Automation and artificial intelligence (AI) processes are becoming more and more common in organizations and marketing tools. Ethical AI, or explainable AI, considers the full impact of AI usage on all stakeholders, from customers and suppliers to employees and society as a whole. Ethical AI helps look for potentially “bad, biased, and unethical” uses of AI.
Two Big Reasons Ethical AI Matters:
- Consumers now expect companies to use their data in an ethical and unbiased way.
- Ethical AI can lead to changes that improve performance.
How to Start Implementing Ethical AI:
- Assess you current AI situation. Conduct a thorough audit to see how machine learning (ML) and AI are being used to make decisions across the marketing organization. This includes: audience targeting, segmentation, modeling, promotions, content, and creative. A trickier audit is looking for potential bias in the media buying process.
- Evaluate your data. Now that you’ve identified areas for potential bias, see if there is anything funky going on with your data. Compare your data set to the general US population to see if there are any outliers in terms of distribution. The goal is to understand where there is deviation so that you have the knowledge and the transparency to see bias in the data and make changes to eliminate it.
- Make changes to your AI use. When you can see where bias exists and what that bias is, you can make decisions to eliminate it. A big portion of that means developing a thorough process and ongoing plan to continually evaluate your data and AI for bias.
AI, automation, algorithms, and machine learning are all powerful tools in marketing today. Making it good for consumers means it needs to be understood, transparent, and free of harmful bias. Check out the full blog post by Merkle for more best practices in Ethical AI.