AI and Gender - Addressing Gender Bias

In 2019, a news broke on the internet when a couple applied for the credit cards. Despite having similar expenses and salaries, Apple has issued different cards to the company. The credit limit of the husband is 20 times higher than that of the wife. 

 

And when the couple complained about this stark inequality to customer care, they simply blamed it on system algorithms. Now the question arises, can machines and algorithms too partialize between males and females? This is not just a question on a single algorithm but a concern to the entire concept of AI. In this blog, we will shed light on AI and Gender bias as well as all the aspects related to it.

 

Suggested Blog - What is AI?

 

What do you mean by AI?

 

AI expands for artificial intelligence. This refers to the technology that develops the logistics and problem-solving ability of machines similar to human minds. The problems here refer to the technical tasks such as voice recognition, face detection, machine algorithms, and computing queries. 

 

AI and machine learning together are revolutionizing the present sketch of the computing map. The common examples of AI include androids, social media applications like Instagram and Facebook, robotics, computers, and other machines.

 

(Suggested Read - AI and Big Data in Instagram)

 

 

What is the meaning of AI algorithms?

 

AI Algorithms can be defined as a subset of machine language. These algorithms help computers to learn on their own. Algorithms in simple language are defined as mathematical queries that are created by the system before proceeding for a task.  

 

How does an AI algorithm work?

 

As mentioned above, AI algorithms are the set of queries created by the system when commanded a task. Now you might be wondering, how does the computer solve these queries on its own? So, here’s your answer.

 

AI works by studying the collection of different data. AI combines large data. In this process, the defined data is filtered through several passes, until the meaning of undefined data is interpreted. And then the query is solved and the task is executed. 

 

Learn more about the process from digital silk’s experts, process of AI algorithms.

 

 

What is Gender Bias in AI?

 

Gender bias is simply defined as differentiating the genders and considering one of them as fragile or weak, especially women. Everyday around us we see several examples of discrimination on the basis of gender, but in last few years the topic has been seen on the AI platform too. Where the same algorithm has responded to different results for a male and a female. This discrimination is termed as “gender bias in AI”. 

 

(Suggested Read - Top Classification Algorithms Using Python)
 

How this often turns into an evil. Read here.

 

Are AI systems really Gender Biased?

 

Whatever is created by the human is biased to one or other extent. Let us consider a simple example for it, if you are making a paper boat, you will definitely choose paper of your favorite color for making a boat. 

 

This might be a little thing for you, in fact it is really an obvious thing depicting human nature. Same happens with AI platforms. As mentioned above AI depends on the codes or data collected and stored. 

 

Most of the analysts, programmers, developers, and coders are male, and unconsciously they are adding features in the system that are sexism in nature. And this results in a gender biased AI system. So, the ultimate answer is, yes, AI systems are generally gender biased in nature.

 

How is Gender Inequality embedded in the system?

 

Let us now understand how this gender inequity is fed into the AI system. The major two reasons for the purpose.

 

1. Only 22 percent of women are contributing to AI professions and data science jobs. Now when the share of male contributors is more in the developing stage, the final results will ultimately have more masculine features.

 

Read the report, gender gap in AI jobs.

 

2. The second reason is the problem of root level collection of data, at root level, humans generate, collect, and label the data. This data later goes into datasets. 

 

Thus, indirectly humans determine what datasets and rules will the algorithms learn to make predictions. And whenever a person delivers a task, he delivers his mindset and ideologies along with the task.

 

These two factors explain why and how gender discrimination is embedded into the AI and other systems.

 

What is the impact of Gender Biased AI?

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Impact of Gender Biased AI

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Anything which is biased in nature is harmful for the environment. Gender inequality is not only toxic for individuals, but is also harmful for the community. It is a challenge to women empowerment and gender equality. The reports of SSIR draws following impacts of gender-biased AI:

 

Lower quality of service:

 

The gender-biased AI data provides low quality of services, especially for women. The poor quality of data results in worse challenges for women in various fields.

 

 

Unfair allocation of resources:

 

The gender-biased AI data is collected from unfair resources. These resources manifest the resulting logics and observation.

 

( Related blog - AI Analytics )

 

 

Decreases opportunities:

 

The incorrect data collected results in lesser opportunities for women. As the data modify the actual observations related to jobs and workplace.

 

 

Harmful stereotypes:

 

The gender-biased AI can also create harmful stereotypes in the mindsets of readers and the public.

 

 

Derogatory treatment:

 

The gender-biased AI often results in critical social conditions.

 

 

Danger to physical safety:

 

The gender-biased AI data and algorithms are dangerous to the physical safety of women. As it might create trust issues in the society and family.

 

Why is the contribution of women lesser than men in AI professions?

 

The headline might have popped up in your mind after reading the reason one. Here is the list of reasons why the contribution of women is only 22 percent when compared to 72 percent of opposite genders.

 

Internet access

 

According to a study, more than 300 million women, when compared to the same ratio of men, are lagging with the connectivity to the internet. The studies imply that males are more in contact with the internet than women. This is a major reason for the lesser participation of women in AI jobs and professions.

 

Availability of smartphones

 

A study shows that 20 percent women are less likely to own a smart device like laptops or smartphones in comparison to men. The data is more relevant in under-developed and developing countries. Due to this reason, women lack participation in AI related sectors and masculine presence automatically increases.

 

Unequal contribution in observation and studies

 

Due to family and social reasons, a few percentiles of women participate in the data collection. Thus, impacting the results and observation of the reports. Indian women don’t even participate in clinical trials. 

 

Women as a source of information don't participate equally to men. And that’s why, they can expect AI algorithms to not be inclined towards masculinity. 

 

Insecurity of females

 

The women are afraid of internet risks and challenges. Many times, when they login to sites like Facebook and Twitter, they sign in with a male detail. This again interferes with the end data.

 

(Related blog - Big Data in Facebook)

 

These factors, one together combine with each other and result in the lesser percentile of women in the field of AI.

 

 

How can the issue of Gender Inequality in AI be resolved?

 

Some of the measures suggested below can be implemented for resolving the issues of gender biased :

 

More participation of women : The participation of women should increase in surveys, reports, and AI related jobs and professions. Once their percentile increases, the result will automatically be more different and feminine.

 

A change in the way of thinking in opposite gender: The male percentage of the globe needs to change their perspective towards their counterparts. Rather than thinking females as fragile and weak, they should motivate and support the females to grab every opportunity.  

 

Increase in the social status of women: The social status of women should also increase. The leaders of the nation should play their part in this field. Women should be treated with dignity and respect.

 

Gender equality plays an important role in building a nation. Not only in the term AI sectors, but it is essential in all sectors. Efforts should be taken to improve the data collection at root levels. The participation of women should also be encouraged in the AI sector. Though, it is a tough but still an important task to make AI and data biased-free.