AI’s Slow Adoption and Its Gradual Societal Impact

AI’s Slow Adoption and Its Gradual Societal Impact

Syllabus:

GS-2:

Government Policies & Interventions

GS-3:

Robotics , Artificial Intelligence , Scientific Innovations & Discoveries , IT & Computers

Focus:

Princeton researchers argue that AI will transform society over decades, not years. They emphasize that AI, like past general-purpose technologies, will require time to diffuse and will not immediately replace human labor, challenging the narrative of AI’s rapid revolution.

Understanding AI’s Impact :

• AI’s Disruptive Potential:

  • Since OpenAI launched its generative chatbot, ChatGPT, in November 2022, AI has been seen as a transformative force, predicted to radically change society, much like the Industrial Revolution.
  • However, researchers at Princeton University argue that AI’s transformative impact will take decades, not years, as often claimed by big tech labs and companies.

• AI as a General-Purpose Technology:

  • AI is compared to past general-purpose technologies like electricity, which did not make human labor redundant immediately but evolved gradually over decades.
  • These technologies integrated into various sectors over time, and AI will follow a similar path.

AI’s Slow Impact: A Normal Technological Progress

• AI’s Rapid Developments Are Exaggerated:

  • Despite the headlines about AI breakthroughs, its actual societal impact remains slow.
  • Arvind Narayanan of Princeton states that the stories about AI’s rapid adoption are overstated, and the reality is that both businesses and societies will adapt to AI gradually, like any other technology.
• The Technology Adoption Curve:
  • Similar to past innovations such as the internet, AI’s adoption process will unfold over decades, not months or years.
  • The learning curve for both AI developers and businesses integrating AI ensures that this transformation is gradual.
• Technological Evolution vs. Disruption:
  • Sayash Kapoor, another Princeton researcher, argues that AI is not inherently different from other past technologies in terms of development and societal impact.
  • The diffusion process, where AI is gradually integrated into society, will unfold slowly.

The Reality of AI’s Development and Deployment:

• AI’s Future Progression:
  • AI has reached a saturation point in terms of learning from available online data. Moving forward, it will need to interact with people and real-world environments to improve, which will take time.
  • The “innovation-diffusion feedback loop” explains this gradual progression. As AI capabilities improve, human adaptation and feedback will guide its further development.
• The Role of Human Control:
  • AI will not make human labor redundant. Instead, the adoption of AI will shift job roles rather than eliminate them entirely.
  • Much like past technologies, AI’s impact on employment will take decades. Many roles will still require human oversight, especially in complex real-world scenarios.
• AI Adoption in Business and Industry:
  • Businesses will adopt AI gradually, ensuring that systems are reliable and under human control. Technologies like self-driving cars demonstrate this slow adoption.
  • Companies that prioritize safety and human control (e.g., Waymo) have succeeded, while those rushing into deployment (e.g., Cruise) have failed.

AI’s Long-Term Economic Impact and Job Dynamics:

• AI’s Economic Role:
  • Historical examples from the adoption of technologies like electricity and the internet suggest that AI will gradually impact the economy over decades.
  • As AI automates tasks, those tasks will become cheaper, and human labor will shift to areas not affected by automation.
  • Human control will remain crucial for many jobs, especially those requiring oversight of AI systems.
• The Slow Process of Technological Diffusion:
  • The adoption of general-purpose technologies has always been slow, with businesses and governments integrating them over decades. The same will happen with AI.

  • Businesses will experiment with AI across different sectors, and its widespread adoption will be gradual.

Policy Recommendations and Addressing Risks of AI:

• Risks Associated with AI Deployment:
  • The risks posed by AI are more related to its deployment rather than its development As AI systems are integrated into industries, issues such as reliability, safety, and unintended consequences become more critical.
  • Policy interventions should focus on the deployment phase, ensuring AI systems are integrated with appropriate fail-safes and safeguards.
• AI’s Impact: Addressing the Real Risks:
  • Aligning AI with human values is important, but it is not enough. Policies must ensure the safe deployment of AI in critical sectors like healthcare, finance, and transportation, where the risks are highest.
  • Ensuring oversight and fail-safes for AI systems will help mitigate societal risks.
• Resilience Approach to AI Regulation:
  • Instead of suppressing AI’s spread, Narayanan advocates for a resilience-based approach. This involves building defenses and responses that can adapt as AI becomes more widespread.

  • The “immune system” approach allows society to adapt gradually and strengthens its ability to address the risks AI may pose.

Positive Social Impacts of AI:

• Revolutionizing Healthcare

  • Enhances diagnostic accuracy through image analysis (e.g., converting X-rays to real images).
  • Assists in disease prediction and personalized treatment.
• Advancing Agriculture
  • Enables precision farming via agronomic data and weather forecasting.
  • Improves productivity and sustainability in food production.
• Fostering Empathy through Simulation
  • AI projects like MIT’s Deep Empathy simulate war-torn conditions to increase global awareness and compassion.
  • Used by UNICEF to highlight disaster impacts in regions like Syria and Yemen.
• Restoring Lost Voices
  • Helps ALS patients regain speech through voice cloning technologies.
  • Promotes dignity and communication for people with speech impairments.
• Creative and Entertainment Applications
  • Deepfake tech used positively in dubbing foreign films and reviving historical figures.
  • Example: Samsung AI Lab animating the Mona Lisa using deep learning.

Negative Social Impacts of AI:

• Gender-based Harassment
• Over 90% of AI-generated deepfakes are pornographic, mostly targeting women (Deeptrace Report).
• Serious threat to women’s dignity and online safety.
• Fueling Extremism and Misinformation
• Terror groups use fake AI-generated content to incite violence and spread propaganda.
• Ex: Fake military abuse videos to stir anti-state sentiments.
• Job Displacement
• Replaces low-skilled jobs, especially in customer service (e.g., Zomato’s AI assistant Zia).
• Risk of increased unemployment without proper skill adaptation.
• Privacy Violations
• Massive data analysis by AI systems raises concerns over data misuse and surveillance.
• Potential threat to personal freedom and autonomy.
• Environmental Impact
• Training large AI models emits significant carbon.
• Ex: A single 213M parameter model can equal 125 flights (NY to Beijing) in carbon output.

AI Regulation: India and Global Landscape

India’s Approach

• Digital India Framework – Under development to regulate AI and protect digital citizens.
• National AI Programme – Promotes responsible and efficient AI use.
• Data Governance Policy – Ensures ethical handling of AI-related data.

• Draft Digital India Act – Will replace the IT Act, with specific AI regulatory provisions.

Global Efforts

• European Union – Draft AI Act to set binding AI use standards.
• USA – Blueprint for an AI Bill of Rights promotes ethical AI usage.
• Japan – Society 5.0 integrates AI to solve social issues.
• China – Enforces AI laws and ethical guidelines via its Next Generation AI Plan.

Way Forward:

• De-biasing AI Models
  • Ensure training data is inclusive and fair to avoid perpetuating discrimination.
• Promoting Transparency
  • Users must be informed about AI limitations and risks.
• Protecting Privacy
  • Implement and enforce strong data protection frameworks.
• Ethical Deployment
  • Push for international agreements like the Bletchley Declaration to promote safe AI practices.

Conclusion: A Gradual Transformation

• AI’s Long-Term Impact:
  • AI will transform industries and societies, but this will happen slowly over decades, not instantly.
  • The real-world challenges of integrating AI into existing systems, training the workforce, and ensuring reliability mean that its full impact will unfold gradually.
  • Recognizing AI’s slow adoption will help temper unrealistic expectations and avoid unnecessary hysteria about its potential.

Source: IE

Mains Practice Question:

Discuss the long-term societal impact of Artificial Intelligence, highlighting the gradual process of its adoption and the importance of human control. How can policymakers ensure AI’s benefits while mitigating associated risks?