Neurorobots as a Means Toward Neuroethology and Explainable AI

Chen, Kexin; Hwu, Tiffany; Kashyap, Hirak J.; Krichmar, Jeffrey L.; Stewart, Kenneth; Xing, Jinwei; Zou, Xinyun

doi:10.3389/fnbot.2020.570308

Cited by 18 publications

(16 citation statements)

References 136 publications

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“…As a consequence, explanations should be tailored to the specific audience they are intended for, aiming at conveying the necessary information in a clear way. This observation has naturally led to the development of XAI approaches that specifically target financial applications ( Philippe et al, 2019 ; Bussmann et al, 2020 ; Misheva et al, 2021 ), but further research could lead to more significant advances.…”

Section: Future Directionsmentioning

confidence: 99%

Principles and Practice of Explainable Machine Learning

2021

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Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in diverse areas such as computational biology, law and finance. However, such a highly positive impact is coupled with a significant challenge: how do we understand the decisions suggested by these systems in order that we can trust them? In this report, we focus specifically on data-driven methods—machine learning (ML) and pattern recognition models in particular—so as to survey and distill the results and observations from the literature. The purpose of this report can be especially appreciated by noting that ML models are increasingly deployed in a wide range of businesses. However, with the increasing prevalence and complexity of methods, business stakeholders in the very least have a growing number of concerns about the drawbacks of models, data-specific biases, and so on. Analogously, data science practitioners are often not aware about approaches emerging from the academic literature or may struggle to appreciate the differences between different methods, so end up using industry standards such as SHAP. Here, we have undertaken a survey to help industry practitioners (but also data scientists more broadly) understand the field of explainable machine learning better and apply the right tools. Our latter sections build a narrative around a putative data scientist, and discuss how she might go about explaining her models by asking the right questions. From an organization viewpoint, after motivating the area broadly, we discuss the main developments, including the principles that allow us to study transparent models vs. opaque models, as well as model-specific or model-agnostic post-hoc explainability approaches. We also briefly reflect on deep learning models, and conclude with a discussion about future research directions.

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Section: Future Directionsmentioning

confidence: 99%

Principles and Practice of Explainable Machine Learning

2021

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“…This sudden spike in interest on XAI is partly due to emerging laws as mentioned previously. The steady accumulation in general and specialized review articles on XAI translates the growing interest in XAI from the research community [26][27][28][29][30].…”

Section: Artificial Intelligencementioning

confidence: 99%

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study of Real-World Gas Turbine Anomalies

Nor¹,

Pedapati²,

Muhammad³

et al. 2022

Preprint

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: Mistrust, amplified by numerous artificial intelligence (AI) related incidents, has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle this challenge. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced to XAI. However, many deficiencies, particularly lack of explanation assessment methods and uncertainty quantification, plague this young field. In this paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as marker for anomalies expanding the prognostic explanation scope to include model’s confidence. Also, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of PHM-XAI publications. The quality of the explanation is finally examined employing local accuracy and consistency properties. The method is tested on real-world gas turbine anomalies and synthetic turbofan data failure prediction. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.

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“…Though this notion has been known for decades, global attention garnered in XAI shows a notable rise more recently, reflected by the increasing initiatives by various parties, including the Defense Advanced Research Projects Agency since 2016 [29]. The steady accumulation in general and specialized review articles on XAI translates to the growing interest in XAI from the research community [30][31][32][33][34]. This sudden spike in interest on XAI is partly due to emerging laws as mentioned previously.…”

Section: Introduction 1artificial Intelligencementioning

confidence: 99%

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data

et al. 2022

View full text Add to dashboard Cite

Mistrust, amplified by numerous artificial intelligence (AI) related incidents, is an issue that has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and is fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle such an issue. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced into XAI. However, many deficiencies, particularly the lack of explanation assessment methods and uncertainty quantification, plague this young domain. In the present paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model and Shapley additive explanations (SHAP) to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as a marker for anomalies and expands the prognostic explanation scope to include the model’s confidence. In addition, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of studies on PHM-XAI. The quality of the explanation is examined employing local accuracy and consistency properties. The elaborated framework is tested on real-world gas turbine anomalies and synthetic turbofan failure prediction data. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed a 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.

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Neurorobots as a Means Toward Neuroethology and Explainable AI

Cited by 18 publications

References 136 publications

Principles and Practice of Explainable Machine Learning

Principles and Practice of Explainable Machine Learning

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study of Real-World Gas Turbine Anomalies

Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data

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