3,797 research outputs found
The Pragmatic Turn in Explainable Artificial Intelligence (XAI)
In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal
Explainable artificial intelligence (XAI) provides explanations for not
interpretable machine learning (ML) models. While many technical approaches
exist, there is a lack of validation of these techniques on real-world
datasets. In this work, we present a use-case of XAI: an ML model which is
trained to estimate electrification rates based on mobile phone data in
Senegal. The data originate from the Data for Development challenge by Orange
in 2014/15. We apply two model-agnostic, local explanation techniques and find
that while the model can be verified, it is biased with respect to the
population density. We conclude our paper by pointing to the two main
challenges we encountered during our work: data processing and model design
that might be restricted by currently available XAI methods, and the importance
of domain knowledge to interpret explanations.Comment: The 1st World Conference on eXplainable Artificial Intelligence (xAI
2023
eXplainable Artificial Intelligence (XAI) in aging clock models
eXplainable Artificial Intelligence (XAI) is a rapidly progressing field of
machine learning, aiming to unravel the predictions of complex models. XAI is
especially required in sensitive applications, e.g. in health care, when
diagnosis, recommendations and treatment choices might rely on the decisions
made by artificial intelligence systems. AI approaches have become widely used
in aging research as well, in particular, in developing biological clock models
and identifying biomarkers of aging and age-related diseases. However, the
potential of XAI here awaits to be fully appreciated. We discuss the
application of XAI for developing the "aging clocks" and present a
comprehensive analysis of the literature categorized by the focus on particular
physiological systems
Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model
This empirical study proposes a novel methodology to measure users' perceived
trust in an Explainable Artificial Intelligence (XAI) model. To do so, users'
mental models are elicited using Fuzzy Cognitive Maps (FCMs). First, we exploit
an interpretable Machine Learning (ML) model to classify suspected COVID-19
patients into positive or negative cases. Then, Medical Experts' (MEs) conduct
a diagnostic decision-making task based on their knowledge and then prediction
and interpretations provided by the XAI model. In order to evaluate the impact
of interpretations on perceived trust, explanation satisfaction attributes are
rated by MEs through a survey. Then, they are considered as FCM's concepts to
determine their influences on each other and, ultimately, on the perceived
trust. Moreover, to consider MEs' mental subjectivity, fuzzy linguistic
variables are used to determine the strength of influences. After reaching the
steady state of FCMs, a quantified value is obtained to measure the perceived
trust of each ME. The results show that the quantified values can determine
whether MEs trust or distrust the XAI model. We analyze this behavior by
comparing the quantified values with MEs' performance in completing diagnostic
tasks.Comment: Accepted in IJCAI 2023 Workshop on Explainable Artificial
Intelligence (XAI
Strategies to exploit XAI to improve classification systems
Explainable Artificial Intelligence (XAI) aims to provide insights into the
decision-making process of AI models, allowing users to understand their
results beyond their decisions. A significant goal of XAI is to improve the
performance of AI models by providing explanations for their decision-making
processes. However, most XAI literature focuses on how to explain an AI system,
while less attention has been given to how XAI methods can be exploited to
improve an AI system. In this work, a set of well-known XAI methods typically
used with Machine Learning (ML) classification tasks are investigated to verify
if they can be exploited, not just to provide explanations but also to improve
the performance of the model itself. To this aim, two strategies to use the
explanation to improve a classification system are reported and empirically
evaluated on three datasets: Fashion-MNIST, CIFAR10, and STL10. Results suggest
that explanations built by Integrated Gradients highlight input features that
can be effectively used to improve classification performance.Comment: This work has been accepted to be presented to The 1st World
Conference on eXplainable Artificial Intelligence (xAI 2023), July 26-28,
2023 - Lisboa, Portuga
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