Policy: The Least Spoken About, But Most Important Topic in Artificial Intelligence

Rajive Keshup
Cathay Innovation
Published in
4 min readMay 19, 2023


Written by Rajive Keshup, edited by Jaclyn Hartnett, Cathay Innovation

Artificial intelligence (AI) is transforming the way we live and work. From predictive maintenance in manufacturing to personalized healthcare, AI is making our lives easier, more efficient and more productive.

As this technology continues to weave itself into the fabric of our lives, the absence of comprehensive regulation is akin to sailing into a storm without a compass. Without it, we stand on the precipice of a potential abyss, teetering dangerously close to unchecked intelligence beyond our control — a dystopian world where…

  • Ceaseless data mining makes privacy a relic of the past,
  • Machines make decisions that favor efficiency over empathy,
  • Socio-economic inequities are exacerbated with power granted to those who control AI, while the uninitiated are left to face the brunt of automation and job loss.

While this may sound dramatic, the urgency for robust AI regulations is very real — not only to ensure ethical deployment and prevent malicious use, but to safeguard the very essence of what it means to be human in this rapidly evolving digital landscape.

Here, we’ll highlight key AI policy and regulation areas — what could be one of the least talked about, but most important topics in bringing AI to the real world.

Policy Infrastructure for AI

AI technologies present unique policy challenges, as they are often complex, opaque, and difficult to regulate. To address these challenges, policy infrastructure for AI must be designed to promote innovation while ensuring the responsible development and deployment of AI technologies. This includes:

Data Governance: AI technologies require large amounts of high-quality data to learn, improve, and make accurate predictions. Therefore, it’s essential to establish data governance policies that ensure the privacy, security and ethical use of data.

Accountability and Transparency: To promote trust and confidence in AI technologies, we must establish accountability and transparency policies that ensure that AI systems are explainable, auditable and accountable for their actions.

Ethical and Legal Frameworks: AI technologies raise ethical and legal issues, including bias, discrimination, privacy, and accountability. Thus, ethical and legal frameworks are needed to address these issues and ensure that AI technologies are developed, deployed, and used in a responsible and ethical manner.

Intellectual Property: AI technologies are often developed through collaborative efforts involving multiple stakeholders. It’s important to establish intellectual property policies that promote innovation while ensuring that the rights and interests of all stakeholders are protected.

International Standards: As AI technologies become more global, there is a growing need for international standards that promote interoperability, transparency, and accountability across different jurisdictions.

Policy Management in AI

Effective policy management is critical to ensuring that policies and regulations are implemented and enforced effectively. This requires collaboration between government, industry, academia and civil society to develop and implement policies and regulations that promote the responsible development and deployment of AI technologies. Policy management includes:

Stakeholder Engagement: To ensure that policies and regulations are effective, it’s essential to engage with stakeholders, including industry, academia, civil society, and policymakers. This engagement can help to identify policy gaps, anticipate policy challenges and ensure policies and regulations are aligned with the needs and interests of stakeholders.

Policy Coordination: AI technologies raise complex and multifaceted policy issues. We need policy coordination mechanisms that can ensure that policies and regulations are integrated, consistent and coherent across different sectors and jurisdictions.

Regulatory Sandboxes: Regulatory sandboxes are controlled environments that allow stakeholders to test and experiment with AI technologies in a safe and supervised manner. Regulatory sandboxes can help to identify policy challenges, develop best practices, and ensure that policies and regulations are effective and proportionate.

Capacity Building: Effective policy management requires a diverse set of skills, including policy analysis, risk assessment, and stakeholder engagement. It will be key to invest in capacity-building programs that can develop these skills and ensure that policymakers and regulators have the knowledge and expertise to manage AI policies and regulations effectively.

In Summary

At Cathay Innovation, we can’t stress enough how critical it is to establish a comprehensive policy infrastructure and management system to ensure AI’s responsible and ethical use. The infrastructure required must include rigorous data governance, unswerving commitment to accountability and transparency, robust ethical and legal frameworks, firm intellectual property rights and harmonized international standards.

Effectual policy management is not an option but a necessity, requiring an earnest engagement from all stakeholders, policy coordination, the strategic use of regulatory sandboxes for secure experimentation and focused efforts on capacity building. Armed with these components, we stand on the threshold of creating an AI ecosystem that strikes a delicate balance of innovation, responsibility and tangible benefits to society. Let us rise to this challenge and shape an AI-infused future that aligns with our collective values and aspirations.

Building in the space? Feel free to reach out!

For more insights on the world of AI, innovation and startups, check out our previous posts:

Industry Insights: How Will AI Impact the Modern-Day Education System?

Reality vs. Hype: How AI & Human Activity Combine



Rajive Keshup
Cathay Innovation

A long term optimist, burdened with short term scepticism. Successfully Exited Founder/Operator turned Venture Capitalist.