Gender inequality in AI
The broad field of computer science known as artificial intelligence. It is concerned with the development of intelligent machines that can function without the need for human intervention. Today, we may imagine a machine that can work according to our requirements without our input. Artificial intelligence can be found in video games, mobile phones, automobiles, and surveillance systems, among other places. Artificial Intelligence is used by a few well-known social media platforms, including Google, Facebook, and others. AI (Artificial Intelligence) is used in Google’s or any other site’s recommendations. A virtual aid program, such as Google Assistant and Siri on the iPhone, is an example of Artificial Intelligence.
Although AI does not require much human participation, hence there is little chance of gender inequality. But is it true? Voice assistants, the majority of which have female voices, are another probable method in which artificial intelligence creates gender stereotypes. These stereotypes creates biased image in young people. This sort of mindset is extremely difficult to modify once it has been implanted in a person’s mind.
According to the results of a poll of data professionals, around one out of every five is a woman. Even though both women and men have similar levels of education, women get paid less than men. Only 22% of the world’s AI professionals are female, according to the Forum’s Global Gender Gap study, compared to 78 percent of male AI professionals. In a 2003 research of Cornell University undergraduates, psychologists discovered that women assessed their scientific ability lower than men, despite doing similarly in an exam.
There are distinct types of gender biases that can be seen in technology sector.
Based on Stereotype: -
Data is required to train the machine learning models but if the input data is biased then models would also be biased. In 2019, Facebook targeted advertisements that were based on gender. For instance, in employment advertisements women were favored for secretarial positions, whereas men were advertised as janitors and drivers. As a result, this created huge stereotype and employers were not able to target their ads.
Based on gender discrimination: -
In 2014, Amazon began working on an AI project. The main aim of the project was to evaluate the resumes of the candidates and grading using an AI based algorithm. However, By the end of the year Amazon realized that their AI based algorithm was favoring resumes of men over women. Data from the past 10 years was used to train the model but due to a greater number of males in the IT (Information Technology) industry and men were more than 50% in Amazon, this created biases against women. As a result, males’ applications were mistakenly considered by Amazon’s hiring system. Therefore, the algorithm is never used after that.
Based on lack of acceptance: -
For a long time, the IT business has been dominated by men. As a result of this lack of inclusivity, developers fail to consider the perspective of women. As a result, all the prejudices emerge. In many circumstances, this lack of inclusion results in women not receiving the same level of comfort as males, for example, many health apps do not cover menstruation-related disorders. Furthermore, speech recognition algorithms have primarily been tested on male sounds, which is why they have trouble recognizing female voices. All these concerns could have been fixed internally if there had been a significant percentage of female developers on software development teams.
Will AI be unbiased?
Input data determines the quality of AI system. An AI system that makes unbiased data-driven judgments could be produced if a training dataset free of biased assumptions about race, gender, and other ideological beliefs.
However, AI cannot be entirely unbiased in near future in the actual world as the data is created by the people. It is nearly impossible to attain unbiased human mind as well as an AI system as there are always biases and they cannot be removed from the world.
How can biasness be minimized?
On GitHub, AI Fairness 360, an open-source framework was released for detecting biases in unsupervised learning algorithm by IBM. This tool assisted AI programmers in identifying biases in models and datasets by using a comprehensive set of metrics.
The male population of the world must adjust their attitude about their female counterparts. Instead of seeing women as frail and weak, they should encourage and support them to seize every chance.
Women’s social status should also improve. The nation’s leaders should participate in this field. Women must be treated with respect and decency.
Women’s participation in surveys, studies, and AI-related jobs and professions should expand. As their percentile rises, the product will become more distinct and feminine.
It is vital to ensure that women at all levels of their careers are inspired to actively participate in the development and usage of new technologies in order to break the cycle of gender imbalance.
To create higher trust and adoption rates, it is vital to raise broad awareness and comprehension of AI. Having a broader view on users’ diverse needs and articulating the difficulties that AI can address can assist to change people’s perceptions about AI. To build and develop AI, we need to include various perspectives and disciplines. Outside of science and technology, this covers professions such as philosophy, problem-solving, ethics, education, and law.
Conclusion
Gender equality is critical to the development of a nation. It is not only important in the AI sector, but in all areas. At the root level, efforts should be made to improve data collecting. In the AI field, women’s participation should be promoted as well. Education and proper exposure to the latest technology is important for all the genders. Though making AI and data free of bias is a difficult but necessary undertaking.
