43,720 research outputs found
Explainable Empirical Risk Minimization
The widespread use of modern machine learning methods in decision making
crucially depends on their interpretability or explainability. The human users
(decision makers) of machine learning methods are often not only interested in
getting accurate predictions or projections. Rather, as a decision-maker, the
user also needs a convincing answer (or explanation) to the question of why a
particular prediction was delivered. Explainable machine learning might be a
legal requirement when used for decision making with an immediate effect on the
health of human beings. As an example consider the computer vision of a
self-driving car whose predictions are used to decide if to stop the car. We
have recently proposed an information-theoretic approach to construct
personalized explanations for predictions obtained from ML. This method was
model-agnostic and only required some training samples of the model to be
explained along with a user feedback signal. This paper uses an
information-theoretic measure for the quality of an explanation to learn
predictors that are intrinsically explainable to a specific user. Our approach
is not restricted to a particular hypothesis space, such as linear maps or
shallow decision trees, whose predictor maps are considered as explainable by
definition. Rather, we regularize an arbitrary hypothesis space using a
personalized measure for the explainability of a particular predictor
Personalized Explanations
Machine learning systems are often hard to investigate and intransparent in their decision making . Explainable Artificial Intelligence (XAI) tries to make these systems more transparent. However, most work in the field focuses on technical aspects like maximizing metrics. The human aspects of explainability are often neglected. In this work, we present personalized explanations, which instead focus on the user. Personalized explanations can be adapted to individual users to be as useful and relevant as possible. They can be interacted with to give users the ability to engage in an explanatory dialog with the system. Finally, they should also protect user data to increase the trust in the explanation system
Knowledge Graph semantic enhancement of input data for improving AI
Intelligent systems designed using machine learning algorithms require a
large number of labeled data. Background knowledge provides complementary, real
world factual information that can augment the limited labeled data to train a
machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for
many practical applications, it is convenient and useful to organize this
background knowledge in the form of a graph. Recent academic research and
implemented industrial intelligent systems have shown promising performance for
machine learning algorithms that combine training data with a knowledge graph.
In this article, we discuss the use of relevant KGs to enhance input data for
two applications that use machine learning -- recommendation and community
detection. The KG improves both accuracy and explainability
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