119 research outputs found
Explaining Factor Ascription
Explanation and justification of legal decisions has become a highly relevant topic in light of the explosion of interest in the use of machine learning (ML) approaches to predict legal decisions. Current suggestions are to use the established factor based explanations developed in AI and Law as the basis for explaining such programs. We, however, identify factor ascription as an important aspect of explanation of case outcomes not currently covered, and argue that explanations must also include this aspect. Finally, we outline our proposal for a hybrid system approach that combines ML and Abstract Dialectical Framework (ADF) layers to engender an explainable process.</jats:p
On the Complexity of Determining Defeat Relations Consistent with Abstract Argumentation Semantics
Typically in abstract argumentation, one starts with arguments and a defeat relation, and applies some semantics in order to determine the acceptability status of the arguments. We consider the converse case where we have knowledge of the acceptability status of arguments and want to identify a defeat relation that is consistent with the known acceptability data – the σ-consistency problem. Focusing on complete semantics as underpinning the majority of the major semantic types, we show that the complexity of determining a defeat relation that is consistent with some set of acceptability data is highly dependent on how the data is labelled. The extension-based 2-valued σ-consistency problem for complete semantics is revealed as NP-complete, whereas the labelling-based 3-valued σ-consistency problem is solvable within polynomial time. We then present an informal discussion on application to grounded, stable, and preferred semantics.</jats:p
The SunPy Project: An Interoperable Ecosystem for Solar Data Analysis
The SunPy Project is a community of scientists and software developers
creating an ecosystem of Python packages for solar physics. The project
includes the sunpy core package as well as a set of affiliated packages. The
sunpy core package provides general purpose tools to access data from different
providers, read image and time series data, and transform between commonly used
coordinate systems. Affiliated packages perform more specialized tasks that do
not fall within the more general scope of the sunpy core package. In this
article, we give a high-level overview of the SunPy Project, how it is broader
than the sunpy core package, and how the project curates and fosters the
affiliated package system. We demonstrate how components of the SunPy
ecosystem, including sunpy and several affiliated packages, work together to
enable multi-instrument data analysis workflows. We also describe members of
the SunPy Project and how the project interacts with the wider solar physics
and scientific Python communities. Finally, we discuss the future direction and
priorities of the SunPy Project.Comment: 15 pages, 1 figure, published in Frontier
Everyday Argumentative Explanations for Classification
In this paper we study everyday explanations for classification tasks with formal argumentation. Everyday explanations describe how humans explain in day-to-day life, which is important when explaining decisions of AI systems to lay users. We introduce EVAX, a model-agnostic explanation method for classifiers with which contrastive, selected and social explanations can be generated. The resulting explanations can be adjusted in their size and retain high fidelity scores (an average of 0.95
Online Handbook of Argumentation for AI: Volume 1
This volume contains revised versions of the papers selected for the first
volume of the Online Handbook of Argumentation for AI (OHAAI). Previously,
formal theories of argument and argument interaction have been proposed and
studied, and this has led to the more recent study of computational models of
argument. Argumentation, as a field within artificial intelligence (AI), is
highly relevant for researchers interested in symbolic representations of
knowledge and defeasible reasoning. The purpose of this handbook is to provide
an open access and curated anthology for the argumentation research community.
OHAAI is designed to serve as a research hub to keep track of the latest and
upcoming PhD-driven research on the theory and application of argumentation in
all areas related to AI.Comment: editor: Federico Castagna and Francesca Mosca and Jack Mumford and
Stefan Sarkadi and Andreas Xydi
A Survey of Computational Tools in Solar Physics
The SunPy Project developed a 13-question survey to understand the software
and hardware usage of the solar physics community. 364 members of the solar
physics community, across 35 countries, responded to our survey. We found that
990.5% of respondents use software in their research and 66% use the
Python scientific software stack. Students are twice as likely as faculty,
staff scientists, and researchers to use Python rather than Interactive Data
Language (IDL). In this respect, the astrophysics and solar physics communities
differ widely: 78% of solar physics faculty, staff scientists, and researchers
in our sample uses IDL, compared with 44% of astrophysics faculty and
scientists sampled by Momcheva and Tollerud (2015). 634% of respondents
have not taken any computer-science courses at an undergraduate or graduate
level. We also found that most respondents utilize consumer hardware to run
software for solar-physics research. Although 82% of respondents work with data
from space-based or ground-based missions, some of which (e.g. the Solar
Dynamics Observatory and Daniel K. Inouye Solar Telescope) produce terabytes of
data a day, 14% use a regional or national cluster, 5% use a commercial cloud
provider, and 29% use exclusively a laptop or desktop. Finally, we found that
734% of respondents cite scientific software in their research, although
only 423% do so routinely
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