2,298 research outputs found
Local Rule-Based Explanations of Black Box Decision Systems
The recent years have witnessed the rise of accurate but obscure decision
systems which hide the logic of their internal decision processes to the users.
The lack of explanations for the decisions of black box systems is a key
ethical issue, and a limitation to the adoption of machine learning components
in socially sensitive and safety-critical contexts. %Therefore, we need
explanations that reveals the reasons why a predictor takes a certain decision.
In this paper we focus on the problem of black box outcome explanation, i.e.,
explaining the reasons of the decision taken on a specific instance. We propose
LORE, an agnostic method able to provide interpretable and faithful
explanations. LORE first leans a local interpretable predictor on a synthetic
neighborhood generated by a genetic algorithm. Then it derives from the logic
of the local interpretable predictor a meaningful explanation consisting of: a
decision rule, which explains the reasons of the decision; and a set of
counterfactual rules, suggesting the changes in the instance's features that
lead to a different outcome. Wide experiments show that LORE outperforms
existing methods and baselines both in the quality of explanations and in the
accuracy in mimicking the black box
Meaningful Explanations of Black Box AI Decision Systems
Black box AI systems for automated decision making, often based on machine learning over (big) data, map a user's features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We focus on the urgent open challenge of how to construct meaningful explanations of opaque AI/ML systems, introducing the local-to-global framework for black box explanation, articulated along three lines: (i) the language for expressing explanations in terms of logic rules, with statistical and causal interpretation; (ii) the inference of local explanations for revealing the decision rationale for a specific case, by auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of many local explanations into simple global ones, with algorithms that optimize for quality and comprehensibility. We argue that the local-first approach opens the door to a wide variety of alternative solutions along different dimensions: a variety of data sources (relational, text, images, etc.), a variety of learning problems (multi-label classification, regression, scoring, ranking), a variety of languages for expressing meaningful explanations, a variety of means to audit a black box
Machine Learning and other Computational-Intelligence Techniques for Security Applications
L'abstract è presente nell'allegato / the abstract is in the attachmen
Toward enhancement of deep learning techniques using fuzzy logic: a survey
Deep learning has emerged recently as a type of artificial intelligence (AI) and machine learning (ML), it usually imitates the human way in gaining a particular knowledge type. Deep learning is considered an essential data science element, which comprises predictive modeling and statistics. Deep learning makes the processes of collecting, interpreting, and analyzing big data easier and faster. Deep neural networks are kind of ML models, where the non-linear processing units are layered for the purpose of extracting particular features from the inputs. Actually, the training process of similar networks is very expensive and it also depends on the used optimization method, hence optimal results may not be provided. The techniques of deep learning are also vulnerable to data noise. For these reasons, fuzzy systems are used to improve the performance of deep learning algorithms, especially in combination with neural networks. Fuzzy systems are used to improve the representation accuracy of deep learning models. This survey paper reviews some of the deep learning based fuzzy logic models and techniques that were presented and proposed in the previous studies, where fuzzy logic is used to improve deep learning performance. The approaches are divided into two categories based on how both of the samples are combined. Furthermore, the models' practicality in the actual world is revealed
Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives
Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future
A Survey Of Methods For Explaining Black Box Models
In the last years many accurate decision support systems have been
constructed as black boxes, that is as systems that hide their internal logic
to the user. This lack of explanation constitutes both a practical and an
ethical issue. The literature reports many approaches aimed at overcoming this
crucial weakness sometimes at the cost of scarifying accuracy for
interpretability. The applications in which black box decision systems can be
used are various, and each approach is typically developed to provide a
solution for a specific problem and, as a consequence, delineating explicitly
or implicitly its own definition of interpretability and explanation. The aim
of this paper is to provide a classification of the main problems addressed in
the literature with respect to the notion of explanation and the type of black
box system. Given a problem definition, a black box type, and a desired
explanation this survey should help the researcher to find the proposals more
useful for his own work. The proposed classification of approaches to open
black box models should also be useful for putting the many research open
questions in perspective.Comment: This work is currently under review on an international journa
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