Dónal Doyle
Dónal is a data analytics leader at Google, where he focuses on developing intelligent, data-driven solutions in the HR operations space. With a strong foundation in applied AI and Business Intelligence, his work focus on bridging data analytics with practical organisational needs, building transparent systems that solve complex business problems and empower business-facing teams.
Donal holds a Ph.D. in Machine Learning from Trinity College Dublin. His research extensively explored Case-Based Reasoning (CBR), explainable AI (XAI), and the evolution of communities in dynamic social networks. At Google, he leverages this expertise in explainable models to develop data-driven metrics and analytical tools that drive organisational performance and optimise HR workflows.
Donal holds a Ph.D. in Machine Learning from Trinity College Dublin. His research extensively explored Case-Based Reasoning (CBR), explainable AI (XAI), and the evolution of communities in dynamic social networks. At Google, he leverages this expertise in explainable models to develop data-driven metrics and analytical tools that drive organisational performance and optimise HR workflows.
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In large-scale distributed enterprises, traditional Knowledge Management (KM) systems face a critical failure mode: static documentation cannot keep pace with evolving operational realities and regional nuances. This "knowledge latency" forces employees out of self-service workflows and into costly support ticketing queues. This paper introduces SENTINEL, a geo-contextual AI framework designed to shift enterprise support from reactive retrieval to proactive interception. The architecture employs a novel dual-engine system integrated into an omni-present interface. The first engine utilizes Large Language Models (LLMs) to conduct pre-emptive, historical case-grounded audits of documentation, generating a "Contextual Density" score that identifies friction zones. The second engine is an autonomous Retrieval-Augmented Generation (RAG) agent that surfaces in-situ via a location-intelligent assistant window, resolving queries in real-time. By functioning as a strategic "defensive barrier" at the point of origin, SENTINEL demonstrates how a proactive AI assistant can drive high-fidelity, in-situ case deflection.
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Tracking the Evolution of Communities in Dynamic Social Networks
Derek Greene
Padraig Cunningham
Proceedings International Conference on Advances in Social Networks Analysis and Mining (ASONAM'10) (2010), pp. 176-183
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Traditional explanation strategies in machine learning have been dominated by rule and decision tree based approaches. Case-based explanations represent
an alternative approach which has inherent advantages in terms of transparency and
user acceptability. Case-based explanations are based on a strategy of presenting
similar past examples in support of and as justification for recommendations made.
The traditional approach to such explanations, of simply supplying the nearest
neighbour as an explanation, has been found to have shortcomings. Cases should be
selected based on their utility in forming useful explanations. However, the relevance
of the explanation case may not be clear to the end user as it is retrieved using
domain knowledge which they themselves may not have. In this paper the focus is
on a knowledge-light approach to case-based explanations that works by selecting
cases based on explanation utility and offering insights into the effects of featurevalue differences. In this paper we examine to two such knowledge-light frameworks
for case-based explanation. We look at explanation oriented retrieval (EOR) a
strategy which explicitly models explanation utility and also at the knowledge-light
explanation framework (KLEF) that uses local logistic regression to support casebased explanation.
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Comparison of the Evaluations of a Case-Based Reasoning Decision Support Tool by Expert Reviewers with those of End Users.
Paul Walsh
Padraig Cunningham
Western Journal of Emergency Medicine, 9 (2008), pp. 72-80
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Decision-support tools (DST) are typically developed by computer engineers for use
by clinicians. Prototype testing DSTs may be performed relatively easily by one or two clinical
experts. The costly alternative is to test each prototype on a larger number of diverse clinicians,
based on the untested assumption that these evaluations would more accurately reflect those of actual end users.
We hypothesized substantial or better agreement (as defined by a κ-statistic greater
than 0.6) between the evaluations of a case based reasoning (CBR) DST predicting ED admission for bronchiolitis performed by the clinically diverse end users, to those of two clinical experts who evaluated the same DST output
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The research presented here explores the hypothesis that the deployment and acceptance of decision support systems in medicine will be enhanced if the basis for the recommendation produced by the system is apparent. We describe a decision support system for advising on patients suffering from bronchiolitis. This system supports its recommendations with precedent cases selected to support the recommendation along with justification text that highlights aspects of these cases relevant to the query case. It also presents an estimate of its confidence in the recommendation. The main contribution of this paper is an evaluation of this system in a clinical context. The evaluation shows that this type of explanation does enhance the usefulness of the system for practitioners
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Generating Estimates of Classification Confidence for a Case-Based Spam Filter.
Sarah Jane Delany
Padraig Cunningham
Procs. of the 6th International Conference on Case-Based Reasoning (2005), pp. 177-190
Knowledge-Light Mechanism for Explanation in Case Based Reasoning.
Ph.D. Thesis (2005)
Preview abstract
Decision support systems are currently achieving higher classification accuracies by using more complex reasoning mechanisms. Examples of such mechanisms include support vector machines and neural networks. However in spite of these increases in accuracy many decision support systems are not accepted by users. In domains where there is a high cost associated with incorrect classifications, such as medical domains, users are not always willing to accept a decision support system’s classification without proper justification.
In every walk of life, from the home to the workplace, people use explanations all the time
to justify their opinions. Explanations can have many different forms depending on the context
in which they are used. Over the last few decades there has been a vast amount of research by
philosophers into the importance and the requirements of suitable explanations.
In spite of the importance of suitable explanations to justify an opinion, many decision support
systems fall sort of this requirement. Part of the reason for this is that in many types of decision support systems it is often extremely difficult, if not impossible, to produce explanations. This is particularly the case for black box systems such as support vector machines. However in other systems such as rule-based systems, where the explanation can be in the form of a rule, the explanations can often be complicated and result in confusion for users.
Alternatively Case-based Reasoning (CBR) Systems lend themselves naturally to producing
explanations. As the reasoning in CBR systems is performed on the most similar past case(s) to a current problem, these similar cases can be used as an explanation for a classification. As these similar cases are real past problems, they are generally easily understood by users.
It is our believe however, that in CBR systems, that there are more suitable cases to use as
an explanation than simply using the most similar cases. It is our belief that these more suitable cases lie between the problem case and the perceived decision boundary. This results in the cases forming an a fortiori argument. We describe a framework that we have developed for selecting such cases.
We also believe that it is often not enough, regardless of the suitability, to just use a case as
an explanation. In our framework we included a mechanism for generating explanatory text that
can express why the case is suitable, or in some situations aspects of the case that may not be
suitable. This explanatory text can further assist users to decide if they agree with the opinion of CBR system.
Based on the developed framework we implemented a decision support system for use in the
domain of Bronchiolitis, a viral infection that effects young children. This system was used and
evaluated in the Kern Medical Center, Bakersfield, California during their Bronchiolitis season.
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