Redefining the Social Contract in the Age of AI: Between Symbiosis and Subjugation

 Redefining the Social Contract in the Age of AI: Between Symbiosis and Subjugation

Rahul Ramya

30.01.2025

Patna, India

This is the age of automated and artificial intelligence technology, where artificially intelligent machines are considered more faithful companions to humans than humans are to one another. People now place greater trust in such machines than in their fellow beings—or even in their own efficiency and intelligence. This growing disbelief in human intellect, capabilities, and the boundless aspirations of human potential is a fundamentally flawed philosophy—one with the dangerous potential to corrode, erode, and corrupt the very essence of humanity from the flesh and bones of human existence.

The recent demand articulated by AI technocrats like Sam Altman for a new social contract in the era of AI represents a serious effort not only to redefine the relationship between humans and machines but also to reshape the very essence of what it means to be human and what purpose humans ultimately serve. However, human interaction with machines is not solely determined by technological visionaries; rather, policies play a far more pivotal and fundamental role than technology itself.

Our social contract with so-called “social” AI, as envisioned by many technocrats, is ultimately a product of our choices regarding technology. Therefore, rather than merely crafting a new social contract, we must critically redefine our relationship with artificial machines and algorithms—because the nature of this relationship is a direct consequence of policy decisions that we are conditioned to accept through prevailing

The discourse surrounding AI technocrats reflects how AI systems are increasingly being positioned not just as tools but as social actors that can provide emotional support and companionship—a shift that demands a careful examination of its psychological and social implications.

Altman and other tech leaders’ calls for a “new social contract” require particularly incisive debates. It highlights how the AI narrative is often shaped by a small group of influential technologists whose visions may prioritize technological advancement over broader human and societal considerations. The veiled suggestion that this demand represents an attempt to “reshape the very essence of what it means to be human” points to the profound philosophical and ethical stakes involved.

The emphasis on policy over technology as the primary driver of human-AI interaction is crucial in this discourse. While technological capabilities set certain parameters, it is ultimately human decisions—expressed through policies, regulations, and social norms—that determine how these technologies are integrated into society. This suggests that democratic engagement with AI development is not just possible but necessary.

However, the notion that we are “conditioned to accept” certain relationships with AI through existing power structures raises important questions about agency and consent in technological adoption. It suggests that we must examine not just the technologies themselves but also the economic and social systems that shape their development and deployment.

The challenge isn’t simply about managing new technologies but about preserving human agency and authenticity in an increasingly AI-mediated world. How do you envision policies that could help safeguard human autonomy while still enabling beneficial technological advancement?

A deeper awareness is needed to read between the lines and recognize how subtle efforts are made to shift the discourse from the personal dimension (AI as “faithful companions”) to the systemic construction of favorable power structures and policy frameworks. This awareness has the potential to illuminate for common people how their individual experiences with AI technology are connected to broader societal transformations.

The realization of the motives behind technocratic governance becomes even more pointed, especially in Altman’s call for a new social contract. His proposal, while framed as a neutral advancement of technology, is in fact a profound attempt to restructure human society and identity. In this context, the phrase “reshape the very essence of what it means to be human” takes on particular weight.

The recognition of policy as the primary driver of human-AI interaction—rather than technological capability alone—serves as a crucial counterpoint to technological determinism. This suggests that the current trajectory of AI development is not inevitable but rather a product of specific policy choices and power dynamics.

The notion of being “conditioned to accept” certain relationships with AI implies not just passive compliance but an active shaping of human behavior through institutional and social structures that define the human-machine relationship. This raises fundamental questions about autonomy and consent in technological adoption.

The relationship between AI and humans, shaped by policy choices, can be either symbiotic or, conversely, antagonistic. While technology coexists with human society, this coexistence can be either complementary or extractive. If policy regards technology as a companion to humans, the relationship between the two becomes symbiotic, promoting mutual growth. Technological advancements enhance and expand human capabilities, and in turn, humans with enhanced capabilities are more likely to develop technology productively. Thus, both feed into and strengthen each other.

However, the reverse is also possible if, through policy choices, technology is designed to replace and subvert humans. In such cases, humans suffer first, becoming less capable of utilizing and enriching technology. Meanwhile, a small group of privileged individuals becomes more capable and increasingly prone to colonizing technology itself. This “colonized” technology is then wielded by this small elite of technologically advanced humans to dominate the rest of humanity and its agency.

That is why, more than redefining the social contract between AI and humans, the urgent need lies in refining the relationship between policy and humans on one hand, and policy and technology on the other.

The key question is : How can society maintain meaningful human agency when the very frameworks for understanding and implementing AI are increasingly shaped by those who stand to benefit most from its uncritical adoption?

Policy Suggestions for a More Symbiotic Relationship Between Humans and AI

To ensure that artificial intelligence and emerging technologies serve as complementary rather than extractive forces, policy interventions must focus on reinforcing human agency, equitable access, and democratic oversight. The following policy recommendations aim to create a symbiotic relationship between humans and AI, preventing technocratic dominance while maximizing social benefits.

1. Human-Centric AI Design and Governance

   •   Mandate Human Oversight in Critical AI Systems: AI should not operate autonomously in decision-making processes that significantly impact human lives, such as in healthcare, criminal justice, and hiring. Policies must require explainability and human intervention mechanisms in such applications.

   •   Legal Recognition of AI’s Role as Assistive, Not Autonomous: Policy frameworks should emphasize AI as a tool to augment human intelligence rather than replace human judgment. Strict regulatory boundaries should prevent AI from making decisions without human accountability.

   •   Publicly Funded Ethical AI Research: To reduce corporate dominance over AI development, governments must fund independent AI research that prioritizes social well-being over commercial profit.

2. Preventing Technocratic Governance and AI Colonization

   •   Regulation Against Algorithmic Power Concentration: No single corporation or technocratic elite should control AI’s governance. Governments should implement strong antitrust laws to prevent monopolization of AI technologies and ensure distributed AI ownership.

   •   Transparency in AI Policy-Making: Policies around AI deployment should be democratically debated rather than dictated by corporate interests. Public participation in AI policy formation through citizen advisory councils and parliamentary debates should be mandated.

   •   Ethical Review Committees for AI Implementation: Every AI policy should undergo scrutiny by interdisciplinary panels that include ethicists, sociologists, and legal experts, not just technocrats.

3. Strengthening Human Capabilities in the AI Era

   •   Universal AI Literacy and Public Awareness Campaigns: To prevent mass disenfranchisement, policies must ensure that AI education is integrated into school curricula, vocational training, and lifelong learning programs. AI literacy should not remain an elite privilege but a universal right.

   •   Reskilling Programs for Job Transition: Governments should provide free or subsidized AI-related training for workers at risk of job displacement, ensuring that technological advancements complement rather than replace human labor.

   •   Digital Commons for AI Innovation: AI advancements should not be owned solely by corporations. Governments should create open-source AI platforms where researchers, students, and small enterprises can innovate without being dependent on tech giants.

4. Ethical AI Deployment in Public Services

   •   AI for Social Welfare, Not Surveillance: AI should be used to enhance public healthcare, education, and environmental conservation, not just for state surveillance and corporate profit. Strict limits should be placed on facial recognition and predictive policing technologies.

   •   Community-Owned AI Initiatives: Policies should incentivize local AI development where communities have control over AI applications that address their unique needs (e.g., AI-driven agricultural support, regional healthcare diagnostics).

   •   Algorithmic Fairness and Bias Audits: AI systems must undergo regular audits for bias and discrimination, ensuring that they do not reinforce social inequalities based on caste, gender, or economic status.

5. Redefining the Social Contract Between Policy and Technology

   •   Democratizing AI Policy Through Global Cooperation: AI governance should not be dictated by a few countries or corporations. An international AI regulatory framework (similar to climate agreements) should be developed to ensure equitable technological progress worldwide.

   •   Tax on AI-Driven Corporate Profits for Social Redistribution: Companies benefiting disproportionately from AI automation should contribute to AI taxation funds, which can be used to finance universal basic income or public welfare programs.

   •   Right to Refuse AI Intervention: Citizens should have the legal right to refuse AI-based decision-making in critical areas like credit scoring, hiring, and law enforcement, ensuring that human discretion remains paramount.

Rather than merely adapting to AI, policies must actively shape AI’s trajectory to reinforce human autonomy, equity, and social welfare. By ensuring that AI is an extension of human intelligence rather than a replacement, governments can prevent the colonization of technology by a select few and instead foster a more democratic, ethical, and symbiotic AI ecosystem.

There are several real-world examples, both historical and contemporary, where automation and AI have been implemented symbiotically—enhancing human capabilities rather than replacing or subverting them. These examples demonstrate how technology, when guided by ethical policies and human-centric governance, can complement human skills, promote equity, and improve societal well-being.

1. Japan’s Human-Centered Robotics in Elderly Care

Example: Japan’s use of robotic assistants in elderly care homes (e.g., Paro, Pepper, and Robear) demonstrates how AI-driven automation can support human caregivers rather than replace them. These robots assist with tasks such as lifting patients, monitoring health conditions, and providing companionship, reducing the physical and emotional burden on human caregivers.

Symbiotic Approach: Instead of replacing nurses or family caregivers, AI-powered robots supplement human efforts, allowing workers to focus on personalized emotional care and more complex medical tasks.

2. Germany’s Industry 4.0: Collaborative Human-AI Workplaces

Example: Germany’s Industry 4.0 initiative emphasizes collaborative robotics (cobots) in manufacturing. Companies like Bosch and Siemens use AI-powered robotic systems that work alongside human employees, assisting them rather than replacing them. These cobots are designed to enhance precision, reduce fatigue, and improve efficiency, particularly in repetitive or hazardous tasks.

Symbiotic Approach: Germany’s policies prioritize worker retraining, job transformation, and human-machine collaboration, ensuring that automation augments labor productivity rather than causing mass layoffs.

3. Kerala’s AI-Powered Public Healthcare

Example: Kerala (India) has integrated AI-driven diagnostics in public health services to detect diseases like tuberculosis and cervical cancer in rural areas. AI tools like Qure.ai and Niramai help frontline health workers diagnose patients with greater accuracy, ensuring early intervention.

Symbiotic Approach: Instead of replacing doctors, AI enables health workers with limited expertise to make better diagnoses, reducing the burden on urban hospitals and improving healthcare accessibility for marginalized populations.

4. OpenAI’s Partnership with Researchers for AI Democratization

Example: OpenAI, despite controversies, has initiated partnerships with universities, independent researchers, and small businesses to share AI advancements. Instead of keeping AI models entirely proprietary, they have open-sourced models like GPT-2 and provided APIs for diverse applications, including education and public sector use.

Symbiotic Approach: AI is not monopolized by a few corporations but is made accessible to a broader community, fostering innovation in education, climate science, and small business applications.

5. Canada’s AI Ethics & Policy Framework

Example: Canada has been at the forefront of ethical AI governance, implementing policies to ensure AI development prioritizes human well-being. The Montreal Declaration for Responsible AI (2018) and Canada’s Directive on Automated Decision-Making (2019) regulate AI in public services, ensuring transparency and human oversight in AI-based government decisions.

Symbiotic Approach: Canada’s model ensures that AI assists human decision-making rather than replacing governance functions, preventing bureaucratic automation from eroding public accountability.

6. Brazil’s AI in Agriculture: Supporting Small Farmers

Example: The Brazilian Agricultural Research Corporation (Embrapa) has introduced AI-driven soil analysis, crop monitoring, and smart irrigation to support small-scale farmers. These AI systems provide real-time insights on climate, soil conditions, and pest outbreaks, enabling farmers to optimize yields with fewer resources.

Symbiotic Approach: AI empowers small farmers rather than displacing them, making agriculture more sustainable, inclusive, and climate-resilient.

7. Finland’s Free AI Education Initiative

Example: Finland launched the “Elements of AI” program, a free online course designed to educate citizens about AI, making AI literacy accessible to everyone—not just tech experts. This initiative has trained over 1% of Finland’s population in AI fundamentals.

Symbiotic Approach: Instead of AI being controlled by a small elite, Finland’s policy ensures that AI knowledge is democratized, enabling the general public to participate in and influence AI developments.

Key Takeaways for Symbiotic AI Policies

From these cases, we can identify common principles that make AI and automation symbiotic rather than extractive:

 1. Enhancing human capabilities rather than replacing them (Japan’s eldercare robots, Germany’s cobots).

 2. Democratizing AI access and education (Finland’s free AI training, OpenAI’s open-source initiatives).

 3. Using AI for public welfare and equity (Kerala’s healthcare AI, Brazil’s agricultural AI).

 4. Ensuring policy-driven AI governance (Canada’s AI ethics regulations).

By following these principles, governments and institutions can harness AI to complement human intelligence and foster inclusive growth, preventing a future where AI deepens inequalities and technocratic control.

Ethical Theories and the Moral Imperative for AI Governance

The governance of AI is not merely a technical or economic challenge but a profound moral responsibility. Ethical theories provide crucial insights into how AI should be integrated into society while preserving human dignity, autonomy, and justice. A deontological approach, rooted in Kantian ethics, insists that AI must be developed with respect for human beings as ends in themselves rather than as means to economic or political goals. This contrasts sharply with the current trajectory of AI development, where technological efficiency often takes precedence over fundamental human rights. Policies that mandate explainability, accountability, and human oversight in AI decision-making align with this perspective, ensuring that AI does not erode moral responsibility.

From a utilitarian standpoint, AI’s value should be measured by its ability to maximize overall well-being while minimizing harm. AI-driven healthcare diagnostics, for example, can vastly improve medical outcomes and reduce disparities in access to healthcare. However, unregulated AI deployment—such as biased hiring algorithms or automated surveillance—can disproportionately harm marginalized communities. A balanced utilitarian policy would thus promote AI’s beneficial uses while implementing strict bias audits, transparency mandates, and equitable access mechanisms to prevent unintended consequences.

A virtue ethics approach, drawing from Aristotle’s notion of eudaimonia (human flourishing), emphasizes that AI should be designed to enhance human virtues, creativity, and social engagement rather than merely optimizing economic output. AI-driven education and skill-development programs, such as Finland’s nationwide AI literacy initiative, reflect this principle by ensuring that people are empowered participants in technological progress rather than passive subjects of automation. Policies that emphasize AI’s role in fostering critical thinking, ethical reasoning, and interpersonal connections will be crucial in preventing a future where human development is reduced to algorithmic efficiency.

John Rawls’ theory of justice provides another critical perspective, particularly in addressing the fairness of AI’s economic and social impacts. Rawls’ principle of fairness and the difference principle suggest that AI policies should be structured to benefit the least advantaged members of society while ensuring that no group unfairly dominates technological benefits. For example, while AI can increase economic productivity, Rawls would argue that the profits generated from AI-driven automation should not merely concentrate in corporate hands but be redistributed to uplift those most vulnerable to job displacement. Policies such as universal AI literacy, job reskilling programs, and AI taxation for public welfare align with Rawlsian principles by ensuring that AI-driven economic gains contribute to reducing inequalities rather than exacerbating them.

Amartya Sen’s Capability Approach and AI’s Role in Expanding Human Freedom

Amartya Sen’s capability approach provides a compelling framework to evaluate AI’s impact on human development. Sen argues that economic growth and technological progress are valuable only if they expand people’s real freedoms and capabilities—their ability to lead meaningful, autonomous lives. AI policies must therefore be assessed not just by their efficiency but by their role in enhancing or restricting human capabilities.

For instance, AI-driven public healthcare initiatives, such as Kerala’s use of AI diagnostics for tuberculosis detection, exemplify how AI can expand human capabilities by improving access to life-saving medical care. Similarly, AI-powered translation tools that bridge linguistic gaps enable people to participate more fully in global conversations. However, if AI remains concentrated in the hands of a technological elite, it risks exacerbating existing inequalities rather than alleviating them. A small group of corporate entities dictating AI’s development and deployment would create a digital caste system, where only those with privileged access to technology can fully benefit from its advancements.

A policy agenda informed by Sen’s framework would emphasize equitable access to AI-driven opportunities, ensuring that marginalized communities are not excluded from AI’s benefits. This could be achieved through publicly funded AI research, digital commons for innovation, and AI taxation policies that redistribute the wealth generated by automation. If AI is to serve as a force for human development rather than elite consolidation, governments must actively shape AI ecosystems to prioritize social justice, participatory governance, and inclusive growth.

 Conclusion: AI, Ethics, and the Future of Human Autonomy

Rather than viewing AI as an autonomous force that shapes society, my argument is  that AI’s trajectory is ultimately determined by human choices and policy frameworks. The ethical challenge of AI is not just about regulation but about ensuring that AI remains a tool for human empowerment rather than an instrument of control. Ethical theories—from Kantian deontology to Aristotelian virtue ethics, Rawlsian justice, and Sen’s capability approach—remind us that AI must be designed and governed in ways that uphold human dignity, foster creativity, and promote collective well-being.

Rawls’ justice as fairness principle demands that AI benefits should be equitably distributed, ensuring that those most vulnerable to AI-driven disruptions are not left behind. Meanwhile, Sen’s capability approach reinforces that AI must serve as an enabler of human freedom, not a force of subjugation. These perspectives collectively emphasize that AI governance must be rooted in justice, equity, and democratic oversight, preventing the monopolization of AI’s power by a privileged few.

In the end, the central question is not whether AI will replace human agency, but whether societies will assert their moral and political will to ensure that AI serves humanity rather than subjugates it. AI’s future must be a deliberate, ethically guided choice—not an inevitability dictated by technocratic elites.