October 26th, 2023
By David Cass
The rapid adoption of artificial intelligence and machine learning yields tremendous benefits. But as with any transformational technology that can affect human lives and societal structures, there are attendant governance challenges.
Effective governance of AI and ML requires a blueprint to ensure these technologies are used safely, ethically, and responsibly. Understanding the risks associated with these technologies, such as biases, potential misuse, and privacy concerns, is essential. A governance framework will help ensure our organizations have transparency and accountability in their implementation of AI and ML, and they promote the responsible use of these technologies to avoid misuse or unintended consequences.
Having a framework also helps to build trust among the general public and the organization’s stakeholders regarding the deployment of AI and ML. You need to have a standard against which you will be measured.
Key components you need for an effective AI/ML governance framework include:
* Clear objectives. There should be well-defined goals and principles to ensure that any AI or ML introduced is fair, reduces bias, and adheres to the ethical principles you define.
* Clearly defined roles and responsibilities. You want to make sure that you delineate the roles and responsibilities of those involved in developing, deploying, monitoring, and testing AI models.
* Data management. Guidelines on data collection have to be clearly spelled out. What data are being collected? How are data being stored? How are data being processed? How are they being used?
*Implementing transparency. How do you document the processes? How do you document the algorithms and the data sources that are used? This will help you explain the model and potentially explain decisions it may make if you’re called before a board of directors, congressional committee, or some other regulatory or governing body. You need to be able to reconstruct what happened, not just from a regulatory point of view, but to ensure there’s nothing wrong with the model.
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