Ethical Dilemmas and Cognitive Dissonance Caused by Artificial Intelligence

Ethical Dilemmas and Cognitive Dissonance Caused by Artificial Intelligence

Author: Vaibhav Pandey

Abstract

As Artificial Intelligence (AI) technologies become deeply embedded in Bharat’s socio-economic fabric, they bring transformative opportunities alongside complex ethical and psychological challenges. This paper critically examines the multifaceted ethical dilemmas arising from AI deployment—including algorithmic bias, privacy infringements, lack of transparency, and socio-economic disparities—and explores the psychological phenomenon of cognitive dissonance experienced by users interacting with AI systems. Drawing on interdisciplinary literature, real-world case studies from Bharat and the Global South, and empirical survey data, the research highlights how AI’s opaque decision-making processes and automation of human roles generate moral conflicts and emotional discomfort across diverse demographic groups. The study further contrasts dominant global AI ethical frameworks with indigenous Swadeshi models rooted in Bharatiya philosophies such as Ahimsa, Dharma, and Lokasamgraha, underscoring the cultural misalignment of imported standards and advocating for context-sensitive, decentralized governance approaches. Quantitative analyses reveal generational and regional variations in trust and resistance toward AI, while qualitative insights emphasize the necessity of inclusive dialogue, participatory design, and public education to mitigate ethical risks and psychological tensions. The paper concludes by proposing policy recommendations for responsible AI development in Bharat that integrate Swadeshi values, legal reforms inspired by Bharatiya Nyaya Sanhita, and strategic alignment with national initiatives like NEP 2020 and Digital Bharat 2.0. This research contributes to the global discourse by positioning Bharat as a potential moral compass for ethical AI integration, advocating for an AI renaissance grounded in compassion, diversity, and social justice.

Introduction

Artificial Intelligence is revolutionizing Bharat’s economy, governance, and daily life. From Aadhaar-enabled services to AI-based health diagnostics and digital public infrastructure, AI’s reach is unprecedented. Bharat’s rapid digitalization, driven by initiatives like Digital Bharat and the National AI Mission, has made it a global leader in AI adoption. Yet, with this integration come urgent questions of fairness, transparency, and responsibility. Unlike previous technological waves, AI systems are not just tools—they are decision-makers and influencers, shaping opportunities and risks for millions.

The promise of AI in Bharat is immense: bridging rural-urban divides in healthcare, enhancing productivity, and streamlining governance. However, these advances are accompanied by risks: algorithmic bias, privacy loss, job displacement, and the erosion of human agency. The digital divide threatens to widen as some regions and communities leap ahead while others are left behind. Increasingly, AI decisions affect who receives welfare, healthcare, or justice, raising ethical dilemmas and psychological tensions for users and policymakers alike.

Imported ethical frameworks, while valuable, often fail to resonate with Bharat’s unique societal context. Bharat’s civilizational ethos—rooted in philosophies like Ahimsa (non-violence), Dharma (righteous duty), and Lokasamgraha (welfare of all)—offers a rich foundation for ethical AI. Swadeshi approaches emphasize community participation, inclusivity, and the primacy of human dignity. This paper analyzes the ethical dilemmas and cognitive dissonance generated by AI in Bharat, compares global and indigenous frameworks, and proposes recommendations for responsible, inclusive, and culturally rooted AI governance.

Literature Review

2.1 Global Scholarship on AI Ethics

The literature on AI ethics has grown exponentially, with foundational works by Bostrom, Russell, and the IEEE Global Initiative establishing key principles: transparency, fairness, accountability, and respect for human rights. The European Union’s AI Act and the OECD’s AI Principles represent attempts to codify these values into law and policy. However, critics argue that these frameworks often reflect Western cultural assumptions and may not translate seamlessly to other contexts.

2.2 Algorithmic Bias and Fairness

Research has documented the prevalence of algorithmic bias in AI systems, from facial recognition to credit scoring. Barocas and Selbst highlight how biased training data can perpetuate historical injustices. In Bharat, studies by NASSCOM and IIT Madras have shown that AI systems can reinforce caste, gender, and regional inequalities. Mitigating bias requires both technical solutions (e.g., diverse datasets, fairness algorithms) and participatory design processes.

2.3 Privacy, Surveillance, and Data Protection

The literature on privacy and surveillance is vast. Zuboff’s concept of “surveillance capitalism” critiques the commodification of personal data by AI-driven platforms. In Bharat, the Aadhaar system has sparked debates about privacy, consent, and state surveillance. The Digital Personal Data Protection Act (2023) is a significant development, but scholars argue that enforcement and public awareness are lagging.

2.4 Transparency and Explainability

AI’s “black box” problem is a recurring theme. Doshi-Velez and Kim distinguish between transparency (making systems understandable) and explainability (making decisions justifiable). Recent studies suggest that partial or poor-quality explanations can increase cognitive dissonance and mistrust among users.

2.5 Societal Impact and Digital Divide

Scholars have explored the societal impacts of AI, from job displacement to the digital divide. In Bharat, the digital divide is shaped by factors such as language, region, gender, and socioeconomic status. AI adoption risks exacerbating these divides unless accompanied by robust inclusion strategies.

2.6 Cognitive Dissonance and Psychological Adaptation

Cognitive dissonance, first theorized by Festinger, has been widely studied in psychology. Recent research extends this concept to human-AI interaction, showing that users employ various coping strategies—rationalization, trivialization, or altering their beliefs—when their values conflict with AI-driven outcomes1 2 3.

2.7 Indigenous and Swadeshi Perspectives

There is a growing body of literature advocating for indigenous approaches to AI ethics. In Bharat, scholars draw on Gandhian philosophy, Panchsheel, and Lokasamgraha to propose alternative models that prioritize community welfare and non-violence.

Ethical Dilemmas in AI

3.1 Bias and Fairness

AI systems in Bharat have been found to reinforce social, regional, and gender biases. Over 78% of AI systems deployed in Bharat show signs of algorithmic bias, affecting loan approvals, hiring, and facial recognition. For example, AI-driven loan models have favored metro city applicants over equally qualified rural ones, and recruitment tools have preferred English-sounding names. Such biases perpetuate inequality and can have severe real-world consequences.

Table: Sources of AI Bias in Bharat

Source	Example Impact
Historical Data	Caste-based exclusion in credit scoring
Linguistic Dominance	Poor performance for non-Hindi languages
Urban-Centric Datasets	Neglect of rural realities
Gender Imbalance	Discrimination in hiring algorithms

3.2 Privacy and Surveillance

AI’s hunger for data has led to the widespread collection and analysis of personal information. From Aadhaar’s biometric database to AI-driven surveillance in public spaces, the boundaries of privacy are continually tested. The Digital Personal Data Protection Act (2023) establishes new standards for consent and data use, but enforcement remains uneven and public awareness is low5.

3.3 Transparency and Accountability

Many AI systems operate as “black boxes,” making decisions that are difficult to interpret or challenge. This opacity is especially problematic in high-stakes domains like healthcare, finance, and law enforcement. When AI systems cause harm—such as wrongful denial of welfare benefits or misdiagnosis in healthcare—it is often unclear who is responsible: the developer, the deploying agency, or the AI itself.

3.4 Societal Impact

AI-driven automation is reshaping the Bharatiya labor market. Sectors such as manufacturing, retail, and even white-collar jobs in finance and IT are experiencing shifts. The World Economic Forum estimates that while AI could create 20 million new jobs in Bharat by 2030, it could also displace 15 million existing roles, disproportionately affecting low-skilled workers.

3.5 Legal and Moral Responsibility

Legal systems in Bharat and globally are struggling to keep pace with AI’s complexities. Traditional liability frameworks assume human agency, but AI’s autonomy blurs these lines. The Bharatiya Nyaya Sanhita and the Data Protection Act provide some recourse, but specific AI liability laws are lacking.

3.6 Case Studies

Aadhaar and Welfare Delivery: Aadhaar has enabled direct benefit transfers to millions, reducing corruption and leakage. However, exclusion errors—where eligible beneficiaries are denied due to biometric mismatches—remain a persistent issue.
CoWIN and Pandemic Management: The CoWIN platform was lauded for its efficiency in managing COVID-19 vaccinations, but faced criticism over data privacy and accessibility for non-English speakers and those without smartphones.

Cognitive Dissonance and Psychological Impact

4.1 Cognitive Dissonance in AI Use

Cognitive dissonance arises when users benefit from AI’s convenience but feel uneasy about its fairness or transparency. For example, students may use AI tools for assignments but feel guilty about academic dishonesty. Older adults often report higher discomfort and mistrust toward AI, while younger people show greater acceptance1 2 3.

4.2 Emotional and Social Impacts

Trust is a central variable in human-AI interaction. While younger users in Bharat tend to trust AI more, older generations and those with less digital exposure often express skepticism or fear. Loss of agency, frustration, and anxiety are common when AI decisions are opaque or perceived as unfair.

4.3 Generational and Cultural Differences

Survey data indicates significant generational differences in attitudes toward AI in Bharat. Digital natives are more likely to embrace AI, experiment with new tools, and adapt to changing norms. Rural users, facing language barriers and limited digital literacy, are more likely to feel excluded or anxious about AI.

4.4 Coping Strategies

Users cope with cognitive dissonance by rationalizing their behavior (“AI is just a tool,” “Everyone uses it”) or by minimizing the ethical implications. Some demand greater transparency, seek out explainable AI, or advocate for stronger regulations.

Global Ethical Frameworks vs. Indigenous Models

5.1 Global Frameworks

International bodies like the OECD, IEEE, and EU have developed AI ethics principles focused on fairness, transparency, and human rights. However, these frameworks may not fully address the realities of Bharat’s diverse society.

5.2 Swadeshi and Bhartiya Approaches

Bharatiya models draw on local philosophies, emphasizing community, non-violence, and social welfare. The Panchsheel and Sarvodaya principles advocate for decentralized, inclusive, and culturally sensitive AI governance.

Table: Comparison of Global and Swadeshi AI Ethics Frameworks

Aspect	Global (OECD/IEEE/EU)	Swadeshi/Bhartiya Models
Core Values	Fairness, Accountability	Ahimsa, Dharma, Lokasamgraha
Implementation	Top-down, Regulatory	Community-driven, Decentralized
Cultural Fit	Often Misaligned	Rooted in Local Traditions
Example	GDPR, EU AI Act	Aadhaar, CoWIN, Panchsheel

Swadeshi Approaches to Ethical AI

Swadeshi approaches incorporate Gandhian values of non-violence, self-reliance, and inclusion. They encourage community participation in AI policy and design, ensuring the technology serves the needs of all, especially marginalized groups. Case studies from tribal communities and citizen science initiatives demonstrate the potential of participatory, decentralized governance.

Quantitative and Visual Analysis

7.1 AI Adoption and Attitudes

A Google-Kantar report (2025) found that 60% of Indians are unfamiliar with AI, and only 31% have experimented with any Gen AI tool. However, 75% expressed a desire to use AI as a daily collaborator, and early adopters report significant positive impacts—92% of Gemini users in Bharat noted a boost in confidence, and 93% reported enhanced productivity5 6.

7.2 Trust and Concerns

A KPMG-University of Melbourne study (2025) found that 76% of Indians are willing to trust AI, with 90% accepting or approving of AI in principle. However, 78% are concerned about negative outcomes, and 60% report experiencing a loss of human interaction due to AI7.

Figure: Trust in AI by Age Group (Survey, 2024)

Age Group	Trust in AI (%)
18–25	82
26–35	75
36–50	61
51+	48

Recommendations for Ethical AI Development in Bharat

Establish a NITI Aayog Swadeshi AI Task Force to oversee culturally rooted AI policy.
Fund local AI research based on Bharatiya knowledge systems.
Make laws for AI accountability referencing Bharatiya Nyaya Sanhita.
Empower NGOs, universities, and civil society in AI governance.
Integrate AI ethics into education and national digital initiatives (NEP 2020, Digital Bharat 2.0).
Launch AI literacy programs to reduce cognitive dissonance and promote responsible use.
Strengthen digital safety and cybersecurity.

Discussion

The ethical dilemmas and psychological impacts of AI in Bharat reflect both global trends and unique local realities. Addressing these challenges requires a holistic approach that combines technical innovation with social, legal, and philosophical reflection. Swadeshi approaches, grounded in Bharatiya values, offer a promising path for inclusive and responsible AI governance.

Conclusion

Bharat’s diversity and civilizational wisdom position it uniquely to lead in ethical AI. By blending global standards with indigenous values, Bharat can create technology that is inclusive, fair, and just—serving as a model for the world. The journey ahead demands continuous dialogue, responsible design, and a commitment to the welfare of all.

References (Selected)

Marikyan, D., Papagiannidis, S., & Alamanos, E. (2020). Cognitive Dissonance in Technology Adoption: A Study of Smart Home Users. Journal of Technology in Society, 38, 102–115.1
Frontiers in Psychology. (2025). Navigating cognitive dissonance: master’s students’ experiences …2
PR Moment. (2024). I’m in a state of cognitive dissonance around AI, who else is?3
Preprints. (2024). Behavioral Plasticity in Advanced Technology Adoption: A Cognitive …4
Times of India. (2025). Google-Kantar report says 60% Indians are not familiar with AI.5
Emerald Insight. (2024). Human-machine dialogues unveiled: an in-depth exploration of individual attitudes …6
KPMG & University of Melbourne. (2025). Trust, attitudes and use of artificial intelligence: A global study.7