4,566 research outputs found
Stakeholders in explainable AI
There is general consensus that it is important for artificial
intelligence (AI) and machine learning systems to be explainable
and/or interpretable. However, there is no general
consensus over what is meant by ‘explainable’ and ‘interpretable’.
In this paper, we argue that this lack of consensus
is due to there being several distinct stakeholder communities.
We note that, while the concerns of the individual
communities are broadly compatible, they are not identical,
which gives rise to different intents and requirements for explainability/
interpretability. We use the software engineering
distinction between validation and verification, and the epistemological
distinctions between knowns/unknowns, to tease
apart the concerns of the stakeholder communities and highlight
the areas where their foci overlap or diverge. It is not
the purpose of the authors of this paper to ‘take sides’ — we
count ourselves as members, to varying degrees, of multiple
communities — but rather to help disambiguate what stakeholders
mean when they ask ‘Why?’ of an AI
Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities
Recent advancements in AI applications to healthcare have shown incredible
promise in surpassing human performance in diagnosis and disease prognosis.
With the increasing complexity of AI models, however, concerns regarding their
opacity, potential biases, and the need for interpretability. To ensure trust
and reliability in AI systems, especially in clinical risk prediction models,
explainability becomes crucial. Explainability is usually referred to as an AI
system's ability to provide a robust interpretation of its decision-making
logic or the decisions themselves to human stakeholders. In clinical risk
prediction, other aspects of explainability like fairness, bias, trust, and
transparency also represent important concepts beyond just interpretability. In
this review, we address the relationship between these concepts as they are
often used together or interchangeably. This review also discusses recent
progress in developing explainable models for clinical risk prediction,
highlighting the importance of quantitative and clinical evaluation and
validation across multiple common modalities in clinical practice. It
emphasizes the need for external validation and the combination of diverse
interpretability methods to enhance trust and fairness. Adopting rigorous
testing, such as using synthetic datasets with known generative factors, can
further improve the reliability of explainability methods. Open access and
code-sharing resources are essential for transparency and reproducibility,
enabling the growth and trustworthiness of explainable research. While
challenges exist, an end-to-end approach to explainability in clinical risk
prediction, incorporating stakeholders from clinicians to developers, is
essential for success
The Grammar of Interactive Explanatory Model Analysis
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.Comment: 17 pages, 10 figures, 3 table
Decision support for efficient XAI services - A morphological analysis, business model archetypes, and a decision tree
The black-box nature of Artificial Intelligence (AI) models and their associated explainability limitations create a major adoption barrier. Explainable Artificial Intelligence (XAI) aims to make AI models more transparent to address this challenge. Researchers and practitioners apply XAI services to explore relationships in data, improve AI methods, justify AI decisions, and control AI technologies with the goals to improve knowledge about AI and address user needs. The market volume of XAI services has grown significantly. As a result, trustworthiness, reliability, transferability, fairness, and accessibility are required capabilities of XAI for a range of relevant stakeholders, including managers, regulators, users of XAI models, developers, and consumers. We contribute to theory and practice by deducing XAI archetypes and developing a user-centric decision support framework to identify the XAI services most suitable for the requirements of relevant stakeholders. Our decision tree is founded on a literature-based morphological box and a classification of real-world XAI services. Finally, we discussed archetypical business models of XAI services and exemplary use cases
Explainable product backorder prediction exploiting CNN: Introducing explainable models in businesses
Due to expected positive impacts on business, the application of artificial intelligence has been widely increased. The decision-making procedures of those models are often complex and not easily understandable to the company’s stakeholders, i.e. the people having to follow up on recommendations or try to understand automated decisions of a system. This opaqueness and black-box nature might hinder adoption, as users struggle to make sense and trust the predictions of AI models. Recent research on eXplainable Artificial Intelligence (XAI) focused mainly on explaining the models to AI experts with the purpose of debugging and improving the performance of the models. In this article, we explore how such systems could be made explainable to the stakeholders. For doing so, we propose a new convolutional neural network (CNN)-based explainable predictive model for product backorder prediction in inventory management. Backorders are orders that customers place for products that are currently not in stock. The company now takes the risk to produce or acquire the backordered products while in the meantime, customers can cancel their orders if that takes too long, leaving the company with unsold items in their inventory. Hence, for their strategic inventory management, companies need to make decisions based on assumptions. Our argument is that these tasks can be improved by offering explanations for AI recommendations. Hence, our research investigates how such explanations could be provided, employing Shapley additive explanations to explain the overall models’ priority in decision-making. Besides that, we introduce locally interpretable surrogate models that can explain any individual prediction of a model. The experimental results demonstrate effectiveness in predicting backorders in terms of standard evaluation metrics and outperform known related works with AUC 0.9489. Our approach demonstrates how current limitations of predictive technologies can be addressed in the business domain
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