Oxygy and Bird & Bird – assisting in the deployment of Responsible AI

AI technologies cover a wide range of methodologies, such as machine learning and deep learning. They are reshaping how businesses function, driving advancements in research, and our work effectiveness in a variety of day-to-day tasks. Given the rapid advancement of AI, it is crucial for companies embracing this technology to carefully understand how to implement AI in a sustainable way, considering issues such as privacy, bias, and job automation. These concerns require a careful risk-based approach as the technology continues to evolve.

Deploying AI tools, whether they're third-party applications or developed in-house, comes with its share of risks and rewards. OXYGY and Bird & Bird together are assisting clients to establish responsible governance structures for AI implementation, providing not only risk mitigation but also a valuable competitive advantage. Our Responsible AI solution revolves around an ESG-inspired governance framework, systematically tackling the various facets of risk and opportunity linked to organisational AI deployment, encompassing aspects such as legal compliance, contractual matters, liability, and intellectual property issues.

We address Sustainable AI implementation by using the “Operating Model” shown below:

operating model addressing sustainable AI implementation

Purpose: Every engagement must start from the client’s fundamental purpose. From here, Responsible AI projects will be modelled around the client’s purpose, mission, vision and values.

Structure: AI governance structure for every step of the AI value-chain (e.g., AI vendor management environment), Ethics Advisory Board, embedding within existing organisational structure.

Process: Embedding Ethics-by-design, continuous improvement, fast and safe new product acquisition and vendor/supply management.

People: Invest in value-creating capabilities around AI ethics training, AI technical & management talent.

Technology and data: Robust information systems and knowledge processes to improve quality of AI stack (data, models, apps, and overall processes/use-cases).

Performance and management: Simple, transparent and actionable performance metrics linked to core purpose (e.g. OKRs).


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