Generative AI Governance Platform

GenAI Governance is a key aspect of any GenAI strategy, and we’ve broken this concept down into some distinctive principles:

• Transparency - Ensure transparency in AI decision-making processes and algorithms to build trust and understanding.
• Accountability - Clearly define roles and responsibilities, holding individuals and organizations accountable for the development and deployment of AI systems.
• Fairness - Strive for fairness and avoid biases in AI systems, promoting equal treatment and opportunities for all users.
• Security - Implement robust security measures to safeguard AI systems from cyber threats and unauthorized access.

As with any framework component, there are various potential risks and challenges that influence ethical practice in regards to GenAI.

• Bias and discrimination - GenAI systems may inherit biases from training data, leading to discriminatory outcomes.
• Privacy concerns - The generation of realistic content raises privacy concerns, especially when it involves personal or sensitive information.
• Regulatory compliance -  Evolving regulations and standards pose challenges in ensuring compliance and keeping AI practices up-to-date.
• Understanding disparity/knowledge gaps - Users and stakeholders may not fully understand how GAI systems operate, leading to mistrust and skepticism.

Integrating ethical considerations at the earliest stages of Generative AI development is essential to proactively identify and mitigate risks, build trust with users and stakeholders, ensure legal compliance, and contribute to the long-term viability of AI systems by addressing societal concerns and fostering responsible development.

Being aware of ethical factors also means acknowledging the fact that some of these influences are always going to be present within the space that AI is deployed in, and by those it is deployed by. Bias is a perfect example of this, and we can apply it to the employees utilizing any form of Generative AI technology. Each individual has their own (often unconscious) biases, and they can impact the output generated in each case. In this way, organizations that want to enjoy the full benefits of Generative AI must account for bias from the offset, and find ways to manage it for each application. 

A model GenAI governance framework refers to a set of policies, procedures, and guidelines that govern the development, deployment, and use of AI models within an enterprise. We’ve shortlisted its key components below:

• Ethical guidelines - Provide a clear outline of the ethical considerations and principles that guide AI model development and usage.
• Transparency measures - Define how transparency will be met, including explanations of model decisions and disclosure of potential biases.
• Accountability mechanisms - Establish roles and responsibilities, and specify who is accountable for various aspects of AI development and deployment.
• Data governance - Define protocols for responsible data handling, including data privacy, security, and consent.
• Risk assessment - Conduct regular risk assessments to identify and mitigate potential risks associated with AI models. 
• Regulatory compliance - Ensure that the AI governance framework aligns with relevant regulations.
• Continuous monitoring and evaluation - Implement mechanisms for ongoing evaluation and improvement of AI models to adapt to changing circumstances.
• Stakeholder involvement - Involve stakeholders in the governance process to incorporate diverse perspectives and address concerns effectively.

AI is weaving its way into normality, and in the context of business operations, it is already creating unprecedented effects. From sparking new innovation and creativity, to unleashing automation for core tasks, the technology is being widely embraced across different industries. Simultaneously, AI solutions are constantly being developed to fulfill new needs, and in turn, the governance frameworks needed to sustain it are being prompted to fit this evolving demand. 

Companies that have invested in AI are now having to reassess their usage in light of new, ethical questions around applications. The consequences of not doing so have significant gravity, varying from prosecution to tarnished brand reputation. Having an adequate governance policy isn’t an isolated task - it requires constant review, adding to the responsibility that enterprises have in order to benefit from the technology in a compliant, safe, secure way.