If you have an artificial intelligence program, you also have a committee, team, or body that is providing governance over AI development, deployment, and use. If you don’t, one needs to be created.
In my last article, I shared the key areas for applying AI and ML models in marketing and how those models can help you innovate and meet client demands. Here I look at marketing’s responsibility for AI governance.
So, what is AI governance?
AI governance is what we call the framework or process that manages your use of AI. The goal of any AI governance effort is simple — mitigate the risks attached to using AI. To do this, organizations must establish a process for assessing the risks of AI-driven algorithms and their ethical usage.
The stringency of the governance is highly dependent on industry. For example, deploying AI algorithms in a financial setting could have greater risks than deploying AI in manufacturing. The use of AI for assigning consumer credit scores needs more transparency and oversight than does an AI algorithm that distributes parts cost-effectively around a plant floor.
To manage risk effectively, an AI governance program should look at three aspects of AI-driven applications:
In my last article, I shared the key areas for applying AI and ML models in marketing and how those models can help you innovate and meet client demands. Here I look at marketing’s responsibility for AI governance.
So, what is AI governance?
AI governance is what we call the framework or process that manages your use of AI. The goal of any AI governance effort is simple — mitigate the risks attached to using AI. To do this, organizations must establish a process for assessing the risks of AI-driven algorithms and their ethical usage.
The stringency of the governance is highly dependent on industry. For example, deploying AI algorithms in a financial setting could have greater risks than deploying AI in manufacturing. The use of AI for assigning consumer credit scores needs more transparency and oversight than does an AI algorithm that distributes parts cost-effectively around a plant floor.
To manage risk effectively, an AI governance program should look at three aspects of AI-driven applications:
- Data: What data is the algorithm using? Is the quality appropriate for the model? Do data scientists have access to the data needed? Will privacy be violated as part of the algorithm? (Although this is never intentional, some AI models could inadvertently expose sensitive information.) As data may change over time, it is necessary to consistently govern the data’s use in the AI/ML model.
- Algorithms. If the data has changed, does it alter the output of the algorithm? For example, if a model was created to predict which customers will purchase in the next month, the data will age with each passing week and affect the output of the model. Is the model still generating appropriate responses or actions? Because the most common AI model in marketing is machine learning, marketers need to watch for model drift. Model drift is any change in the model’s predictions. If the model predicts something today that is different from what it predicted yesterday, then the model is said to have “drifted.”
- Use. Have those that are using the AI model’s output been trained on how to use it? Are they monitoring outputs for variances or spurious results? This is especially important if the AI model is generating actions that marketing uses. Using the same example, does the model identify those customers who are most likely to purchase in the next month? If so, have you trained sales or support reps on how to handle customers who are likely to buy? Does your website “know” what to do with those customers when they visit? What marketing processes are affected as a result of this information?
