HBR: How Companies Can Take a Global Approach to AI Ethics

Artificial intelligence ethics in global business contexts require a nuanced approach that balances universal principles with local cultural norms and values. As companies increasingly deploy AI solutions worldwide, they must navigate complex ethical landscapes that vary significantly across regions and cultures.

The challenge of global AI ethics: Companies developing AI ethics programs often overlook the crucial fact that ethical considerations can differ substantially across cultural contexts, leading to potential conflicts and misunderstandings.

• Current global AI ethics standards are predominantly based on Western perspectives, which may not fully address or resonate with ethical concerns in other parts of the world.
• This Western-centric approach can result in the implementation of AI systems that inadvertently conflict with local values or fail to address region-specific ethical issues.
• As AI technologies become more prevalent globally, there is an increasing need for a more inclusive and culturally sensitive approach to AI ethics.

A contextual global AI ethics model: To address these challenges, companies need to develop a more flexible and collaborative approach to AI ethics that incorporates diverse perspectives and local expertise.

• This model prioritizes collaboration with local teams and devolves decision-making authority to ensure that AI ethics frameworks are relevant and effective in different cultural contexts.
• By involving regional teams in the development and implementation of AI ethics guidelines, companies can create more robust and adaptable frameworks that respect local norms while adhering to core ethical principles.

Implementing a global AI ethics strategy: A three-step process is recommended for companies looking to develop a contextual global AI ethics model:

• First, establish a set of core ethical principles that can be applied across all geographies, serving as a foundation for more specific regional guidelines.
• Second, set up relevant teams across different regions, empowering local experts to contribute their knowledge and insights to the development of AI ethics frameworks.
• Third, engage regional AI ethics teams in ongoing conversations and incorporate their feedback to refine and improve global ethics strategies continually.

Balancing global principles and local contexts: Continuous interaction between global and local teams is crucial for maintaining a balance between overarching ethical principles and specific cultural considerations.

• This iterative process allows companies to adapt their AI ethics frameworks as they gain new insights and encounter unique challenges in different markets.
• Regular communication between global and local teams helps ensure that ethical guidelines remain relevant and effective across diverse cultural landscapes.

Technological solutions for ethical implementation: Emerging technology products are helping companies translate abstract AI ethics concepts into configurable parameters that can be adjusted for different contexts.

• These tools enable organizations to implement ethical guidelines more consistently across their AI systems while allowing for necessary regional variations.
• By leveraging such technologies, companies can more effectively operationalize their AI ethics frameworks and ensure compliance across diverse global operations.

Case study: Hewlett Packard Enterprise (HPE): HPE serves as an example of a company taking a global, contextual approach to AI ethics:

• The company has invested significant time and resources into developing a nuanced, adaptable AI ethics framework that goes beyond a simple compliance-based or checkbox approach.
• HPE’s strategy demonstrates the importance of considering cultural differences and local perspectives when implementing AI ethics programs on a global scale.

The ongoing nature of ethical AI development: Contextualizing AI ethics should be viewed as an ongoing process rather than a one-time effort, requiring continuous refinement and adaptation.

• As AI technologies evolve and new ethical challenges emerge, companies must be prepared to revisit and update their ethics frameworks regularly.
• This dynamic approach allows organizations to stay ahead of potential ethical issues and maintain trust with stakeholders across different cultural contexts.

Broader implications for global business: The need for a contextual approach to AI ethics highlights the broader challenges of operating in a globalized business environment.

• Companies must increasingly balance universal standards with local expectations and norms across various aspects of their operations, not just in AI ethics.
• This trend underscores the importance of cultural intelligence and adaptability in successful global business strategies, particularly as technology continues to reshape international commerce and communication.