The Contribution Of AI And Digital Technologies To Addressing The Gender Gap At Work
Introduction – Good News is Important Too
Ask the average person about “AI and women’s jobs” and the topic will soon veer into concerns – the machine will steal her job, will discriminate against her, will leave her behind since she lacks a smart phone. There are good reasons for these fears; for example, research by the International Labour Organization reveals that women’s jobs are nearly twice as susceptible to the impact of generative AI than men’s jobs (ILO, 2025). This is an important finding, and it has been analyzed further in another accompanying paper.
However, when the story ends here, what results are incomplete and subtly discouraging picture. One which implies that women are only to be viewed as victims of technology, not as its users, creators of business on its foundation or benefactors of its advancements. Evidence, again, does not point towards this lopsided perspective either. Millions of women who would never have been able to secure a loan from a bank are now able to do so because an algorithm, and not a branch manager who did not believe in them, studied their mobile payment record. Women homemakers who were told that selling something for money was ‘not respectable’ are now running small businesses off their smartphones. Women who were the victims of bias and discrimination during human interviews are now being applied and selected for jobs in controlled tests after the hiring algorithm has been adjusted for that very bias. Thus, this paper will present this side of the story too, in simple terms, so that the debate on “gender, technology and the changing nature of work” is complete.
One thing needs to be said right up front- this is not an assertion that technology is inherently neutral, inherently fair or necessarily beneficial to all. This is an assertion that technology is a tool, and that such tools can be used in a helpful way if humans decide to point them that way and establish appropriate guidelines for their use. The examples presented in this paper are evidence of this possibility, not evidence that it occurs.
The following pages examine five distinct realms within which this “helpful direction” has already brought tangible benefits to women: access to credit and financial services, entrepreneurship through e-commerce and reselling websites; digital remote and flexible employment; hiring processes made more equitable through algorithmic debiasing; and access to information- financial, agricultural and medical – through vernacular voice-based AI. Each of the sections will describe the positive impact in question, provide specific examples, and outline any limitations that exist to that positive impact. The paper concludes with a brief section discussing some limitations, as a strictly one-sided presentation of the topic would be as misguided as its opposite.
Obtaining Loans Without a Male Co-signer- AI and Financial Inclusion
All over the world, one billion women have absolutely nothing to do with the banking sector, having neither bank accounts nor any form of credit. There is one very simple explanation for why this is the case: financial institutions determine the eligibility for loans based on criteria that women in most parts of the world cannot provide – property under their name, long history of formal employment, or co-signer. It is not a technological issue but rather an age-old problem. However, AI is opening new possibilities.
Rather than assessing whether she owns land or not, machine learning tools can analyze her daily transactions, such as how frequently she receives and spends mobile money, her cash flow throughout the year in terms of small business revenue, her repayment history of small loans that she may have taken in the past. With this information, women who lack any collateral or credit history can be deemed worthy of credit through an algorithm. Machine learning to eliminate any biases in digital lending decisions and increase women’s access to credit has been explored by Women’s World Banking, together with the University of Zurich, and their efforts have helped more than 159 million women customers in 34 countries.
Generative AI solutions offer another advantage for the inclusion of disadvantaged groups – the language. In rural India, the voice-based artificial intelligence operating in native language is opening new avenues for women who cannot speak English, let alone read it. This is significant, as illiteracy and inability to read in English are often unspoken barriers to accessing finance in India.
The potential is significant. According to estimates by the World Economic Forum, there is $5 trillion at stake globally in terms of closing the gap in digital and financial inclusion for women entrepreneurs – an amount that corresponds to the GDP of Japan (about the same size), and women entrepreneurs with access to the internet are 2.5 times more likely to apply AI technology for their business optimization. It means that the women who manage to go online do not just consume AI but use it for optimizing their business operations.
This is an important point to make since the positive effects of financial inclusion are broader than just the individual borrower. The research shows that access to financing and money in general for women leads to a wide range of positive results both for the households and communities. Women with access to funds see positive changes in health of their families, more freedom in decision making at the family level, and improvements in educational outcomes for their children as well as their diet. The inclusion of women into the process of accessing credit does not stay only within the realm of economics.
From Homemaker to Entrepreneur: What Reselling and E-Commerce Apps Have Done
One of the best examples that showcase how technology has paved the way for women empowerment in India comes from the social commerce reselling apps that have emerged in recent times, one of which is known as Meesho. In layman’s terms, a woman registers herself in the application for free, selects a product from a list provided by the company, and then markets the same through her social media networks such as WhatsApp, Facebook, and Instagram.
It becomes important to note this in terms of an essay on gender and work because of the people who use it. According to Meesho’s numbers itself, around 70 per cent of the resellers in this platform are women; these are women who are homemakers and have no previous exposure to business or even any financial capacity to start one. The barriers which have been holding Indian women back from entering the business space because of the need for capital, the need to travel to a store or marketplace, or the need to get family approval to work outside the home are all broken down and circumvented by this system. There is no stock to purchase, no shop to rent, and the workplace consists of a smart phone inside one’s home. Case studies conducted by industrial researchers reveal homemakers generating an additional income of about ₹20,000 to ₹25,000 every month via such platforms, which in many homes becomes the first income of the woman.
It should not be glorified either – reselling incomes tend to be unreliable and small, and they lack job security and other benefits that come with formal employment. But it would be wrong to underestimate it as well. For a woman who never had any income of her own before, monthly income of ₹20,000 earned from the comfort of her own living room, without having to ask anyone for permission to leave the house, may be a genuine change, which will mark the beginning of the journey towards economic empowerment rather than its conclusion.
Alongside these incomes, there is a more silent change happening as well. These platforms try to promote reselling by targeting homemakers through advertising which uses language to make selling one’s products an accepted identity rather than just an occupation – Meesho advertises using the slogan “Not just a homemaker, a Meesho Entrepreneur”. It is important since in the Indian context, “homemaker” and “entrepreneur” were two incompatible identities until recently. Being able to call oneself and be recognized as an entrepreneur by others – family members included is much more important than the monthly income.
Working from Home: Flexible and Virtual Work Using Digital Technology
One thing that keeps coming up in labour studies about India is that whether a woman is willing to do work depends more on whether that work is compatible with her needs than whether there is work available. Flexible work that involves the use of digital technology doesn’t get rid of any of those issues; what it does do is allow women to make room for paid work through them.
Online freelancing, data/content work done remotely, online tutoring, telecalling, online customer support are all jobs which can be done from home, on flexible timing, without the dangers of commute and family pressures due to long night shifts which can come with regular jobs that one needs to go out of the house for. For women living in conservative families, in small towns with no reliable public transport, or those who have to juggle child-care and paid employment, it is often the only way in which they make money as opposed to not making any at all, not due to lack of skills or willingness, but simply because the ‘fit’ of the job works for their schedule, not the other way round.
In even such gig jobs as gig delivery and ride-hailing, which are, as any research shows, not very attractive for women, the relatively few women involved tend to be exactly those dealing with childcare, and they exploit the flexibility of the platform in order to do paid work at times which no regular job would permit. This certainly does not mean that gig platforms have finally found the solution to women’s underrepresentation in the workforce – far from it, the gender gap in terms of their participation in delivery and ride-hailing is still quite significant. However, this does prove that the flexibility works exactly as it was meant to for the women involved.
Debiasing with the Tool That Could Do the Same Damage: Algoritms for Hiring Free from Bias
It seems quite paradoxical to speak about algorithms of hiring as good news for women, considering all the focus made on discrimination of female applicants by such algoritms – there is the famous case of an internal algorithm for recruiting that was learning to punish applications including the word “women’s”. However, researchers working on this issue did not only diagnose the problem, but also checked the remedy, which proved to be effective.
In a new experiment in the ILR Review, the effects of discovering bias in an AI hiring system and subsequently correcting it, which the scientists call “debiasing” were analyzed. As a result, it was discovered that merely letting applicants know about the bias in a hiring algorithm was, on its own, sufficient for deterring eligible women from applying for the highly competitive position dominated by men. Once the bias was removed and the process was transparently communicated to the applicants, however, the number of women applying, including the most qualified ones, grew considerably without any drop in the overall quality of candidates. To put it briefly, a biased algorithm silently deters qualified women from applying in the first place, whereas a fixed one brings them back, and does so more efficiently and more effectively than any campaign aimed at human recruiters.
And this is a key, often overlooked aspect of any consideration of “gender, technology and the future of work”– while the assumptions underlying a human recruiter’s assessment process cannot in practice be reviewed, measured, and disproved in an individual instance, the rules on which an algorithm-based assessment is based can, in theory, be tested, audited, and improved – and the improvement can then be implemented for every single application processed by the system, rather than for the individual case a lawyer decided to litigate. A biased human recruitment panel is notoriously difficult to improve through piecemeal intervention; an algorithm-based system that is found to be biased can, in theory, be corrected once and for all, thus providing a solution that would help every woman applying for the position in question. Technology, therefore, can become a faster and more effective way of ensuring fairness than the existing system – on one condition, which is essential.
Connecting With Indian Women That Text-Based Technology Missed
One of the quietest ways that the latest generation of AI, namely the generative AI models that can converse in natural language, helps is by eliminating the two major obstacles that have prevented countless women in India from getting online: literacy and fluency in writing and typing in English. The numbers from India itself speak to the size of the problem: more than half of women in India are unable to send or receive emails, and they trail men by 24 percent in conducting online banking transactions.
Voice-first and language-specific AI solutions provide a straightforward solution for just such a problem, in that no typing or knowledge of English is necessary – a woman only needs to ask her question aloud in Hindi, Tamil, Bengali, or some other Indian language and receive an oral response. This approach has been called “Redefining financial inclusion by bridging the critical gaps in language, literacy, and trust” by the World Economic Forum, and rural Indian women have been identified as one particular target demographic for which this transition would be the most impactful, since it is the language and literacy, not the lack of motivation or need, that has kept them excluded.
The same reasoning can be extended into non-banking sectors as well. The use of voice- and vernacular-oriented AI advisers to provide agricultural advice to female farmers, basic health advice to female users who may hesitate to go to a clinic and pose their questions there, and government scheme information to female users who are unable to navigate a text-based government site is increasingly common. These applications do not resolve the issue of digital divide that was described above but rather reduce another, separate barrier – the barrier related to literacy and language skills that would remain even with access to connectivity.
Questioning without Shyness: AI and Women’s Health Information
The more subtle but certainly significant advantage of artificial intelligence chatbots and health information sources is that they provide women with an opportunity to ask the questions which they would not otherwise dare to pose aloud. Issues such as menstrual health, sexual health, contraceptive choices, and worries related to pregnancy remain quite sensitive topics for discussion with family members, male doctors, or even female doctors in crowded clinics. The AI chatbot neither passes judgment nor gossips; moreover, a woman does not have to speak any embarrassing words to another person to receive an honest reply.
None of these is a substitute for a medical professional, and no legitimate use of these methods claims this – there is simply no way a chatbot could examine, diagnose, and order tests for a condition not covered in text form. However, what these tools could accomplish is reduce the very first step: to help a woman determine whether she needs to see a doctor at all, to understand a diagnosis and a prescription that uses complex medical terms, or at least to know enough to pose a more detailed question to her doctor during the consultation. In small towns and villages, where a gynecologist might take hours of travel from the area and seeing him or her would require some explaining to the family that the woman would rather avoid, the first step, taken without anyone else knowing about it, could literally mean life or death to a problem getting detected early on.
Acknowledging the Limitations
An article that highlights the advantages of technological advancements for women must not over-exaggerate these benefits, and the current piece of writing will not. In each of the examples mentioned above, there is a limitation that should be taken into consideration. The use of AI algorithms in loaning decreases the amount of bias in decision-making, but it does not guarantee that such bias is not present, as it still depends on the way an algorithm was developed. There may be opportunities related to reselling and gig work, but these opportunities are not stable, and one cannot benefit from them in the same manner as he or she would from having a stable job with social security and leave of absence. Flexibility in remote work increases access to new opportunities, but the amount of unpaid household labor makes it difficult for a person to make use of these opportunities. Finally, any debiasing of a hiring algorithm does not help unless an organization decides to check it out and disclose results.
This paper does not suggest that such caveats should be disregarded. What it seeks to do is to demonstrate that such caveats do not tell the whole truth, and when a field of research always gives you the cautionary tale of the story, there will inevitably be a form of bias present – that of assuming technology can take away from women and not give them anything new.
Conclusion
Whether technology treats women well or badly is not an inherent property of technology – rather, technology will do whatever the developers, funders, regulators and implementors of the technology want and are careful enough to ensure that it will do. In this paper, it has been attempted to show, through clear explanation and examples, what these deliberate and careful decisions look like when they work well: an algorithm that reads a woman’s payment history in her mobile phone instead of demanding a male guarantor; an app that transforms a homemaker’s smart phone into a shop without requiring her to step out of her home; a recruitment algorithm that is thoroughly checked and corrected for bias, thereby enabling women to be part of the candidate pool once again; a voice-enabled technology that communicates to a woman in her own language, thus eliminating illiteracy from preventing her access to her bank account.
Nothing about any of this negates the actual risks that have been discussed in other literature – automation, algorithmic discrimination in which no one is looking, and the digital divide that means millions of women remain unconnected. However, a full account of “gender, technology, and the changing nature of work” must consider both sides of the story – the damage that is done if technology goes unchallenged and the benefits that emerge when it is not. It is only in doing so that we will really be able to learn from success.
