Artificial intelligence (AI) has become a defining force, shaping how online spaces evolve and operate. From tailoring user experiences to moderating billions of online interactions, AI is now a key part of how we connect, communicate, and stay safe in digital spaces.
When it comes to online safety, trust remains a growing concern. Misinformation, manipulation, privacy lapses, and online abuse — the threats continue to grow. AI has emerged as a powerful tool to contain these threats and make the internet safer. However, the same systems can also amplify harm. A biased algorithm or poorly designed safeguard does not fail once, it fails everywhere, rippling across cultures, languages, and communities.
This raises a pressing question: Who is keeping AI safe?
AI safety means different things in different places
Privacy concerns dominate European thinking, while social harmony guides much of Asia; Western frameworks often prioritize individual rights over collective well-being. And the regulatory landscape mirrors these divides too: Europe’s GDPR, India’s IT Rules, China’s algorithm regulations, and the United States’ sectoral approach are not merely different, they are built on incompatible philosophies of what AI should protect, and why.
The stakes are too high for any single perspective to prevail across this cultural, legal, and regulatory diversity.
how culture rewrites the AI safety playbook
Cultural norms shape how societies perceive AI risks and define ethical priorities.
- Individualism vs. collectivism: In individualistic cultures, AI debates focus on autonomy, control, and privacy. Safety measures protect users from harm and ensure transparency. Collectivist societies flip the script — prioritizing social harmony and public good, sometimes accepting greater state oversight. Same technology. Completely different ethics.
- Perceptions of trust and bias: Different societies hold varying levels of trust in institutions and corporations. Cultures with higher institutional trust may demand less transparency in AI systems. Cultures with low trust demand stronger oversight and redress mechanisms. And then there is the problem of bias. Cultural bias in training data leads to discriminatory AI outcomes, solved only by diverse datasets and inclusive design teams, a challenge when most AI development happens in Western hubs.
- Attitudes toward automation: A culture's relationship with technology shapes its comfort with AI. Some embrace automation; others, especially post-colonial societies, fear dependence on foreign technology and cultural erosion. History matters. Context matters. One size does not fit all.
how legal and regulatory systems shape AI safety
National laws vary so widely that global companies face a patchwork of governance models:
- European Union (EU): prescriptive and risk-based: The EU's AI Act, which is the world's first comprehensive AI law, bans high-risk systems like government social scoring and certain biometric surveillance. Non-compliance brings fines up to €30 million or 6% of global turnover.
- United States (US): flexible and sector-specific: The U.S. relies on a patchwork of state and federal regulations, prioritizing flexibility to promote innovation. There's no single AI law. Agencies like the Federal Trade Commission adapt existing consumer protection laws. Federal frameworks like NIST's AI Risk Management remain voluntary. State-level laws from California and others add complexity.
- China: state-centric and controlled: Regulations align with national interests, emphasizing surveillance and social stability. AI outputs must reflect "socialist core values." Social scoring systems are banned in the EU but are required here.
- United Kingdom (UK): pro-innovation and principles-based: The UK relies on existing regulators centered on safety, fairness, accountability, transparency, and contestability.
This means that a system compliant in California may violate EU law. An approach accepted in the UK may not be accepted in China. For instance, facial recognition is legal in Singapore, restricted in the EU, and banned in several U.S. cities.
Adding to this complexity, international collaboration remains limited. Bodies like The Organization for Economic Co-operation and Development (OECD) and United Nations Educational, Scientific and Cultural Organization (UNESCO) promote human-centric AI principles, but without enforcement, implementation depends on national priorities — which diverge sharply.
A single AI safety model cannot navigate such a diverse and complex maze.
challenges that must be addressed
The global divergence in AI safety strategies presents several key challenges:
- Regulatory fragmentation: Different legal and regulatory approaches increase compliance costs and complexity for international AI developers and businesses.
- Ethical imperialism: Western dominance limits the global relevance and acceptance of AI systems.
- Safety and security disparities: Weakly regulated nations may become testing grounds for risky AI.
- Cultural misinterpretation:AI trained on one culture's data often fails elsewhere, eroding trust.
These challenges are not going away. As AI becomes more powerful and more embedded in daily life, the fragmentation will only deepen.
the path forward: adaptation, not harmonization
Perfect global consensus on AI safety is unrealistic. The cultural and philosophical differences run too deep. A pragmatic approach is adaptability:
- Develop AI that learns and adapts to local contexts
- Build transparency mechanisms that work across cultures
- Create governance frameworks flexible enough to accommodate different legal requirements
- Invest in diverse teams that bring multiple perspectives to safety challenges
- Establish international dialogue that respects sovereignty while addressing shared risks
True, the global AI safety landscape is complex, fragmented, and evolving rapidly, but it does not have to be navigated alone.
how can Infosys BPM help?
Infosys BPM provides comprehensive Trust and Safety (T&S) services to navigate the complex digital landscape and evolving global regulations. We leverage Gen AI-powered solutions and deep human expertise to proactively mitigate threats, fraud, and abuse. Further, The Infosys Responsible AI Toolkit, an open-source offering, provides a collection of technical guardrails that integrate security, privacy, fairness, and explainability into artificial intelligence (AI) workflows. Partner with us to ensure platform integrity and foster secure, trustworthy experiences that drive business resilience.
