On Specifying for Trustworthiness

no code implementations • 22 Jun 2022 • Dhaminda B. Abeywickrama, Amel Bennaceur, Greg Chance, Yiannis Demiris, Anastasia Kordoni, Mark Levine, Luke Moffat, Luc Moreau, Mohammad Reza Mousavi, Bashar Nuseibeh, Subramanian Ramamoorthy, Jan Oliver Ringert, James Wilson, Shane Windsor, Kerstin Eder

As autonomous systems (AS) increasingly become part of our daily lives, ensuring their trustworthiness is crucial.

A Methodology and Software Architecture to Support Explainability-by-Design

1 code implementation • 13 Jun 2022 • Trung Dong Huynh, Niko Tsakalakis, Ayah Helal, Sophie Stalla-Bourdillon, Luc Moreau

The methodology consists of three phases: (A) Explanation Requirement Analysis, (B) Explanation Technical Design, and (C) Explanation Validation.

Decision Making Sentence

A taxonomy of explanations to support Explainability-by-Design

no code implementations • 9 Jun 2022 • Niko Tsakalakis, Sophie Stalla-Bourdillon, Trung Dong Huynh, Luc Moreau

As automated decision-making solutions are increasingly applied to all aspects of everyday life, capabilities to generate meaningful explanations for a variety of stakeholders (i. e., decision-makers, recipients of decisions, auditors, regulators...) become crucial.

Decision Making

Provenance Graph Kernel

1 code implementation • 20 Oct 2020 • David Kohan Marzagão, Trung Dong Huynh, Ayah Helal, Sean Baccas, Luc Moreau

Provenance is a record that describes how entities, activities, and agents have influenced a piece of data; it is commonly represented as graphs with relevant labels on both their nodes and edges.