19 research outputs found
Play MNIST For Me! User Studies on the Effects of Post-Hoc, Example-Based Explanations & Error Rates on Debugging a Deep Learning, Black-Box Classifier
This paper reports two experiments (N=349) on the impact of post hoc
explanations by example and error rates on peoples perceptions of a black box
classifier. Both experiments show that when people are given case based
explanations, from an implemented ANN CBR twin system, they perceive miss
classifications to be more correct. They also show that as error rates increase
above 4%, people trust the classifier less and view it as being less correct,
less reasonable and less trustworthy. The implications of these results for XAI
are discussed.Comment: 2 Figures, 1 Table, 8 page
Explainable AI for Interpretable Credit Scoring
With the ever-growing achievements in Artificial Intelligence (AI) and the
recent boosted enthusiasm in Financial Technology (FinTech), applications such
as credit scoring have gained substantial academic interest. Credit scoring
helps financial experts make better decisions regarding whether or not to
accept a loan application, such that loans with a high probability of default
are not accepted. Apart from the noisy and highly imbalanced data challenges
faced by such credit scoring models, recent regulations such as the `right to
explanation' introduced by the General Data Protection Regulation (GDPR) and
the Equal Credit Opportunity Act (ECOA) have added the need for model
interpretability to ensure that algorithmic decisions are understandable and
coherent. An interesting concept that has been recently introduced is
eXplainable AI (XAI), which focuses on making black-box models more
interpretable. In this work, we present a credit scoring model that is both
accurate and interpretable. For classification, state-of-the-art performance on
the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is
achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then
further enhanced with a 360-degree explanation framework, which provides
different explanations (i.e. global, local feature-based and local
instance-based) that are required by different people in different situations.
Evaluation through the use of functionallygrounded, application-grounded and
human-grounded analysis show that the explanations provided are simple,
consistent as well as satisfy the six predetermined hypotheses testing for
correctness, effectiveness, easy understanding, detail sufficiency and
trustworthiness.Comment: 19 pages, David C. Wyld et al. (Eds): ACITY, DPPR, VLSI, WeST, DSA,
CNDC, IoTE, AIAA, NLPTA - 202
Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations
AI advice is becoming increasingly popular, e.g., in investment and medical treatment decisions. As this advice is typically imperfect, decision-makers have to exert discretion as to whether actually follow that advice: they have to "appropriately" rely on correct and turn down incorrect advice. However, current research on appropriate reliance still lacks a common definition as well as an operational measurement concept. Additionally, no in-depth behavioral experiments have been conducted that help understand the factors influencing this behavior. In this paper, we propose Appropriateness of Reliance (AoR) as an underlying, quantifiable two-dimensional measurement concept. We develop a research model that analyzes the effect of providing explanations for AI advice. In an experiment with 200 participants, we demonstrate how these explanations influence the AoR, and, thus, the effectiveness of AI advice. Our work contributes fundamental concepts for the analysis of reliance behavior and the purposeful design of AI advisors
Interpretability of machine learning solutions in public healthcare : the CRISP-ML approach
Public healthcare has a history of cautious adoption for artificial intelligence (AI) systems. The rapid growth of data collection and linking capabilities combined with the increasing diversity of the data-driven AI techniques, including machine learning (ML), has brought both ubiquitous opportunities for data analytics projects and increased demands for the regulation and accountability of the outcomes of these projects. As a result, the area of interpretability and explainability of ML is gaining significant research momentum. While there has been some progress in the development of ML methods, the methodological side has shown limited progress. This limits the practicality of using ML in the health domain: the issues with explaining the outcomes of ML algorithms to medical practitioners and policy makers in public health has been a recognized obstacle to the broader adoption of data science approaches in this domain. This study builds on the earlier work which introduced CRISP-ML, a methodology that determines the interpretability level required by stakeholders for a successful real-world solution and then helps in achieving it. CRISP-ML was built on the strengths of CRISP-DM, addressing the gaps in handling interpretability. Its application in the Public Healthcare sector follows its successful deployment in a number of recent real-world projects across several industries and fields, including credit risk, insurance, utilities, and sport. This study elaborates on the CRISP-ML methodology on the determination, measurement, and achievement of the necessary level of interpretability of ML solutions in the Public Healthcare sector. It demonstrates how CRISP-ML addressed the problems with data diversity, the unstructured nature of data, and relatively low linkage between diverse data sets in the healthcare domain. The characteristics of the case study, used in the study, are typical for healthcare data, and CRISP-ML managed to deliver on these issues, ensuring the required level of interpretability of the ML solutions discussed in the project. The approach used ensured that interpretability requirements were met, taking into account public healthcare specifics, regulatory requirements, project stakeholders, project objectives, and data characteristics. The study concludes with the three main directions for the development of the presented cross-industry standard process
A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods
The recent surge in publications related to explainable artificial
intelligence (XAI) has led to an almost insurmountable wall if one
wants to get started or stay up to date with XAI. For this reason,
articles and reviews that present taxonomies of XAI methods seem
to be a welcomed way to get an overview of the field. Building on
this idea, there is currently a trend of producing such taxonomies,
leading to several competing approaches to construct them. In this
paper, we will review recent approaches to constructing taxonomies
of XAI methods and discuss general challenges concerning them as
well as their individual advantages and limitations. Our review is
intended to help scholars be aware of challenges current taxonomies
face. As we will argue, when charting the field of XAI, it may not
be sufficient to rely on one of the approaches we found. To amend
this problem, we will propose and discuss three possible solutions:
a new taxonomy that incorporates the reviewed ones, a database of
XAI methods, and a decision tree to help choose fitting methods
Explanation matters:An experimental study on explainable AI
Explainable artificial intelligence (XAI) is an important advance in the field of machine learning to shed light on black box algorithms and thus a promising approach to improving artificial intelligence (AI) adoption. While previous literature has already addressed the technological benefits of XAI, there has been little research on XAI from the user’s perspective. Building upon the theory of trust, we propose a model that hypothesizes that post hoc explainability (using Shapley Additive Explanations) has a significant impact on use-related variables in this context. To test our model, we designed an experiment using a randomized controlled trial design where participants compare signatures and detect forged signatures. Surprisingly, our study shows that XAI only has a small but significant impact on perceived explainability. Nevertheless, we demonstrate that a high level of perceived explainability has a strong impact on important constructs including trust and perceived usefulness. A post hoc analysis shows that hedonic factors are significantly related to perceived explainability and require more attention in future research. We conclude with important directions for academia and for organizations.</p
What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research
Previous research in Explainable Artificial Intelligence (XAI) suggests that
a main aim of explainability approaches is to satisfy specific interests,
goals, expectations, needs, and demands regarding artificial systems (we call
these stakeholders' desiderata) in a variety of contexts. However, the
literature on XAI is vast, spreads out across multiple largely disconnected
disciplines, and it often remains unclear how explainability approaches are
supposed to achieve the goal of satisfying stakeholders' desiderata. This paper
discusses the main classes of stakeholders calling for explainability of
artificial systems and reviews their desiderata. We provide a model that
explicitly spells out the main concepts and relations necessary to consider and
investigate when evaluating, adjusting, choosing, and developing explainability
approaches that aim to satisfy stakeholders' desiderata. This model can serve
researchers from the variety of different disciplines involved in XAI as a
common ground. It emphasizes where there is interdisciplinary potential in the
evaluation and the development of explainability approaches.Comment: 57 pages, 2 figures, 1 table, to be published in Artificial
Intelligence, Markus Langer, Daniel Oster and Timo Speith share
first-authorship of this pape
Explainable software systems: from requirements analysis to system evaluation
The growing complexity of software systems and the influence of software-supported decisions in our society sparked the need for software that is transparent, accountable, and trustworthy. Explainability has been identified as a means to achieve these qualities. It is recognized as an emerging non-functional requirement (NFR) that has a significant impact on system quality. Accordingly, software engineers need means to assist them in incorporating this NFR into systems. This requires an early analysis of the benefits and possible design issues that arise from interrelationships between different quality aspects. However, explainability is currently under-researched in the domain of requirements engineering, and there is a lack of artifacts that support the requirements engineering process and system design. In this work, we remedy this deficit by proposing four artifacts: a definition of explainability, a conceptual model, a knowledge catalogue, and a reference model for explainable systems. These artifacts should support software and requirements engineers in understanding the definition of explainability and how it interacts with other quality aspects. Besides that, they may be considered a starting point to provide practical value in the refinement of explainability from high-level requirements to concrete design choices, as well as on the identification of methods and metrics for the evaluation of the implemented requirements
Explainable software systems: from requirements analysis to system evaluation
The growing complexity of software systems and the influence of software-supported decisions in our society sparked the need for software that is transparent, accountable, and trustworthy. Explainability has been identified as a means to achieve these qualities. It is recognized as an emerging non-functional requirement (NFR) that has a significant impact on system quality. Accordingly, software engineers need means to assist them in incorporating this NFR into systems. This requires an early analysis of the benefits and possible design issues that arise from interrelationships between different quality aspects. However, explainability is currently under-researched in the domain of requirements engineering, and there is a lack of artifacts that support the requirements engineering process and system design. In this work, we remedy this deficit by proposing four artifacts: a definition of explainability, a conceptual model, a knowledge catalogue, and a reference model for explainable systems. These artifacts should support software and requirements engineers in understanding the definition of explainability and how it interacts with other quality aspects. Besides that, they may be considered a starting point to provide practical value in the refinement of explainability from high-level requirements to concrete design choices, as well as on the identification of methods and metrics for the evaluation of the implemented requirements