From Algorithms to Accountability: Rethinking AI’s Role for 2047

From Algorithms to Accountability: Rethinking AI’s Role for 2047

 

 

 

India, being the fastest growing economy with having the world’s second largest population, has a significant stake in the AI revolution. Recognising AI’s potential to transform economies and the need for India to strategise  its approach, Hon’ble Finance Minister, in his budget speech for 2018 – 2019, mandated NITI Aayog to establish the National Program on AI, with a view to guiding the research and development in new and emerging technologies. In pursuance of the above, NITI Aayog has adopted a three-pronged approach – undertaking exploratory proof-of-concept AI projects in various areas, crafting a national strategy for building a vibrant AI ecosystem in India and collaborating with various experts and stakeholders. This paper delves into AI’s far-reaching impact, offering a balanced exploration of global trends and India’s unique challenges and strengths. It highlights essential “do’s and don’ts” for ethical AI deployment, the critical role of regulatory frameworks, and the leadership strategies needed to guide AI toward equitable growth. By addressing eight key gaps in AI implementation, this work envisions a roadmap for sustainable and inclusive AI development while offering a futuristic perspective on what lies ahead. As we unlock AI’s transformative potential, this document serves as a guide to navigating its complexities responsibly and innovatively.

Approach to Leadership in AI

India stands at a pivotal moment to shape its unique AI leadership, leveraging the transformative power of technology while its global adoption remains in its early stages. The proposed #AIforAll initiative embodies inclusive leadership, aligning AI innovation with India’s unique aspirations and needs. The strategy aims to harness AI for economic progress, social development, and inclusive growth, positioning India as a hub for innovative technology and developing economies. While AI offers immense potential across sectors, its adoption so far has been largely driven for commercially perspective.

Technology disruptions like AI are once in a generation phenomenon, and hence scale adoption strategies, especially nationally strategies. Governing and the regulating bodies need to strike a balance between narrow definitions of financial impact and the greater good. In this paper we Focus on five sector to benefit the most from AI in solving societal needs.

1. Healthcare: increased access and affordability of quality healthcare.
2. Agriculture: Enhanced farmers income, increased farm productivity and reduction of wastage.
3. Education: Improved Access and quality.
4. Smart city & Infrastructure: efficient and connectivity
5. Smart Mobility & Transportation: Smarter and safer modes of transportation.

The road to implement AI in above mentioned focus sectors isn’t easy, it involves lots of different challenges and barriers for society. There are some important barriers that need to be addressed.

1. Lack of broad-based expertise in Research and Application Of AI
2. Absence of enabling data ecosystems – access to intelligent data.
3. High resources cost and low awareness for adoption of AI
4. Privacy and Security
5. Absence of collaborative approach

India wants to achieve the goals of #AIforAll which needs superior research capabilities in emerging technologies. Unlock the  growth potential demands expertise in both core and applied research. While recent progress shows promise, AI research in India remains in its early stages and needs significant collaborative efforts on a larger scale.

As AI continues to reshape industries and redefine job requirements, skilling and reskilling the workforce has become essential. This includes upgrading current skills and preparing future talent to match the evolving job market. Decentralized teaching models, in collaboration with private companies and educational institutions, can offer meaningful certifications. Additionally, promoting new job opportunities, such as data annotation, can help reabsorb workers displaced by automation.

To maximize AI’s impact, its adoption across startups, private companies, public sector units (PSUs), and government bodies is crucial. This can create a self-sustaining cycle of supply and demand. A marketplace model can address challenges in AI development by enabling discovery of the best approaches and pricing. A centralized platform, like a National AI Marketplace (NAIM), could focus on data collection, annotation, and deployable AI models.

The government’s role is key to speeding up AI adoption. By fostering partnerships, offering infrastructure, encouraging research, and creating demand through problem-solving for public needs, AI can become a cornerstone of innovation and growth.

Data is the backbone of AI solutions, so managing it responsibly while ensuring privacy and security is crucial. Key challenges include using data without consent, risks of identifying individuals through datasets, biases in data selection leading to unfair AI outcomes, and unequal data aggregation. To address these, the paper recommends establishing strong data protection policies, industry-specific regulations, and adopting global standards.

For India to harness AI innovation effectively, a solid intellectual property (IP) framework is vital. While the government has made strides in improving the IP regime, challenges persist—especially in applying rigid patent laws to the unique nature of AI solutions. For example, the role of data in shaping useful AI models highlights these complexities. To resolve such issues, the paper suggests creating IP facilitation centers to connect AI developers with experts, and ensuring that IP authorities, judges, and tribunals receive adequate training to handle AI-specific cases.

Future look of AI – prediction view

In 2025 and beyond, what can we expect from  AI, Gen AI Looking ahead to 2025 and beyond, the Technology sector is poised for major growth, fueled by rapid adoption of generative AI. However, several challenges must be addressed to fully realize this potential:

1. Striking a balance between investing in generative AI infrastructure and generating profits.
2. Bridging gender gaps in generative AI usage.
3. Reducing the energy demands of AI-driven data centers.
4. Building trust to counter concerns around deepfakes.
5. Maximizing the role of generative AI in media, gaming, and real-time applications.
6. Navigating gaps in cloud and streaming video spending.

Defining Eight Gaps for AI implementation in Tech

1. Generative AI Investment vs. Returns: Companies are pouring billions into AI infrastructure like chips and data centers, but the revenue from these efforts lags far behind. While businesses fear underinvesting more than overspending, the gap between costs and returns keeps growing.
2. Energy and Sustainability Challenges: AI data centers demand massive amounts of power—ideally from clean energy sources. Unfortunately, power grids and sustainability efforts aren’t keeping up, leaving a significant gap that won’t likely close by 2025.
3. Generative AI Gender Gap: Women are using AI tools less than men, partly due to trust issues. However, this disparity is expected to narrow in some regions within the year.
4. Deepfake Trust Issues: With fake AI-generated content becoming more convincing and cheaper to produce, society is struggling to trust what it sees and hears. Solutions like better detection tools and clear labelling of AI content are critical to restoring trust.
5. AI in Content Creation: While studios are exploring generative AI for making content faster and cheaper, concerns about intellectual property and reliability are slowing widespread adoption.
6. Autonomous AI Agents: AI bots capable of independently completing tasks and managing workflows are being piloted in 2024. The big question is whether they’ll go mainstream by 2025 or in future.
7. Shrinking Streaming Subscriptions: Consumers aren’t holding onto multiple streaming services as expected. Instead, they’re bundling a few favourites and dropping others, forcing companies to rethink their strategies.
8. Cloud Cost Management: Cloud computing promised cost savings, but poor spending oversight has made it expensive for many. Businesses are now adopting tools like FinOps to regain control and cut costs.

 

 

 

Now lets us take a look on impact AI globally and in India to understand the importance of regulating bodies for AI implementation and to control its effect and any type of consequences. The analysis highlights in the context of the evolution and influence of Artificial Intelligence (AI) globally and specifically in India across various sectors, combining historical milestones with present applications. It also addresses policy development, societal changes, and its effects on health, professional life, and living standards.

Global Impact

AI has transformed the global landscape across all domains, becoming a cornerstone of technological advancement. Historically, the roots of AI trace back to the 1950s, with the development of neural networks and symbolic reasoning. The 1997 victory of IBM’s Deep Blue over chess champion Garry Kasparov marked a major milestone, while Google DeepMind’s AlphaGo defeat of the world’s top Go player in 2016 showcased AI’s exponential growth. Today, AI contributes over $15.7 trillion to the global economy annually, with applications ranging from automation and robotics to language processing and predictive analytics.

Policy Development and Societal Impact: Governments and global organizations have embraced AI, formulating policies to maximize its benefits while mitigating risks. In 2021, UNESCO adopted the “AI Ethics Recommendation,” advocating for human-centric AI principles. Similarly, countries like the US and China lead AI policy development, focusing on economic growth and global competitiveness. However, AI has also raised societal concerns, including data privacy, algorithmic bias, and job displacement. While automation has streamlined industries, reducing costs and increasing efficiency, it has also disrupted labor markets, with predictions of 85 million job transitions by 2025. The social debate balances AI’s potential for inclusive growth with its risks to equity and fairness.

Living Standards and Health Impacts: AI has significantly improved living standards, driving innovations in smart homes, virtual assistants, and intelligent transportation systems. Healthcare has benefited immensely from AI-driven technologies, such as diagnostic tools like IBM Watson Health and drug discovery platforms. During the COVID-19 pandemic, AI expedited vaccine development, optimized healthcare delivery, and monitored outbreaks in real-time. However, over-reliance on AI in health poses ethical dilemmas, such as accountability in decision-making and equitable access to technology.

Professional and Individual Impacts: In professional domains, AI has revolutionized workflows by automating repetitive tasks, enabling remote work through advanced communication tools, and enhancing decision-making through data analytics. For individuals, AI has personalized experiences in education, entertainment, and commerce, offering adaptive learning systems, curated content, and predictive shopping recommendations. Yet, challenges remain, including digital fatigue, cybersecurity threats, and the potential for AI misuse in spreading misinformation.

Impact on India

India’s AI journey mirrors its ambition to position itself as a global technology leader. While early AI adoption in the 1980s was limited, the past decade has witnessed a surge in AI integration across sectors. India’s AI market is projected to grow to $7.8 billion by 2025, contributing significantly to its $1 trillion digital economy target. Applications span agriculture, healthcare, education, and governance, with initiatives like AI-enabled crop monitoring, telemedicine platforms, and e-governance systems revolutionizing service delivery.

Policy Development and Societal Impact: The Indian government has been proactive in fostering AI development, launching the National AI Strategy in 2018 and creating the Centre for Artificial Intelligence and Robotics (CAIR). Organizations like NITI Aayog have driven initiatives to integrate AI in agriculture, health, and urban management. However, societal challenges persist, including workforce displacement in traditional industries and the digital divide in AI access. While AI-powered governance enhances efficiency and transparency, ensuring inclusivity in technology adoption remains critical.

Living Standards and Health Impacts: AI has notably elevated living standards in India, especially in urban areas. Smart city projects use AI for traffic management, waste disposal, and energy conservation. In healthcare, AI-powered solutions address the country’s challenges of high population density and inadequate medical infrastructure. AI-based diagnostics and telemedicine platforms like Aarogya Setu played pivotal roles during the COVID-19 pandemic. However, concerns about data privacy and ethical decision-making in healthcare underscore the need for robust governance.

Professional and Individual Impacts: AI has redefined professional landscapes in India, particularly in IT, finance, and manufacturing. Automation and robotics have optimized industrial processes, while AI-driven analytics have enhanced decision-making in corporate environments. For individuals, AI has brought transformative changes in education through platforms like Byju’s, which use adaptive learning technologies, and in entertainment via personalized content from streaming services. Yet, challenges such as unemployment due to automation and the risk of digital exclusion among rural populations persist.

AI in India represents a blend of opportunities and challenges. Its potential to drive economic growth, improve governance, and enhance quality of life is immense, but addressing ethical concerns, ensuring inclusivity, and preparing the workforce for AI-driven futures are critical. With a strong policy framework and collaborative efforts, India can harness AI to achieve its vision of equitable and sustainable development.

 

 

Artificial Intelligence (AI) has become a transformative force globally and in India, reshaping industries, economies, and societies. While AI promises immense benefits across sectors like healthcare, education, agriculture, and governance, it also presents significant risks and challenges. The following analysis explores both the Do’s and Don’ts of AI implementation, blending real-life examples and past developments with their implications on individuals, health, and professional life in both global and Indian contexts.

Do’s in AI Implementation

1. Ethical AI Development and Transparent Use: Globally, a core best practice is ensuring AI is developed ethically and transparently. Companies like IBM and Microsoft have established AI ethics boards to guide product development according to human rights and privacy standards. For instance, IBM Watson Health has played a crucial role in diagnostics, improving accuracy in cancer treatment plans, but also faced challenges when its algorithms produced inconsistent results due to poor data quality. Transparency in how AI systems operate fosters trust and allows continuous improvement. India has followed suit with initiatives like the “AI for All” campaign, ensuring that AI benefits reach diverse sectors such as agriculture, healthcare, and governance.
2. AI for Healthcare and Rural Access: In both global and Indian contexts, AI’s integration into healthcare has been revolutionary. AI-powered systems like DeepMind’s AI for eye disease detection and platforms like 1mg and DocOnline in India have enhanced healthcare access, especially in rural areas where doctors are scarce. These systems have enabled faster, more accurate diagnoses, and remote consultations, improving health outcomes. For instance, in India, AI-powered diagnostics help detect early-stage diseases like tuberculosis or diabetes in underserved populations. While AI offers potential for life-saving advancements, it raises ethical concerns about data privacy and over-reliance on technology for critical health decisions.
3. AI in Agriculture and Governance: Globally, AI has revolutionized agriculture, with platforms like TartanSense offering predictive analytics and automated solutions for crop monitoring and yield prediction. In India, AI in agriculture addresses the challenges of climate change, low productivity, and labour shortages. For example, the government’s push for AI-driven solutions in farming helps increase efficiency and ensure sustainable agricultural practices. AI in governance, such as the Smart Cities Mission, has improved urban planning and public services, streamlining processes and enhancing citizens’ lives. However, ensuring equitable access to these technologies across rural and urban divides remains a key challenge.
4. Regulation and AI Governance: Effective AI governance is vital, both globally and in India. The European Union’s AI Act and India’s National AI Strategy exemplify the need for robust policies to regulate AI applications, particularly in sectors like healthcare, law enforcement, and finance. These regulations aim to safeguard citizens from potential misuse while fostering innovation. India’s adoption of AI in various government services like MyGov has streamlined citizen engagement, but it also highlights the need for ethical frameworks that avoid biases in decision-making processes.

Don’ts in AI Implementation

1. Ignoring AI Bias and Discrimination: One of the most significant risks in AI development globally is ignoring bias. The COMPAS algorithm in the U.S. and recruitment tools used by Amazon have been criticized for reinforcing racial biases, leading to discriminatory outcomes. Similarly, in India, AI-powered recruitment platforms and law enforcement tools must be developed with care to prevent reinforcing societal inequalities. Inaccurate or biased algorithms could perpetuate discrimination against marginalized groups, which is a global issue that India must address, especially in a diverse society with various caste and gender concerns.
2. Over-relying on AI for Critical Decision-Making: Another global don’t is over-relying on AI for decisions that have significant human consequences, such as hiring, legal judgments, or medical treatment. Amazon’s scrapping of its AI recruitment tool due to gender bias is an example of how AI should never replace human judgment entirely, especially in sensitive areas. In India, AI’s role in decision-making, especially in hiring or judicial processes, must be scrutinized to avoid wrongful discrimination. While AI can be a powerful assistant, it should complement human expertise rather than replacing it altogether.
3. Data Privacy Neglect: Both globally and in India, the protection of data privacy is critical when implementing AI systems. The Facebook-Cambridge Analytica scandal is a prime example of the dangers posed by inadequate data protection. In India, while the Personal Data Protection Bill is under review, the use of AI to handle personal information—especially for targeted advertising or surveillance—requires strong privacy safeguards. Without these protections, individuals’ personal data can be exploited or misused, violating fundamental rights. Therefore, AI systems must adhere to rigorous data security measures to maintain trust and integrity.
4. Misuse of AI in Surveillance and Control: Globally, one of the significant concerns is the use of AI for mass surveillance. In China, AI-driven facial recognition technology has been used for extensive government surveillance, raising concerns about individual freedoms and privacy. In India, while AI can aid in public safety, its use must be carefully regulated to avoid mass surveillance or social control. The danger of AI being used to track citizens’ every move or influence public behaviour in ways that compromise privacy or civil liberties is a real concern that requires vigilant oversight.

 

Importance of Regulating Body for AI and LLMs

India currently doesn’t have a specific law or regulation for artificial intelligence (AI). However, there are several advisories, guidelines, and IT rules in place that provide a framework to oversee the development of AI technologies, including Generative AI and large language models (LLMs).

This document explores the ethical issues involved in using AI in India, divided into two main areas: system-focused and society-focused concerns. System-focused concerns deal with how decisions are made, ensuring fairness for all affected, and holding the right people accountable. On the other hand, society-focused concerns look at how automation affects jobs and employment opportunities.

In 2018, NITI Aayog introduced India’s first national AI strategy, called #AIForAll, aiming to make artificial intelligence accessible and beneficial for everyone. This strategy focused on using AI to address key areas like healthcare, education, agriculture, smart cities, and transportation—areas crucial for national development.

Since its launch, several steps have been taken to bring the strategy to life. For example, efforts have been made to create high-quality datasets to support AI research and innovation. Additionally, frameworks for data protection and cybersecurity have been developed to ensure safe and responsible use of AI technologies.