Digital disparity in India's digital sphere and the subsequent inclination towards welfare for the digitally-inclined population
In India, the Aadhaar system, a crucial foundation for delivering various government welfare schemes, has become a barrier for many, particularly women in the informal sector. This is part of a broader trend in India where social welfare, work, and access are increasingly mediated by digital systems, with assumptions that digital systems will reduce inefficiencies and corruption.
However, the potential human costs of future welfare schemes in countries like India that heavily rely on digital IDs, biometric authentication, and artificial intelligence (AI) are significant. These risks include exclusion, errors, bias, and loss of trust, especially given the existing digital divide and algorithmic biases.
Exclusion and Access Barriers
A significant portion of India’s population, particularly among rural, indigenous, socially disadvantaged groups, women, and minorities, face challenges such as intermittent internet connectivity, lack of digital literacy, and unreliable biometric authentication. These factors can result in failure to access entitlements despite meeting scheme criteria.
Biometric Failures and Denial of Benefits
Failures in biometric authentication, such as fingerprint recognition errors or data mismatches in Aadhaar, have led to delays or denial of crucial benefits like wages under MGNREGA or public distribution system (PDS) entitlements. In some cases, these failures have had fatal consequences, as seen in reported starvation deaths linked directly to Aadhaar failures.
Algorithmic Bias and Discrimination
AI systems and automated decision-making, if trained on skewed or incomplete datasets, can perpetuate or amplify existing social biases. This can result in erroneous exclusion or unequal treatment of marginalized populations who might be underrepresented or misrepresented in the data used for welfare eligibility verification.
Opacity and Lack of Accountability
Digitization and centralization of welfare processes may add layers of opacity. Errors or delays in digital processes might not be easily visible or contestable by the affected individuals, cutting them off from timely redress or correction mechanisms.
Increased Vulnerability to Cyberattacks
The growing reliance on AI and digital identities increases exposure to cyber risks. A ransomware attack or AI system failure can disrupt the entire infrastructure underlying welfare delivery, delaying benefits to millions. Furthermore, insufficient preparedness in cyber resilience can exacerbate human costs when systems fail.
Overemphasis on Quantitative Metrics
Traditional welfare metrics often ignore qualitative and recognition-based inequalities caused by digital exclusion. Errors in digital entitlement systems do not always appear in economic inequality statistics but materially affect people’s lives.
While India has made remarkable progress in digital inclusion, these achievements coexist with significant human costs when digital systems fail or exclude. Robust human oversight, grievance redressal mechanisms, inclusive design, and measures to bridge the digital divide are critical to mitigate these human costs.
Key human costs to anticipate:
| Type of Risk | Human Cost / Impact | |---------------------------------|------------------------------------------------------------| | Digital exclusion | Loss of access to welfare due to internet/digital illiteracy| | Biometric authentication failure| Denial/delay of essential benefits, with possible fatalities| | Algorithmic bias | Discrimination and systemic exclusion of marginalized groups| | Lack of transparency | Inability to contest errors leading to prolonged hardship | | Cyber vulnerabilities | Disruption of benefits delivery on large scale |
These factors illustrate the urgent need for ethical, inclusive, and resilient design of digitally enabled welfare programs in India. Efforts must be made to interview those who are purposely left out of digitised platforms, denied benefits, misclassified or are invisible in the system. When inequality is written in algorithms, it must be resisted powerfully, precisely, and provocatively in all forms. The move towards direct benefit transfer is making things worse for those who are not recognised in the system. Rural workers, including indigenous people, socially backwards, women, and minorities, are expected to have uninterrupted internet, consistent biometric access, familiarity with apps, and the ability to contest errors logged far away.
- The digital divide in India is a concern, particularly for rural, indigenous, socially disadvantaged groups, women, and minorities, who face challenges like intermittent internet connectivity and lack of digital literacy.
- In India, biometric failures such as fingerprint recognition errors or data mismatches in Aadhaar can lead to delays or denial of crucial benefits like wages under MGNREGA or public distribution system (PDS) entitlements.
- AI systems, if trained on skewed or incomplete datasets, can perpetuate or amplify existing social biases, resulting in erroneous exclusion or unequal treatment of marginalized populations.
- Opacity in digitized welfare processes may prevent affected individuals from effectively contesting errors or delays, cutting them off from timely redress or correction mechanisms.
- The growing reliance on AI and digital identities increases exposure to cyber risks, with ransomware attacks or AI system failures having the potential to disrupt the entire infrastructure underlying welfare delivery.
- Overemphasis on quantitative metrics in traditional welfare systems can result in the ignoring of qualitative and recognition-based inequalities caused by digital exclusion.
- Robust human oversight, grievance redressal mechanisms, inclusive design, and measures to bridge the digital divide are critical to mitigate human costs associated with digital welfare systems.
- In India, cyber attacks can potentially create significant disruptions in the delivery of essential goods and services, especially in the context of digitally-mediated welfare programs.
- The reliance on digital IDs and biometric authentication for accessing work, social welfare, and services in India can lead to exclusion, errors, bias, and loss of trust, particularly for marginalized populations.
- In the realm of education and self-development, the increasing use of online platforms may exacerbate the digital divide, making it difficult for disadvantaged groups to access learning opportunities.
- In the field of career development, digital tools like job search engines and AI-powered resume screening may unintentionally favor those with technological skills and resources, perpetuating existing inequalities.
- In the context of finance, the move towards digital transactions and online banking can create barriers for those without access to smartphones or internet, leading to exclusion from financial services.
- The growing trend of sports betting online can lead to increased vulnerability for those struggling with gambling addiction, as lack of physical oversight may make it difficult to control gambling habits.
