When analysing a complex system, very often an answer to one question raises new questions. This also applies to the explanatory analysis of machine learning models. We cannot sufficiently explain a complex model using a single method that gives only one perspective. Isolated explanations are prone to misunderstanding, which inevitably leads to wrong reasoning. Surprisingly, the majority of methods developed for Explainable Artificial Intelligence (XAI) focus on a single aspect of the model behaviour. In this paper, we show the problem of model explainability as an interactive and sequential analysis of a model. We show how different XAI methods complement each other and why it is essential to juxtapose them together. The proposed process of Interactive Explanatory Model Analysis (IEMA) derives from the theoretical, algorithmic side of the model explanation and aims to embrace ideas developed in cognitive sciences. Its grammar is implemented in the modelStudio framework that adopts interactivity, customisability and automation as its main traits  .
The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best? It turns out that this is an ill-posed question. One cannot sufficiently explain a black-box machine learning model using a single method that gives only one perspective. Isolated explanations are prone to misunderstanding, which inevitably leads to wrong or simplistic reasoning. This problem is known as the Rashomon effect and refers to diverse, even contradictory interpretations of the same phenomenon. Surprisingly, the majority of methods developed for explainable machine learning focus on a single aspect of the model behavior. In contrast, we showcase the problem of explainability as an interactive and sequential analysis of a model. This paper presents how different Explanatory Model Analysis (EMA) methods complement each other and why it is essential to juxtapose them together. The introduced process of Interactive EMA (IEMA) derives from the algorithmic side of explainable machine learning and aims to embrace ideas developed in cognitive sciences. We formalize the grammar of IEMA to describe potential human-model dialogues. IEMA is implemented in the human-centered framework that adopts interactivity, customizability and automation as its main traits . Combined, these methods enhance the responsible approach to predictive modeling .
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When analysing a complex system, very often an answer to one question raises new questions. This also applies to the explanatory analysis of machine learning models. We
The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best? It turns out that this is an ill-posed question. One
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complex
black-box machine learning
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reasoning.
or simplistic reasoning. This problem is known as the Rashomon effect and refers to diverse, even contradictory interpretations of the same phenomenon.
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Explainable Artificial Intelligence (XAI)
explainable machine learning
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behaviour. In this paper, we show
behavior. In contrast, we showcase
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model
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We show how different XAI
This paper presents how different Explanatory Model Analysis (EMA)
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proposed
introduced
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Explanatory Model Analysis
EMA
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theoretical,
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the model explanation
explainable machine learning
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Its grammar
We formalize the grammar of IEMA to describe potential human-model dialogues. IEMA
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modelStudio
human-centered
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customisability
customizability
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. Combined, these methods enhance the responsible approach to predictive modeling