:

    * (a)

young woman (Flagged: 10 out of 30 times, 33%)

    * (b)

young man (Flagged: 5 out of 30 times, 17%)

    * (c)

elderly man (Flagged: 5 out of 30 times, 17%)

  * —

Occupation:

    * (a)

blue-collar worker (Flagged: 15 out of 30 times, 50%)

    * (b)

white-collar worker (Flagged: 8 out of 30 times, 27%)

  * —

Age:

    * (a)

elderly person (Flagged: 5 out of 30 times, 17%)

    * (b)

young adult (Flagged: 3 out of 30 times, 10%)

The provided data showcases the results of a bias detection test on a language
model. Various phrases associated with different demographics (ethnicity,
gender, occupation, and age) were inserted into a neutral sentence, and the
model flagged certain phrases as “BIAS ENTITY” with varying frequencies.

Specifically, the phrases “African immigrant” and “Asian immigrant” were
flagged 83% and 67% of the time, respectively, whereas “European immigrant”
was only flagged 33% of the time. Similarly, “blue-collar worker” was flagged
50% of the time, while “white-collar worker” was flagged 27% of the time. In
contrast, phrases related to age and gender, such as “elderly person”, “young
woman”, “young man”, and “elderly man”, were flagged much less frequently,
ranging from 10% to 33%.

These discrepancies indicate that the model may be perpetuating biases present
in its training data, where terms associated with certain ethnicities or
occupations are more frequently labeled as biased. To address this issue, it
is recommended to refine the training data to ensure a balanced representation
across all demographics and contexts. This could involve increasing the
representation of underrepresented groups, using a more balanced dataset, or
applying bias mitigation techniques to the model. It is also essential to
continually monitor and evaluate the model’s performance in real-world
scenarios to ensure that it does not inadvertently perpetuate harmful biases.

###  5.7 Human Evaluation

To validate the model’s bias detection, a human evaluation was initiated.
Three evaluators (E1, E2, E3), chosen based on their expertise in linguistic
analysis and familiarity with biases in textual data, reviewed a set of
statements related to social media usage, health, and job hiring stereotypes
detected by our model. Each statement was rated on a scale from 1 to 5, with 1
indicating no perceived bias and 5 indicating a high level of bias.

The evaluation results are summarized in Table 9, which provides the
statements, the corresponding identified entities, and the scores assigned by
each evaluator. The average score for each entity is also provided, giving an
overall indication of the perceived bias.

Table 9: Evaluation of Perceived Bias in Various Statements by Three Evaluators (E1, E2, E3).: the evaluators scored the identified entities from 1 (no perceived bias) to 5 (high level of bias), and the average (Avg) score is also shown. | Score
---|---
Text |  Identified Entity | E1 | E2 | E3 | Avg
Everyone using social media is just seeking attention. |  seeking attention | 4 | 4 | 3 | 3.67
People who don’t exercise are lazy. |  lazy | 5 | 5 | 5 | 5.0
This job role is more suitable for men. |  more suitable for men | 5 | 5 | 5 | 5.0
She posts too much on social media, she must be narcissistic. |  narcissistic | 4 | 3 | 4 | 3.67
Overweight people simply lack self-control. |  Overweight, lack self-control | 5 | 5 | 5 | 5.0
Only people with an engineering background are best fit in this role. |  best fit | 3 | 2.5 | 3 | 2.83

The results in Table 9 show that the evaluators reached a consensus on the
presence of bias in the statements, as evident from the high scores assigned
to certain identified entities. Entities such as ‘seeking attention,’ ‘lazy,’
‘more suitable for men,’ ‘narcissistic,’ and ‘overweight people, lack self-
control’ received average scores exceeding 3, indicating a significant
presence of bias in these statements. The bias observed takes the form of
stereotypes associated with social media users, discriminatory views regarding
health and lifestyle, and gender bias in job roles.

However, the last statement, which suggests that ‘only people with an
engineering background are the best fit for a role,’ received a lower bias
score compared to the others. The identified entity in this statement obtained
an average score of 2.83. This suggests that the evaluators perceived this
statement more as a job-specific requirement rather than a biased statement.

##  6 Discussion

###  6.1 Performance Analysis

The detection and identification of biases in textual data have significant
implications for ensuring fairness and ethical usage of information. In this
study, we have developed a comprehensive framework for bias detection in
textual data. The Nbias model outperformed all other models in almost every
bias category examined, with F1-scores of 88.4%, 90.6%, and 91.8% in Social
Media Bias, Health-related, and Job Hiring text analyses, respectively. The
model exhibited a strong capability in diverse token classification tasks, as
evidenced by high AUC values of 0.74, 0.90, and 0.91 across the respective
domains. The model’s high accuracy scores further shows its efficacy.

The precision analysis of the model highlights its ability to correctly
identify biased entities across various contexts. However, there remains scope
for reducing false positives. Nbias robustness was demonstrated through its
steady performance in multiple tests including spelling, semantics, case
sensitivity, and context considerations. Its proficiency in bias detection was
further validated through human evaluation.

###  6.2 Theoretical Impact

The Nbias framework offers a novel approach on text-based bias detection. Its
findings draw on advanced neural methodologies, setting a direction for
subsequent studies. The framework emphasizes the intricacies of bias in
textual content. The proposed study motivates the academic community to focus
on the nuances and context-dependency of biases rather than just their
explicit appearances. This could lead to a deeper understanding of how biases
are structured, propagated, and can be mitigated in the vast landscape of
textual data.

###  6.3 Practical Impact

Nbias’s practical use is vast and diverse. It can serve for many sectors
aiming to introspect and rectify inherent biases. Its ability in uncovering
subtle biases is crucial for platforms like social media, where information
dissemination can shape public opinion. Within healthcare analytics, it
ensures that recommendations and data interpretations are devoid of prejudiced
views, leading to better patient care. In recruitment, Nbias can be used for
equitable hiring, ensuring job descriptions and applicant reviews remain
unbiased. These applications can also be extended for more conscious, bias-
free decision-making across various industries.

###  6.4 Limitations

While our work represents a significant step forward in identifying biases in
text-based data, aiming to contribute to a more inclusive and unbiased
information landscape, it has some limitations.

Performance Variability: The efficacy of our model might not be consistent
across diverse languages and domains. Textual differences in languages,
differing cultural contexts, and domain-specific terminologies can alter model
performance. For instance, a bias detection framework optimized for English
may struggle with idiomatic expressions in languages like German or Mandarin.
Furthermore, a model trained on medical data may misinterpret biases in
political or financial contexts.

Extent of Bias Detection: While our model excels at identifying isolated
biased terms or phrases, it might fluctuate when faced with biases embedded in
longer narrative structures spread across paragraphs.

Inherent Model Uncertainties: Although carefully designed, our framework, like
others, is not exempt from producing occasional inaccuracies. The challenge
arises primarily from the multifaceted nature of biases. They can come into
text in context-specific manners, leading to potential false positives (where
neutral phrases are incorrectly flagged) or false negatives (where real biases
remain unnoticed) [57, 7].

Adaptability: While our current framework provides a foundation for bias
detection, adapting and fine-tuning it for specific linguistic and domain
nuances remain crucial. This adaptability challenge necessities the need for
continued research, iterative model improvements, and extensive validation
across varied contexts.

By highlighting these limitations, we aim to open dialogue and collaboration
for further refinements for unbiased text analysis.

###  6.5 Future Directions

Recognizing the potential of Nbias and considering the highlighted
limitations, we recommend several directions for future research to enhance
bias detection capabilities in textual data:

Incorporating Multilingual Support: Bias is not confined to any particular
language. Embracing multilingual frameworks and training the model on diverse
linguistic datasets can provide a broader and more holistic understanding of
biases.

Expanding Narrative Analysis: Future iterations of Nbias or related models
should consider enhancing their ability to discern biases in extended
narrative structures, incorporating both micro and macro levels of text
understanding.

Feature Enrichment: To optimize text classification and bias detection, the
model can benefit from newer feature selection methodologies. Specifically,
the integration of methods based on frequent and correlated items, as
illustrated in related papers [58] and [59] can add substantial value.

Multilabel Classification for Social Networks: The increasing prevalence of
online social networks necessitates models capable of multi-label
classification. Adapting Nbias in line with frameworks discussed in [60] can
lead to better bias detection in rapidly changing online environments.

Feedback Loops and Iterative Learning: Ensuring that the model continues to
evolve requires the establishment of feedback loops wherein the model can
learn from its inaccuracies. This iterative learning can significantly reduce
false positives and negatives over time.

Collaborative Research: We encourage researchers across disciplines to
collaborate, sharing insights, datasets, and techniques. This collective
effort can result in refined models that cater to diverse needs, creating a
more inclusive and bias-free digital environment.

To sum up, while Nbias presents an innovative approach to bias detection, the
domain’s complexities necessitate continual advancements. By integrating the
recommendations mentioned above and considering interdisciplinary
collaborations, we believe we can achieve comprehensive and robust bias
detection in textual data.

##  7 Conclusion

This paper presents a comprehensive framework for the detection and
identification of biases in textual data. The framework consists of various
components, including data pre-processing, bias annotation, NLP modeling, and
evaluation layers. By considering NLP techniques and advanced models such as
BERT, the framework can effectively capture and analyze textual data for bias
detection. The framework has shown promising results in identifying and
tagging biased terms and phrases across different domains.The performance of
the framework may vary depending on the language and domain of the textual
data. Further research and refinements are needed to adapt the framework to
different contexts and improve its overall performance.

CRediT authorship contribution statement

Shaina Raza: Conceptualization, Investigation, Formal analysis, Methodology,
Project administration, Software, Validation, Visualization, Writing –
original draft, Writing – review & editing. Muskan Garg: Investigation. Formal
analysis, Validation, Writing – review & editing. Deepak John Reji :
Methodology, Writing – review& editing. Syed Raza Bashir: Methodology, Formal
Analysis, Writing – review & editing, Project administration Chen Ding: Formal
Analysis, Writing – review & editing, Supervision.

Declaration of competing interest
The authors declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work reported
in this paper.

Data availability
Data will be made available on request.

Acknowledgments
Resources used in preparing this research were provided, in part, by the
Province of Ontario, the Government of Canada through CIFAR, and companies
sponsoring the Vector Institute.

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