"India AI Mission must address gender gaps to ensure inclusive AI development"
Algorithmic bias
systematic errors in AI outputs caused by unrepresentative or historically skewed training data
AI pipeline leakage
progressive attrition of women (or marginalised groups) at each successive stage of AI education, workforce, and leadership
India's ambition to become a global AI powerhouse rests on a foundation of Digital Public Infrastructure (DPI) — the stack of open, interoperable digital systems including Aadhaar, UPI, and the Open Network for Digital Commerce (ONDC) — that has demonstrated how technology can deliver public welfare at scale. This DPI-first philosophy now underpins the India AI Mission, a government initiative aimed at building sovereign AI capabilities through compute infrastructure, foundational model development, and a national dataset ecosystem. The constitutional commitment to equality, enshrined since 1950 when India granted universal adult franchise to men and women simultaneously, provides the normative anchor for arguing that AI — like the franchise — must be genuinely universal in both design and benefit.
The gender dimension of technology access in India has a long policy history. Successive National Policies for Women, the Beti Bachao Beti Padhao scheme, and the STEM education push under the National Education Policy (NEP) 2020 have sought to close structural gaps in education and workforce participation. Yet these interventions have not prevented a systematic attrition of women from the technology pipeline — a pattern that now threatens to replicate itself, at greater speed and consequence, in the AI era. The Bhashini AI platform under MeitY and the broader DPI architecture offer infrastructure that could, if designed inclusively, reach women in rural and semi-urban India who have historically been excluded from digital services.
The concept of algorithmic bias refers to the tendency of AI systems to reproduce, and sometimes amplify, the inequalities embedded in their training data. Because AI models learn statistical patterns from historical data, and because historical data reflects societies structured by gender, caste, class, and geography, the outputs of AI systems can systematically disadvantage already-marginalised groups. A credit-scoring model trained predominantly on male financial histories may underestimate the creditworthiness of women borrowers who operate through informal savings networks such as Self-Help Groups (SHGs). A maternal health AI tool trained on urban or upper-income health data may generate inaccurate recommendations for rural women with different nutritional and epidemiological profiles.
The AI pipeline leakage problem describes the progressive attrition of women at each successive stage of the AI value chain. …
With the present state of development, Artificial Intelligence can effectively do which of the following?
1.Bring down electricity consumption in industrial units
2.Create meaningful short stories and songs
3.Disease diagnosis
4.Text-to-Speech Conversion
5.Wireless transmission of electrical energy
Select the correct answer using the code given below:
Which of the following is/are the aim/aims of "Digital India" Plan of the Government of India ?
1.Formation of India's own Internet companies like China did.
2.Establish a policy framework to encourage overseas multinational corporations that collect Big Data to build their large data centres within our national geographical boundaries.
3.Connect many of our villages to the Internet and bring Wi-Fi to many of our schools, public places and major tourist centres.
Select the correct answer using the code given below :
Consider the following statements regarding the AI Action Summit held in Grand Palais, Paris in February 2025:
I.Co-chaired with India, the event builds on the advances made at the Bletchley Park Summit held in 2023 and the Seoul Summit held in 2024.
II.Along with other countries, US and UK also signed the declaration on inclusive and sustainable AI.
Which of the statements given above is/are correct?
Consider the following statements regarding women's representation in India's AI ecosystem:
1.Women constitute approximately 43 per cent of India's STEM graduates.
2.Women hold approximately 26 per cent of advanced AI professional roles.
3.Women account for approximately 10 per cent of senior AI leadership positions.
Which of the statements given above is/are correct?
Which of the following best describes the concept of 'algorithmic bias' in the context of AI systems?
With reference to the India AI Mission, consider the following statements:
1.It aims to build sovereign AI capabilities through compute infrastructure, foundational model development, and a national dataset ecosystem.
2.The Bhashini platform, developed under MeitY, provides multilingual AI translation and is being integrated into India's AI governance architecture.
3.The India AI Mission explicitly mandates gender-representative data audits in all publicly funded AI projects.
Which of the statements given above is/are correct?
Assertion (A): AI credit-scoring models trained predominantly on male financial histories may systematically underestimate the creditworthiness of women borrowers in India.
Reason (R): Women's financial activity in India is often channelled through informal Self-Help Group savings networks that are largely absent from formal financial datasets used to train AI models.
Which one of the following is correct?
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