Artificial Intelligence (AI) has revolutionized financial services, accuracy, prediction, response time, and more. But, decisions and recommendations made by algorithms shouldn’t be completely handled by AI without oversight. We should maintain confidence in why automated suggestions and conclusions were made.
This isn’t to say that AI is inaccurate. Computers exceed our ability to handle and parse huge amounts of data – but data can be weird and unique. AI is only as good as the data fed into it and it is only as smart as the training and modeling it goes through to learn.
Understanding how and why AI arrives at conclusions is known as Model Explainability. It’s the opposite of black box calculations. This kind of skill/oversight is incredibly critical, especially as we hand over more jobs, reasoning, and recommendations to artificial intelligence.
Finance organizations understand reputation and customer satisfaction ride on these interactions. Clients don’t want to hear your organization blame AI for their bad credit score without being able to talk them through the key data put into the system and why the recommendation came out the way it did.
Explainable AI should not be an afterthought. Checking in should be part of an integrated process – from the beginning of data collection, and processing to model training, evaluation, and model serving.
Here are some ways you can insert explainability into your AI processes:
- Sample the AI against real data, not just test data.
- Inspect what you expect.
- Creating model cards can help explain limitations or trade-offs and provide a way to test out what the model does.
- During machine learning, don’t create unfair bias. If you notice that a model is not performing well address those concerns.
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