by Urszula Czerwinska

Machine Learning (ML) or Artificial Intelligence (AI) models have become a common tool in business and research over the past years. Models, for example, assist our decisions on which hotel to book or set the optimal price for a flight ticket based on real-time demand, and recommendation apps advise us where to eat or which sights are worth seeing. However, not all decisions taken by automated systems are easily understandable by human beings. How can one make sure that the model does not encompass unwanted bias or discriminate clients? How can one explain to the CEO that the price should be less than what the concurrence proposes? Fortunately, ML models that are explainable do indeed exist, and there is also active research on how to make unexplainable models interpretable. The most popular methods involve Lime and Shapley values, allowing researchers to identify the importance of the input variables and, in turn, to add an interpretation to the model prediction. In this regard, this chapter will describe how to use interpretability tools with ML models and will also provide intuition on the usefulness and limitations of these tools.