Digital disparity in India's digital sphere and the resulting favoritism towards the affluent
In India, the push towards data-driven governance has raised concerns, particularly for marginalized populations, including women in the informal sector. A study revealed that about 36% of 200 migrant women workers faced biometric authentication failures during pregnancy-related hospital visits, highlighting the challenges they face in accessing government welfare schemes [1].
The issue of algorithmic bias is significant, as demonstrated by examples such as Google's autosuggestions and ChatGPT's letter of recommendation generation, which show gender and racial biases. In the context of India, AI-powered recruitment or service delivery systems may replicate caste, gender, and language biases, leading to exclusion from benefits or fair opportunities [1].
Moreover, the digital divide—marked by low digital literacy, limited smartphone or internet access, and distrust of digital tools—restricts informal women workers from fully utilizing government digital initiatives. Only about 24% of the population is financially literate, with even fewer women and informal workers able to navigate digital platforms [3].
The move towards direct benefit transfer is making things worse for those not recognised in the system. Biometric failures in systems like Aadhaar and the public distribution system (PDS) have had deadly consequences, as reported in 2018, with seven out of 12 cases of starvation deaths linked to Aadhaar [2].
Policy measures needed include investing in women-centric digital literacy and infrastructure, gender-responsive algorithm design that audits bias, and inclusive labor reforms recognizing unpaid care work and informal sector realities [2][3]. Strengthening community-based digital support and ensuring alternate offline access to welfare services are crucial to bridging these gaps and enabling equitable benefits from digital governance for informal women workers.
The digital divide in India is high, with only 31% of the rural population using the internet compared to 67% of the urban population, according to a 2022 Oxfam report [4]. Digital mapping platforms like Google Maps and Wikipedia reflect geographic inequalities, with significant under-representation of the Global South [5].
In conclusion, the challenges posed by algorithmic bias and the digital divide demand intersectional, gender-sensitive, and context-aware policy interventions. When inequality is written in algorithms, it must be resisted powerfully, precisely, and provocatively in all forms. The modes of measuring inequality must be reconsidered, focusing not just on quantitative precision, but also on how adjectives are gendered and what comprises "training data".
References:
- The Hindu
- The Wire
- Oxfam
- Oxfam
- The Conversation
- The digital divide and concerns over algorithmic bias in finance, technology, education-and-self-development, general-news, and sports industries could be significantly impactful for informal women workers in India, who may struggle with biometric authentication and digital literacy.
- Policy measures aimed at addressing these issues should prioritize women-centric digital literacy, gender-responsive algorithm design, and inclusive labor reforms, as proposed by sources like The Hindu, The Wire, and Oxfam.
- Consideration should also be given to the underrepresentation of marginalized populations in digital platforms such as Google Maps and Wikipedia, as highlighted by The Conversation, to address the geographic inequalities that persist within these spaces.
- To achieve equitable benefits from data-driven governance for informal women workers, it is essential to strengthen community-based digital support and ensure alternate offline access to welfare services. This will help bridge the digital divide and counteract the effects of algorithmic bias that have resulted in deadly consequences, as evidenced by past incidents of starvation deaths linked to Aadhaar.
