AI governance is needed to properly monitor and evaluate algorithms for ROI, bias, risk, effectiveness, etc. Unfortunately, due importance is not given to this process.
CIOs acknowledge that enterprises often do not take proper measures when drafting AI governance strategies. These governance measures are especially required for monitoring bias, risk, and ROI, among other factors in the enterprise.
Enterprise leaders feel that the main reason for this disconnect is the near non-existence of coordination relevant to AI projects in an organization. More often than not, data science departments are separate and isolated from application development. To add to this issue, regulators have started posing questions that businesses are unable to answer satisfactorily.
AI is known to provide solutions and introduce new problems as well. CIOs are aware that training data is more often than not flawed with duplications, bias, and errors. The other major issue is the model drift. As a result, AI degrades gradually due to data and algorithms not accurately reflecting real-time changes. Often enterprises miss out on revenue opportunities and make wrong decisions due to these discrepancies.
CIOs are looking to see if AI governance automation can be a solution to this issue. This solution is still in its nascent stages and thus needs a hands-on approach. It requires effective management of people and processes to be accurate.
To develop good AI governance, it is imperative to have an effective data policy in place. CIOs point out that AI and data share a close relationship. Organizations need to evaluate what data they have, where they source it from, what modifications are being done to it, and who is accountable for the modifications.
Organizations also need to be aware of factors like differences in application developers and data scientists’ approach. The difference often leads to a communication breakdown between them. They must clearly define the principles and requirements.
The next process is to build an AI governance plan. Before deploying the machine learning algorithm to the public, it needs to be done either as a recommendation engine, voice-based commerce bot or for image analysis. This process is important not only for healthcare, banking, or finance but for all enterprises where decisions are being made depending on the algorithm’s output, that will directly impact the organization and the clients.
The plan should be clear, actionable, and straightforward. It should be answerable to the concerns of all stakeholders. One team or person is required to own the plan and review it frequently. It will be necessary to consult Operation engineers and AI/Data engineers to the plan for all projects.
Often AI governance adds complexity to a process that may be quite unnecessary. The plan doesn’t need to be comprehensive. When AI is not the main business focus for an enterprise, it will suffice to have simple guidelines sets that allow agile behavior for the development team and lets them follow best practices. However, if they require AI in multiple domains, an AI Centre of Excellence will be the recommended course of action for the centralization of consistency and completeness of AI projects and efforts.
CIOs explain that other than the complex part of setting up the plan’s goals, it is also challenging due to explainability, transparency, fairness, and ethics. Each industry has its own methods; thus a standard template cannot be created. A sufficient amount of time needs to be spent on developing the plan while making everyone feel included. Transparency is the most important factor for AI governance; it clarifies why a decision needs to be made. Ethics and fairness help understand if the final decision is required, and if required, is it unprejudiced.
The key take-away point is that enterprises should not wait until it’s too late. They should develop a governance plan before implementing the AI project.