| Journal of Collective Sciences and Sustainability
Received: 13 November 2025; Revised: 25 December 2025; Accepted: 25 December 2025; Published Online: 27 December 2025.
J. Collect. Sci. Sustain., 2025, 1(3), 25411 | Volume 1 Issue 3 (December 2025) | DOI: https://doi.org/10.64189/css.25411
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
Responsible AI in the Age of Generative Media: A
Comparative Study of Ethical and Transparent AI
Frameworks in India and the USA
Bhavya S. Tripathi,
1,*
Shrawani C. Gaonkar,
1
and Anagha Dhavalikar
2
1
Department of Artificial Intelligence and Data Science, Vasantdada Patil Pratishthan’s College of Engineering & Visual Arts, Mumbai,
Maharashtra, 400022, India
2
Department of Electronics and Computer Science, Vasantdada Patil Pratishthan’s College of Engineering & Visual Arts, Mumbai,
Maharashtra, 400022, India
*Email : bhavyatripathi02@gmail.com (Bhavya. S. Tripathi)
Abstract
Advances in generative artificial intelligence, such as Google Gemini, ChatGPT and DALL·E, are opening new
possibilities for creativity on digital media but also raising pressing concerns about misinformation, deepfakes
and declining trust in media outlets. This paper explores Responsible Artificial Intelligence (RAI) efforts at the
policy level in India and the United States, comparing their unique and shared approaches to addressing
generative media - where policies are driven toward balancing innovation with transparency, accountability and
fairness. India's official AI for All strategy speaks to inclusivity and social development, though concrete
enforcement mechanisms and safeguards against generative media misuse are currently lacking. The US relies
on the National Institute of Standards and Technology (NIST) AI Risk Management Framework, emphasizing
risk assessment, technical robustness and accountability but lacks a robust regulatory mechanism that aligns
its varying state and sector-specific initiatives. Alongside policy analysis, we designed and ran a pilot survey of
college students and working professionals through September 2025 to capture awareness, trust and concerns
related to Responsible AI. At a preliminary level, our findings showed low relative exposure to national policy
frameworks on RAI but high expressed concern related to potential misuse of generative AI around deepfakes,
manipulated images and the lack of checks on content authenticity. Respondents expressed particularly high
endorsement for content labels that would mandate labeling artificial intelligence generated media. We
conclude that India and the USA display parallel and diverging paths on RAI for generative media but both
experiences are marked by a gap between policy aspirations and public understanding. Moving ahead, we see a
need for greater policy clarity, cross-border coordination and public outreach to foster transparency and
responsibility in adoption of AI for media
Keywords: Generative artificial intelligence; Responsible AI; Ethical AI adoption; AI transparency and accountability;
Policy frameworks: India and USA; Deepfakes and media trust.
1. Introduction
2. Literature review
Responsible Artificial Intelligence (AI) rests on fairness, accountability, transparency, and explainability,
shaping how emerging technologies are guided ethically.
[5]
These principles aim to protect individual rights,
reduce discriminatory outcomes, and maintain societal trust in automated systems. Global initiatives, including
the Organisation for Economic Co-operation and Development (OECD) AI Principles (2019), UNES
Act (2024), promote a human-centric approach to AI governance that prioritizes fundamental rights and social
well-being. However, the rapid growth of generative AI, which can produce realistic synthetic text, images, and
speech, poses new ethical challenges by obscuring content authenticity and source credibility. To address these
risks, technical mechanisms such as the Coalition for Content Provenance and Authenticity (C2PA) framework
have been proposed to embed verifiable provenance metadata into AI-generated content.
[8,9]
2.1 Global ethical frameworks
More than 40 published AI ethics guidelines emphasize FATE principles, namely fairness, accountability,
transparency, and explainability, alongside human rights protection.
[1,3]
Among these, five frameworks are
(2021), and the NIST AI Risk Management Framework (AI RMF) (2023), as summarized comparatively in Table
1. While all five frameworks align broadly with FATE principles, they differ in enforcement strength and
operational focus. Some emphasize legally binding compliance mechanisms, whereas others function primarily
as voluntary guidelines or technical standards. Recent governance-focused surveys note that, despite broad
ethical consensus, gaps remain in translating high-level principles into enforceable and measurable practices,
particularly for fast-evolving generative AI systems.
[1,10]
Table 1: Comparative overview of major global responsible AI frameworks.
Framework
Year
Primary Focus
Key Limitation
IEEE Ethically Aligned Design
[1]
2021
Engineering ethics
Complex implementation
NIST AI RMF 1.0
[7]
2023
Technical accountability
Fragmented U.S. adoption
EU AI Act
[10]
2024
Legal compliance tiers
High burden for SMEs
OECD AI Principles
[11]
2019
Voluntary global guidelines
Non-binding; lack enforcement
UNESCO Ethics
Recommendation
[12]
2021
Socio-cultural governance
Limited auditability
These frameworks collectively establish ethical baselines for AI governance, yet their scope, legal authority, and
implementation mechanisms vary considerably across regions.
2.2 National frameworks: India vs. United States
AI for All strategy (2018), together with the NITI Frontier Tech Hub initiative (2024), emphasizes
inclusivity, skill development, and socially beneficial AI deployment.
[6,13]
However, these initiatives function
RMF 1.0 (2023) focuses on structured risk identification, documentation, and accountability across the AI
lifecycle.
[7]
Although comprehensive in technical guidance, its adoption remains voluntary and fragmented
across federal agencies and private sectors. Comparative policy studies indicate that while both countries share
core ethical values, their governance models differ substantially in enforcement mechanisms and institutional
coordination.
[1,3]
Recent cross-national analyses further highlight that generative AI governance remains
uneven, with limited standardized mechanisms for content verification and accountability across
jurisdictions.
[4,10]
2.3 Research gap
Despite extensive policy and ethical guideline development, three key gaps persist in the existing literature.
First, an empirical gap remains, as many studies focus predominantly on conceptual or policy-level analysis
without measuring public awareness or perceptions of Responsible AI in practice.
[1,4]
Second, although
generative media risks such as deepfakes and manipulated content are widely acknowledged, research has
largely emphasized content creation rather than user-level validation, detection, and trust mechanisms.
[8,14]
Third, cross-cultural and cross-national comparisons remain limited, particularly studies that connect national
AI governance frameworks with public understanding and trust outcomes.
[15]
Existing studies focus primarily
on policy design rather than public awareness and trust in generative media, leaving a disconnect between
regulatory intent and user experience. This research addresses these gaps by combining policy analysis with
survey-based empirical data to examine awareness, trust, and ethical expectations surrounding generative AI.
3. Methodology
This study uses both quantitative and qualitative methods, connecting survey responses with official policy
documents. This mixed-methods approach enables comparison across governance systems while exploring
individual perceptions of transparency and ethics.
3.1 Policy comparison
the NITI Frontier Tech Hub initiative (2024), and
the U.S. NIST AI RMF 1.0 (2023).
[6,7,13]
Policy documents were qualitatively coded using a deductive framework
based on five Responsible AI dimensions: fairness, accountability, transparency, inclusivity, and enforceability.
The comparative assessment of these policies across these dimensions is presented in Table 2. Each policy was
independently assessed against these dimensions and compared for scope, legal authority, and governance
mechanisms.
Table 2: Comparative overview of selected responsible AI policies.
Policy
Year
Country
Fairness
Accountability
Transparency
Inclusivity
Enforceability
Remarks
AI for All
2018
India
✓
✓
✓
Partial
Advisory
Focuses on
access and
skill
development;
non-binding
NITI
Frontier
Tech Hub
2024
India
✓
✓
✓
✓
Advisory
Emphasizes
responsible
deployment
and
collaboration;
non-binding
NIST AI Risk
Management
Framework
1.0
2023
USA
✓
✓
✓
Partial
Voluntary
Provides
structured
risk
management
guidance;
adoption
fragmented
across
agencies
3.2 Survey design
A structured online survey using Google Forms was conducted in September 2025, involving 40 voluntary
respondents. The survey contained 12 items divided into four thematic sections:
1. Awareness & Exposure familiarity with AI tools and RAI concepts;
2. Reliability and clarity - perceptions of trustworthiness and comprehensibility;
3. Ethical issues - concerns about bias, data misuse, and synthetic media;
4. Policy Focus - opinions on labeling, monitoring, and oversight policies.
Students and early-career professionals were selected due to their high exposure to generative AI tools and their
emerging role as primary adopters. Convenience sampling was employed due to the exploratory nature of the
study. Sample questions included:
•
• 5 scale)
Participation was anonymous, restricted to adults (18+), conducted in accordance with institutional ethical
standards.
3.3 Sampling and demographics
Respondents represented both students (85%) and professionals/developers (15%), all based in India. The
demographic profile, AI tool usage patterns, and survey reliability are summarized in Table 3. Given the
exploratory sample size (n = 40), the findings are descriptive and intended to identify patterns rather than support
inferential generalizations.
3.4 Analytical procedure
Descriptive statistics and cross-tabulation analyses were conducted using Google Forms outputs and manual
aggregation. Relationships between awareness levels, trust perceptions, and policy preferences were examined
through percentage comparisons and thematic interpretation.
Table 3: Demographic Profile of Respondents (n = 40).
Category
Sub-group
%
Country
India
100
Primary Role
Students
85
Professionals/Developers
15
AI Tool Usage
Daily
75
Few times/week
22.5
Rarely
2.5
Survey Reliability
= 0.84
High consistency
Expanded descriptive cross-tabulation analyses were included to illustrate patterns such as:
• Awareness vs. Trust
• Awareness vs. Support for Labeling
Due to the exploratory sample size (n = 40), inferential statistical tests, such as correlation coefficients or p-
values, were not applied. This approach allows identification of trends and patterns across respondents while
linking findings to the policy frameworks reviewed.
.
4. Results and analysis
Findings from the public survey (n = 40) are organized around four themes: awareness, trust, policy orientation,
and cross-national perception.
4.1 Awareness of responsible AI and generative tools
The results, as shown in Fig. 1, indicate a high level of engagement with AI tools, with 75% of respondents
reporting daily usage, 22.5% using AI tools a few times per week, and 2.5% reporting infrequent use. Despite
widespread usage, awareness of Responsible AI concepts remained limited. Only 30% of respondents reported
a clear understanding of the term, while 32.5% indicated partial familiarity. Notably, as shown in Fig. 2, 37.5%
encountered the concept of Responsible AI for the first time through this survey. This gap between usage
intensity and conceptual understanding highlights the need for improved awareness initiatives alongside
expanding AI adoption.
Fig. 1: (n = 40).
4.2 Trust in AI-generated media and transparency expectations
Trust in AI-generated content was moderate to low among respondents. Only 25% reported high levels of trust,
while a substantial majority expressed uncertainty or skepticism, primarily due to concerns regarding
misinformation and synthetic media manipulation. As shown in Fig. 3, transparency emerged as a critical factor
systems. Respondents who reported familiarity with verification mechanisms, such as digital provenance tags
or content markers, demonstrated comparatively higher trust in AI-generated outputs than those without such
awareness.
Fig. 2: Prior awareness of the concept of Responsible AI among respondents (n = 40).
Fig. 3: Respondent perceptions of transparency importance in AI-generated content (n = 40).
4.3 Policy support and content labeling
Strong support was observed for regulatory interventions addressing AI-generated content. A majority of
respondents (65%) favored mandatory labeling of AI-generated material, as seen in Fig. 4, while 27.5%
supported labeling in critical or high-risk contexts. Only a small minority (2.5%) opposed labeling altogether.
Similarly, 62.5% of respondents expressed support for official oversight mechanisms, indicating broad public
approval for governance measures emphasizing transparency and accountability, as shown in Fig. 5.
Cross-tab Observations:
• Awareness vs. Trust: Respondents familiar with Responsible AI concepts exhibited higher levels of trust in
AI-generated content compared to respondents without prior awareness.
• Awareness vs. Support for Labeling: Respondents with prior knowledge of Responsible AI principles were
more likely to support mandatory labeling of AI-generated content than those encountering the concept for
the first time.
4.4 Perceptions of IndiaU.S. ethical alignment
As shown in Fig. 6, with respect to cross-national governance, 45% of respondents favored a shared ethical
framework between India and the United States, while 35% preferred initially independent national
approaches. The remaining respondents expressed no definitive preference. Qualitative responses suggested
that U.S. frameworks were perceived as structured and enforcement-oriented, whereas Indian approaches were
viewed as inclusive and aspirational. These perceptions indicate complementary strengths across the two
governance models rather than direct opposition.
5. Discussion
Results are assessed in relation to the goals of the research - by contrasting country-level systems, measuring
public understanding and confidence, also sketching approaches for ethical AI use.
5.1 Governance comparison
India's AI for All along with NITI Tech Hub focus on access and basic skills yet enforcement stays limited.
[6,12]
The
U.S. NIST AI RMF 1.0provides clearer records along with better responsibility tracking but faces scattered
supervision.
[7]
That aligns with Kumari, whose work highlights ongoing variation worldwide when it comes to
actual enforcement.
[15]
Fig. 4: -generated content (n = 40).
Fig. 5: Public attitudes toward regulatory oversight of AI systems (n = 40).
Fig. 6:
= 40).
5.2 Knowing and believing
Even though 75% used AI every day, just 30% grasped RAI ideas - similar to findings by Jobin et al.,
[1]
showing
poor public awareness of ethics. Confidence in generated content stayed weak at 25%, which aligns with
transparency theory.
[14]
Understanding tools like C2PA or digital marks tied to stronger trust; recognition led to
greater belief in AI results.
[9]
5.3 Moving toward clear adoption
Respondents supported content labels (65%) along with government supervision (62.5%), showing clear
support for responsibility measures. A mixed strategy - using tech protections together with public awareness
efforts - aligns with international standards such as human oversight systems, origin tracking data, and
openness checks.
[4,8,9]
5.4 New ethical issues
Generative AI boosts creative potential; however, it also increases dangers like fake media and data abuse - key
issues highlighted in the study and often discussed in AI & Society.
[8]
For responsible use moving forward, rules
alone aren't enough - stronger public understanding of tech, moral awareness, and systems that track digital
origins must grow at the same pace.
6. Policy implications and strategic recommendations
6.1 Establishing practical and enforceable guidelines and regulations
Since 65% of respondents supported mandatory labeling of AI-generated content, policymakers in India and
the United States should prioritize the adoption of standardized provenance and content authentication
mechanisms, such as the Coalition for Content Provenance and Authenticity (C2PA) framework.
[8,9]
The strong
public preference for labeling indicates a clear demand for verifiable indicators that distinguish synthetic
content from human-generated media, particularly in high-risk information environments.
6.2 Raising public knowledge while improving skills
Although most respondents reported frequent use of generative AI tools, only 30% demonstrated clear
understanding of Responsible AI concepts, highlighting a significant awareness gap. To address this, both
countries should strengthen AI literacy initiatives by integrating Responsible AI, ethics, and media verification
concepts into school and university curricula.
[6]
These efforts should be supported through partnerships
between academic institutions, industry stakeholders, and public outreach programs to ensure broader societal
understanding of ethical AI use.
6.3 Joint India-U.S. governance efforts
Survey findings show that 45% of respondents favored shared ethical oversight between India and the United
States, indicating public openness to cross-national cooperation. Building on this support, a bilateral
Responsible AI working group could facilitate coordination on transparency standards, content verification
framework.
[12]
while connecting the operational guidance of the NIST AI Risk Management Framework with
6.4 Economic or social aspects
While the adoption of transparency and verification mechanisms may introduce short-term compliance and
implementation costs for digital platforms, such measures have the potential to generate long-term societal
benefits. Survey responses indicate strong public support for transparency-oriented governance, suggesting
that verified content labeling and provenance systems can enhance user trust and confidence in digital
information environments. Over time, increased transparency may contribute to more reliable information
sharing, greater user engagement, and sustained public trust in AI-enabled media systems.
7.
8. Conclusion
Funding Declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Data Availability Statement
Artificial Intelligence (AI) Use Disclosure
The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement,
grammar improvement, and manuscript structuring purposes during the preparation of this work. All technical
content, experimental implementation, results, and interpretations were independently developed and verified
by the authors.
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