Throughout my 25 years in IT and dispute resolution, I've witnessed how artificial intelligence has transformed the fabric of our society. What fascinates me most is not just the technological advancement, but the intricate web of relationships it has created between businesses, governments, and individuals. As someone who regularly mediates technology disputes, I've seen firsthand how AI has introduced both groundbreaking opportunities and deeply concerning challenges.

The Governance Challenge

Just last month, I arbitrated a case where a manufacturing company's AI-powered quality control system made decisions that resulted in significant losses for their client. The challenge? Neither party's contract had adequately addressed AI decision-making accountability. This isn't an isolated incident – I'm increasingly seeing similar cases where traditional legal frameworks simply can't keep up with AI's complexity.

Recently and due to a huge demand from the business to utilize AI capability, we found in the market a lot of ICT services providers saying they are AI enablers and sign contracts to deliver values to the business; unfortunately, it doesn’t deliver any value due to short of skills or misunderstanding to the business cycle and what it need to better results. Here a lot of clashes and disputes are appearing and have come to legal action.

What keeps me up at night is the dark side of AI capabilities. In a recent dispute, we dealt with a sophisticated deepfake video that nearly destroyed a business partnership built over decades. These aren't hypothetical scenarios anymore – they're real challenges landing on my desk with increasing frequency.

Global Best Practices Emerging

I've been particularly impressed with how different regions are tackling these challenges. The EU's AI Act, while not perfect, has taken a bold step forward. Having worked with European clients implementing these regulations, I can tell you it's not just about compliance – it's about fundamentally rethinking how we approach AI risk.

Singapore's approach resonates with me on a practical level. Their Model AI Governance Framework mirrors what I've long advocated for – clear risk assessment protocols and transparent data handling. I've seen small businesses in Singapore adapt these principles successfully, proving that good governance doesn't have to be overwhelming.

From my experience working with U.S. companies, the Algorithmic Accountability Act has been a game-changer in how organizations approach AI development. Though, I must say, implementing it has been challenging for many of my clients, particularly when balancing innovation with compliance.

The UAE's Leadership Role

Having been based in the UAE for several years, I've had a front-row seat to its AI transformation. A collaborative project between local enterprises and Microsoft at Abu Dhabi's AI center. What struck me was the practical focus on solving real-world problems while maintaining ethical standards.

The DIFC's updated Data Protection Regulations have transformed how we handle AI-related disputes. Just last quarter, I worked on a case where these regulations provided crucial guidance in resolving a complex AI data processing dispute between a global company and its local partner.

The Need for International Collaboration

In my arbitration practice, I'm increasingly dealing with cases that cross multiple jurisdictions. Recently, I handled a dispute involving an AI system deployed in Dubai, developed in Singapore, with data processed in Europe. This complexity is exactly why we need stronger international cooperation.

Toward a Global Governance Framework

Based on my experience mediating AI-related disputes, I believe we need to prioritize:

Transparent Decision-Making: I recently worked with a financial institution that revolutionized their AI transparency after a costly dispute. Their approach of maintaining detailed decision logs and clear audit trails has become a model I often recommend to others.

Shared Accountability: Through my arbitration cases, I've learned that clear liability frameworks are essential. One successful approach I've seen involves staged responsibility matrices, where each party's obligations are clearly defined based on their role in the AI system's lifecycle.

A Collaborative Path Forward

Looking ahead, I remain both optimistic and cautious. The challenges I see in my practice daily remind me that we're still in the early stages of understanding how to govern AI effectively. Yet, the innovation and willingness to collaborate that I witness across borders gives me hope.

In conclusion, while AI's potential excites me as a technologist, my experience as an arbitrator has taught me to remain vigilant about its risks. The path forward requires not just frameworks and regulations, but active engagement from all of us in the technology community.

IT Expert and Arbitrator