Diversity in Training Dataset to Cater Gender Bias in Machine Learning Models

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 Ideologies

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.

Learning 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.

Naming

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.

Ordering

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.

Descriptions

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

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 AI bias.

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;

Insufficient Training Corpus:

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.

Assigning 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.

Modeling Techniques:

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.