Current AI models show gender imbalances in AI and without feminist intervention a patriarchal technological world is ensured

Women in some South-Asian countries, like India, Pakistan, Bangladesh, and Afghanistan face significant hardships and problems, ranging from human trafficking to gender discrimination. Compared to their counterparts in developed countries, developing-world women encounter a biased atmosphere. Many South Asian countries have a patriarchal and male-dominated society, and their culture has a strong preference for male offspring (Kristof, 1993; Bhalotra et al., 2020; Oomman and Ganatra, 2002). These countries, particularly India, have experienced instances where technology has been utilized to create gender bias (Guilmoto, 2015). For example, there has been a great misuse of some of the techniques, of which sonography, or ultrasound, is one. The public misappropriated sonography, which was intended to determine the unborn’s health select the fetus’s gender, and perform an abortion if the fetus is female (Alkaabi et al., 2007; Akbulut-Yuksel and Rosenblum, 2012; Bowman-Smart et al., 2020). Many Indian states, particularly the northern states of India, have seen a significant drop in the sex ratio due to this erroneous use of technology. India’s unbalanced child sex ratio has rapidly deteriorated. In 1981, there were 962 females in the 0–6-year age group for every 1000 boys. In 1991, there were 945 girls, then 927 girls in 2001, and 918 girls at the time of the 2011 Census (Bose, 2011; Hu and Schlosser, 2012). In 1994, the Indian government passed the Pre-conception and Pre-Natal Diagnostic Techniques (Prohibition of Sex Selection) Act to halt this trend. The law made it unlawful for medical practitioners to divulge the sex of a fetus due to worries that ultrasound technology was being used to detect the sex of the unborn child and terminate the female fetus (Nidadavolu and Bracken, 2006; Ahankari et al., 2015). However, as the Indian Census data over the years demonstrates, the law did not operate as well as intended. According to public health campaigners, this is due to the Indian government’s failure to enforce the law effectively; sex determination and female feticide continue due to medical practitioners’ lack of oversight. Radiologists and gynecologists, on the other hand, argue that it’s because the law was misguided from the outset, holding the medical profession responsible for a societal problem (Tripathi, 2016).

In the realm of artificial intelligence, gender imbalance is a critical issue. Because of how AI systems are developed, gender bias in AI can be problematic. Algorithm developers may be unaware of their prejudices, and implicit biases and unknowingly pass their socially rooted gender prejudices onto robots. This is evident because the present trends in machine learning reinforce historical stereotypes of women, such as humility, mildness, and the need for protection. For instance, security robots are primarily male, but service and sex robots are primarily female. Another example is AI-driven risk analysis in the justice system. The algorithms may overlook the fact that women are less likely to re-offend than men, placing women at a disadvantage. Female gendering increases bots’ perceived humanity and the acceptability of AI. Consumers believe that female AI is more humane and more reliable and therefore more ready to meet users’ particular demands. Because the feminization of robots boosts their marketability, AI is attempting to humanize them by embedding feminist characteristics and striving for acceptability in a male-dominated robotic society. There’s a risk that machine learning technologies will wind up having biases encoded if women don’t make a significant contribution. Diverse teams made up of both men and women are not just better at recognizing skewed data, but they’re also more likely to spot issues that could have dire societal consequences. While women’s characteristics are highly valued in AI robots, protein-basedFootnote1 women’s jobs are in jeopardy. Presently, women hold only 22% of worldwide AI positions while men hold 78% (World Economic Forum Report, 2018). According to another study by wired.com, only 12% of machine learning researchers are women, which is a concerning ratio for a subject that is meant to transform society (Simonite, 2018). Because women make up such a small percentage of the technological workforce, technology may become the tangible incarnation of male power in the coming years. The situation in the developing world is worse, and hence a cautious approach is required to ensure that a male-dominated society in some of the developing world’s countries does not abuse AI’s power and use it to exacerbate the predicament of women who are already in a precarious situation.

This manuscript discusses the relationship between women, technology, manifestation, and probable prospects in the developing world. Taking India as a case study, the manuscript further focuses on how the ontology and epistemology perspectives used in the fields of AI, and robotics will affect the futures of women in developing countries. Women in the developing world, particularly in South Asia, are stereotyped as doing domestic work and have low representation in high-level positions. They are disproportionately underemployed and endure employment discrimination. The article will attempt to explore, whether the introduction of AI would exacerbate the already fragile position of women in South Asia or serve as a liberating force for them. While studies on the influence of AI on women have been conducted in industrialized countries, research in developing countries has been minimal. This manuscript will aim to bridge this gap.

The study tests the following hypothesis:

H01: The changing lifestyle, growing challenges, and increasing use of AI robots in daily life may not be a threat to the existing human relationship.

HA1: The changing lifestyle, growing challenges, and increasing use of AI robots in daily life may be a threat to the existing human relationship.

H02: There is no significant difference in the perception of females and males towards AI robots.

HA2: There is a significant difference in the perception of females and males towards AI robots.

H03: There is no major variation in the requirements and utilization of AI robots between men and women.

HA3: There is a considerable difference in the requirements and use of AI robots between men and women.

H04: When it comes to the gender of the robots, there is no substantial difference between male and female preferences.

HA4: When it comes to the gender of the robots, there is a substantial difference between male and female preferences.

The manuscript is divided into four sections. The first section is an introduction, followed by a literature review, methodology, results and discussion, and finally, a conclusion.