In machine learning models, a growing awareness of bias effects exist. For instance, facial recognition software that is embedded in most smartphones works best for those who are male and white. It is of no surprise that today, Artificial Intelligent models are learning gender bias problems using a data set based on human traits. For instance, an ingredient in AI models that is Natural Language Processing (NLP) is harnessed in various devices such as Siri by Apple, Google Assistant, Alexa by Amazon, etc.
These devices show gender biases. Many technologies and algorithms, for example, Computer Vision (CV) models are failed to provide an accurate report depicting gender differentiation. They give high error rates while recognizing women, especially those who have a dark complexion. More effort and improved technologies are needed based on deep research that goes well and eliminates gender bias in identification and differentiation between gender.
Scoring systems are used increasingly that are based on biased algorithms and make decisions about the interest of people with respect to jobs, e-commerce, insurance, etc. In Artificial Intelligence, the debate is about gender bias particularly. There is a need for data scientists to look into the matter deeply to eliminate the issue of bias and for women negative consequences could be well handled.
Feminist Studies and Gender
To better understand the behavioral and
language differentiation, feminist studies are done that better portray the
machine learning model infrastructure to deter wrong results while
differentiating between men and women. Gender ideologies are embedded in corpus
and text sources that are used for model training and testing. These ideologies
help build a frictionless machine learning model and stereotypical concepts.
This article presents some ways that
contribute to the idea of successfully differentiating between men and women
using improved ML and AI models. Word embeddings should be identified with
better precision using Natural Language Processing techniques and underlying
algorithms that after identifying the pitch, frequency and other parameters in
the voice and based on them, give results providing whether vocal waves
correspond to man or woman.
Bias Using Text
Linguistic language features are identified
in the corpus containing data in form of text. The computational approach is
used to identify gender bias in the text that furthermore helps in learning for
machine learning models. The following are some of the concepts that help provide an abstract
understanding that can contribute to deal with gender bias in the text.
Addressing gender bias with both critical and theoretical perspectives helps in
feature extraction in machine learning models.
In gender bias, recognition and
differentiation are done based on grouping. Categories related to men and women
are defined. For instance, how the father corresponds to a family man and
single mum corresponds to the working woman, all these terms are redefined and
in the corpus, the categories are illustrated. With respect to each feature,
the name in a category is given.
In language, gender bias is evident in
items ordering in the lists. For example, in English, the convention while
naming pair is used in which the first male name is represented and after woman
such as son and daughter, Mr and Mrs, husband and wife, etc. This practice is
also needed to be considered while training machine learning models.
Men are most of the time represented with
respect to behavior and women are most of the time represented in terms of
appearance. The adjectives should be extracted and considered while training
models to incorporate them at the time of gender proofing.
Metaphor identification helps smoothen the
gender identification if done efficiently. In the text, metaphors should be
identified and their use is considered in that particular context. Women
metaphors are considered more prolific and disparaging as compared to men.
Role of Emotion AI
With respect to emotion AI, gender biases
is studied. Emotion AI is penetrating in various industrial use-cases. Bias in
humans occurs when a person is misinterpreted with respect to the emotions. For
example, thinking that gender is angrier as compared to others. Machines learn
the same and misinterpret emotions of individuals, hence give biased results.
To dig out the reason for bias, let’s look into the causes that give birth to
Causes of AI Bias
Talking about gender bias in the context of
AI and machine learning means that there is a high difference in the
identification of gender characteristics. Various aspects contribute to gender
bias and these variables should be taken into account by the developers and
machine learning models training. Some of the factors may include;
A skewed or incomplete training dataset is
most of the time a reason behind an AI model to give expected answers. Because,
when demographic categories are not present in the training dataset, they are
considered incomplete. The machine learning or artificial intelligence models
that are trained and developed against this dataset seems not to behave
according to what is expected because when in real-life, communication is done,
the model that is not scalable will most likely behave strangely. For instance,
while distributing the training and testing data set, in the training dataset,
data containing female speakers is only 15%, and in testing on the machine
learning model is done against females, there will be more chances of errors.
Labels to Words in Corpus:
In commercial AI models, supervised machine
learning is used, all the training data is labeled to teach the machine
learning models that how to behave in certain circumstances. Humans come up
with relevant labels against the categories in which a label lies. So times,
labeling gets complex that it splits certain labels into irrelevant categories
and hence result in confusing machine learning models. After assigning labels,
models are trained on them such that they start learning that for which feature
what label needs to be considered. Whenever a wrong label is assigned knowingly
or unknowingly in the gender category, misclassification leads to gender bias.
Sometimes numerical measurements are used
in machine learning models as inputs that have a major difference which let
them lie in different categories. For instance, in the beginning, speech-to-text
models and technologies failed to clearly differentiate between the male and
female voice. From this, it analyzed that machine learning models work fine in
detecting voice that has high frequency, low pitch, and longer vocal cords. As
the female voice is high-pitched, models fail to differentiate them from male
voices. This degree of misclassification leads to gender bias which needs to
remove in upcoming machine learning models.
How to Address Gender Bias?
Make sure the following three things:
Diverse Training Dataset
A huge training data set with equivalent diversity of both male and female sample data categories can help train machine learning models with better accuracy and identification. The audio samples of both male and female should be equal so that models learn individually each audio and successfully differentiate them based on different vocal traits.
Diverse Background Categories
The training data set should be collected from diverse backgrounds. The reason is, people living in different areas have different ascent, models should be trained generically so that they would be able to entertain male and female voices belonging to different regions and categories.
Category Individual Accuracy Calculation
Developers should ensure that the machine learning models measure the accuracy for each category separately based on demographics. Each category should be treated equally for better results and to combat gender bias.
Many Other Applications
Identification of machine learning models, their causes, and improvements should be addressed keeping into account all the inconsistencies of machine learning models. The issue of gender bias also exists while recognizing facial features in the face recognition technology and differentiating between men and women. At an industrial level, biometric software is used to identify individuals in real-time. However, improvements are still on-going that needs to consider various other parameters that contribute to the betterment of AI models. Future research will be focusing on wider gender variants and their representations to expand the scope of machine learning models that are generic and fits well in the overall problem category.