The governance of AI is a critical topic of corporate discussion. Get on the right track to getting maximum value from the digitisation data journey. The AI Codex will create a platform to balance transparency, accountability, sustainability and security. The AI Codex will improve security quality, develop fair trade practices, harness technology, and enhance risk management.

The role of directors and management in addressing AI and sustainability issues is crucial for companies. The rapid advancement of AI technology has the potential to significantly impact corporate history, making it essential for corporate leaders to have a unified response.
While financial stability is essential, there are other significant consequences related to AI. One such consequence is the potential for AI models to be used for spreading misinformation. Finding practical solutions to these challenges becomes more difficult without a coordinated global corporate response.

A more coordinated approach is necessary to regulate AI on a global scale, taking into account the geopolitical implications involved. There is no clear path for how this technology will be regulated globally. Global leaders must collaborate and establish frameworks that ensure AI’s responsible and ethical use while fostering innovation and sustainability. Implementing a responsible AI program is critical to achieving AI compliance. The Artificial Intelligence Governance Code by The Corporate Governance Institute is a comprehensive framework for AI technologies’ ethical and accountable development. Addressing concerns such as bias, privacy, and societal impact, the code emphasises the importance of sustainability, transparency, accountability, and risk management throughout the AI lifecycle. The AI Code will harness AI’s potential and comply with emerging regulations through a robust AI governance codex. We recommend that you do not rush, as reaching high AI maturity will take 2-3 years, highlighting the urgency to start now to build customer trust while mitigating risks. Here are some steps to consider:

  1. Risk Management:
    Create a framework to identify high-risk AI applications for enhanced scrutiny. Employ thorough risk management measures at every advanced AI system development stage, from inception to deployment, to identify, assess, and mitigate potential risks.
  2. Transparency:
    Ensure transparency in AI systems, with developers providing clear explanations of system operations and data usage, including disclosure of any biases or limitations.
  3. Accountability:
    Develop principles, policies, and guardrails governing AI use that align with the organisation’s mission and values. Hold developers and organisations accountable for AI system decisions, establish mechanisms to address the harm caused, and provide avenues for recourse and redress.
  4. Privacy and Data Protection:
    Uphold user privacy and adhere to data protection laws, obtaining informed consent for data collection and ensuring secure storage and processing of personal data.
  5. Fairness and Bias:
    Integrate AI governance into existing corporate structures to ensure apparent decision-making authority. Design and train AI systems to be fair and unbiased, actively mitigating biases in training data to prevent discrimination based on protected characteristics.
  6. Human Oversight:
    Establish a senior leaders committee to oversee AI development and implementation that includes human oversight and control for AI systems, allowing intervention, override, or modification of decisions, especially in critical domains like healthcare, finance, and criminal justice.
  7. Safety and Security:
    Prioritise safety and security in AI system design, implementing measures to prevent malicious use, protect against cyber threats, and ensure robustness and reliability.
  8. Social Impact:
    Consider and minimise the societal impact of AI systems, addressing issues such as job displacement, economic inequality, and the potential amplification of social biases.
  9. Early Identification of Vulnerabilities:
    Actively monitor AI systems for vulnerabilities and emerging risks post-deployment, taking appropriate action to address issues and encouraging third-party and user reporting.
  10. Responsible Information Sharing:
    Engage in responsible information sharing and incident reporting among organisations developing advanced AI systems, collaborating with industry, governments, civil society, and academia to enhance AI safety and security.

Monitor high-profile litigation cases related to AI to prepare for evolving legal issues. Building a comprehensive RAI program takes time, but it’s a journey worth it as it can drive value and growth. Contact us for an in-house onsite or online workshop.