1,289 research outputs found
A Logic-based Multi-agent System for Ethical Monitoring and Evaluation of Dialogues
In Proceedings ICLP 2021, arXiv:2109.0791
Logic Programming and Machine Ethics
Transparency is a key requirement for ethical machines. Verified ethical
behavior is not enough to establish justified trust in autonomous intelligent
agents: it needs to be supported by the ability to explain decisions. Logic
Programming (LP) has a great potential for developing such perspective ethical
systems, as in fact logic rules are easily comprehensible by humans.
Furthermore, LP is able to model causality, which is crucial for ethical
decision making.Comment: In Proceedings ICLP 2020, arXiv:2009.09158. Invited paper for the
ICLP2020 Panel on "Machine Ethics". arXiv admin note: text overlap with
arXiv:1909.0825
Explanatory machine learning for sequential human teaching
The topic of comprehensibility of machine-learned theories has recently drawn
increasing attention. Inductive Logic Programming (ILP) uses logic programming
to derive logic theories from small data based on abduction and induction
techniques. Learned theories are represented in the form of rules as
declarative descriptions of obtained knowledge. In earlier work, the authors
provided the first evidence of a measurable increase in human comprehension
based on machine-learned logic rules for simple classification tasks. In a
later study, it was found that the presentation of machine-learned explanations
to humans can produce both beneficial and harmful effects in the context of
game learning. We continue our investigation of comprehensibility by examining
the effects of the ordering of concept presentations on human comprehension. In
this work, we examine the explanatory effects of curriculum order and the
presence of machine-learned explanations for sequential problem-solving. We
show that 1) there exist tasks A and B such that learning A before B has a
better human comprehension with respect to learning B before A and 2) there
exist tasks A and B such that the presence of explanations when learning A
contributes to improved human comprehension when subsequently learning B. We
propose a framework for the effects of sequential teaching on comprehension
based on an existing definition of comprehensibility and provide evidence for
support from data collected in human trials. Empirical results show that
sequential teaching of concepts with increasing complexity a) has a beneficial
effect on human comprehension and b) leads to human re-discovery of
divide-and-conquer problem-solving strategies, and c) studying machine-learned
explanations allows adaptations of human problem-solving strategy with better
performance.Comment: Submitted to the International Joint Conference on Learning &
Reasoning (IJCLR) 202
Explainable and Ethical AI: A Perspective on Argumentation and Logic Programming
In this paper we sketch a vision of explainability of intelligent systems as a logic approach suitable to be injected into and exploited by the system actors once integrated with sub-symbolic techniques.
In particular, we show how argumentation could be combined with different extensions of logic programming – namely, abduction, inductive logic programming, and probabilistic logic programming – to address the issues of explainable AI as well as some ethical concerns about AI
Argumentation and Logic Programming for Explainable and Ethical AI
In this paper we sketch a vision of explainability of intelligent systems as a logic approach suitable to be injected into and exploited by the system actors once integrated with sub-symbolic techniques. In particular, we show how argumentation could be combined with different extensions of logic programming – namely, abduction, inductive logic programming, and probabilistic logic programming – to address the issues of explainable AI as well as to address some ethical concerns about AI
Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning
Current advances in Artificial Intelligence and machine learning in general, and deep learning in particular have reached unprecedented impact not only across research communities, but also over popular media channels. However, concerns about interpretability and accountability of AI have been raised by influential thinkers. In spite of the recent impact of AI, several works have identified the need for principled knowledge representation and reasoning mechanisms integrated with deep learning-based systems to provide sound and explainable models for such systems. Neural-symbolic computing aims at integrating, as foreseen by Valiant, two most fundamental cognitive abilities: the ability to learn from the environment, and the ability to reason from what has been learned. Neural-symbolic computing has been an active topic of research for many years, reconciling the advantages of robust learning in neural networks and reasoning and interpretability of symbolic representation. In this paper, we survey recent accomplishments of neural-symbolic computing as a principled methodology for integrated machine learning and reasoning. We illustrate the effectiveness of the approach by outlining the main characteristics of the methodology: principled integration of neural learning with symbolic knowledge representation and reasoning allowing for the construction of explainable AI systems. The insights provided by neural-symbolic computing shed new light on the increasingly prominent need for interpretable and accountable AI systems
Symbolic AI for XAI: Evaluating LFIT inductive programming for explaining biases in machine learning
Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods become crucial. Inductive logic programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the processing of data. Learning from interpretation transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains. In order to check the ability to cope with other domains no matter the machine learning paradigm used, we have done a preliminary test of the expressiveness of LFIT, feeding it with a real dataset about adult incomes taken from the US census, in which we consider the income level as a function of the rest of attributes to verify if LFIT can provide logical theory to support and explain to what extent higher incomes are biased by gender and ethnicityThis work was supported by projects: PRIMA (H2020-MSCA-ITN-2019-860315),
TRESPASS-ETN(H2020-MSCA-ITN-2019-860813), IDEA-FAST (IMI2-2018-15-853981), BIBECA(RTI2018-101248-B-I00MINECO/FEDER), RTI2018-095232-B-C22MINECO, PLeNTaS project PID2019-111430RBI00MINECO; and also by Pays de la Loire Region through RFI Atlanstic 202
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