Governance involves the use of clear oversight into the decision-making process in which AI is involved. This must be addressed before it enters your regulated workflows.
The recent EU AI Act has made logging and governance a priority for global businesses. If not, they could face a fine of 7% in global turnover. Yet aside from this, comprehensive audit trails, AI-related or not, are known to increase compliance efficiency by 30%. Thus, using effective decision governance and the right frameworks must be planned and executed before they enter your regulated workflow, to increase efficiency and prevent regulatory backlash.
AI Decision Governance
When AI enters your workflow, it can promise the earth. Speed, the ability to scale quickly, and lightning-fast adaptability are all within your grasp. Yet using AI does not eliminate the concept of risk. Instead, it takes a different form than that which you may have been used to before.
Generally, this stems from black box thinking. This is when the process used to reach a decision becomes unclear. With AI systems, people very often know what goes in, such as the data they provide, and then they see the outcome. Yet how this is reached, and what processes the AI uses, becomes unclear. This is where AI governance comes in.
AI decision governance is a set of standards that ensures AI systems are safe and ethical. It helps to provide oversight in research, development, and applications, eliminating bias and promoting fair practice. It can also help a company maintain regulatory compliance.
The Issues with AI
One issue is that AI works on data that goes into it, which is usually accrued by humans. This means they inherently contain biases and errors. The result is that AI can give incorrect information, which impacts not just your regulated workflow but the wider business brand and workforce. Strong governance helps reduce these risks, spotting and building procedures to mitigate them.
Another issue is that if AI enters systems that are already broken, it won't fix them. Instead, it could just amplify. This means that systems must be efficient and in place before AI decision-making enters the fray. This is another reason why an AI governance framework is essential. When regulated AI workflows enter your organisation, the question should not be how fast you can adopt, but how much you can trust the system at scale.
Why Transparency Is Essential
The key to this is transparency. When a company is open about how it uses AI in its workflows, it builds trust, not just of the company but of AI usage itself. Key details such as data sources, model processes, and algorithms should be understood and shared so that people can see what decisions have been made based on them.
An AI audit trail is a key part of this. This maps the entire lifecycle of an AI system and its place in your workflow. In many cases, they are essential for compliance with the EU AI Act and GDPR, which demand tamper-proof records of AI systems. Estimates are that only 28% of companies using AI are doing this so far.
What Does an AI Governance Framework Look Like?
An AI governance framework will differ depending on your organization, what it has implemented into its workflow, and even the AI you use. Yet it can be generally broken down into several different factors.
The first of these is organization. This addresses the basis for AI through best practice, and looks at people, processes, and data. It is about how oversight is gained while reducing risk and with a movement toward the company's goals.
Second to this are the legal and compliance pillars. These are the elements that ensure you don’t fall foul of fines due to government or industry standards being overlooked. Legal risk and strategies are included in this section. A company may need to start thinking about how they deploy legal protections, safeguards, and look forward to emerging trends, and how they will have to comply with legal regulations.
Lastly, ethics and transparency must be introduced. The processes involved must be explainable AI systems for the public, shareholders, and other stakeholders. Thus, accountability and structure must be built in at this point. This can include removing bias that may interfere with discrimination, cultural norms, sensitivity, and other related factors. Any decisions made must be easy to interpret. In many ways, this is the hardest of all sections, given the aforementioned black box nature of AI decision-making.
Thus, getting your regulated workflows in top condition and reducing issues to a minimum is imperative when considering AI. When you do bring these systems in to automate your workflow and the decision-making process, its governance must be first and foremost. Without it, your company will have a lack of transparency and accountability, which will impact all the stakeholders involved. Plan this from the start, implementing it into your overall work ethos, then bring it into AI systems and get the speed and scalability you desire.
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