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  • Harrison A.

The Business Case for XAI

Explainable AI (XAI) has immediate business operations benefits when leveraged correctly. As AI/ML becomes a more utilized technology, the opportunity for implementation pitfalls increases.


Today, many AI/ML projects or products befall to issues that XAI can assist in. These issues are things like consumer distrust, litigation potential, and stagnant model performance. Combining these implementation weaknesses with a projected exponential increase in AI/ML, the need for XAI will never be greater in the next 5-10 years.


The business case for XAI can be illustrated in multiple areas:


  1. Model Trust - When an AI/ML model is explainable, model consumers are far more likely to trust and use the product. A black box product can drive consumer assumptions to its inner workings that can be good (and bad). By increasing trust, sunk cost is avoided and profits increased.

  2. Model Improvement - Many AI/ML models are trained and initially have strong performance. Over time, variables and the environment around the model changes, which requires a focused improvement effort. Rather than using excess effort retraining an entire model, XAI can aid in pinpointing improvement areas or reducing cost in data capture efforts for features that may not be significant in prediction.

  3. Risk Mitigation - While some AI/ML products have minimal negative impacts, such as a simple model for predicting a runner's 5K finish time based on their cadence, speed and other features, some AI/ML products can have severe negative impacts if their results are incorrect. An example of this is the current race in the automobile industry to develop self-driving in cars. This type of complex model can have severe litigation risk if it makes one incorrect decision. XAI can assist in tracing the series of decisions that led to a failure. XAI is especially effective in this case when its used during the testing phase of model development.


Businesses considering AI/ML products (as a consumer or developer) should consider requiring XAI during model development. XAI models generally add very little additional development time but have tremendous value in the long run.

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