The dual function of explanations: Why it is useful to compute explanations

Tsakalakis, Nikolaos; Stalla-Bourdillon, Sophie; Carmichael, Laura; Huynh, Trung Dong; Moreau, Luc; Helal, Abdelsalam

doi:10.1016/j.clsr.2020.105527

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…A case study addressing regulatory requirements on explainability of automated decisions in the context of a loan application [38] provided foundations for a systematic approach. Within this context, explanations can act as external detective controls, as they provide specific information to justify the decision reached and help the user take corrective actions [39]. But explanations can also act as internal detective controls, i.e.…”

Section: • How Can Explainability By Design Contribute To Ass Specifi...mentioning

confidence: 99%

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On Specifying for Trustworthiness

Abeywickrama¹,

Bennaceur²,

Chance³

et al. 2022

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As autonomous systems are becoming part of our daily lives, ensuring their trustworthiness is crucial. There are a number of techniques for demonstrating trustworthiness. Common to all these techniques is the need to articulate specifications. In this paper, we take a broad view of specification, concentrating on top-level requirements including but not limited to functionality, safety, security and other non-functional properties. The main contribution of this article is a set of high-level intellectual challenges for the autonomous systems community related to specifying for trustworthiness. We also describe unique specification challenges concerning a number of application domains for autonomous systems.

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Section: • How Can Explainability By Design Contribute To Ass Specifi...mentioning

confidence: 99%

“…a mechanism for organisations to demonstrate compliance to the regulatory frameworks they have to implement. Both [38] and [39] provide systematic questions that an explanation designer needs to consider: What is the purpose of an explanation? What is the audience of an explanation?…”

Section: • How Can Explainability By Design Contribute To Ass Specifi...mentioning

confidence: 99%

On Specifying for Trustworthiness

Abeywickrama¹,

Bennaceur²,

Chance³

et al. 2022

Preprint

Self Cite

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show abstract

“…Indeed, while operating with various degrees of autonomy, multi-agent systems make decisions that may affect humans. Such systems may therefore be subject to laws and regulations, such as the potential requirement of explainability in the GDPR [47,91,93]. Independently of regulatory needs, there is an increasing desire to be transparent about decisions that affect people, and society demands accountability for outcomes resulting from (semi-) automated processing.…”

Section: Other Areas Of Multi-agent Systems Researchmentioning

confidence: 99%

Reasoning and interaction for social artificial intelligence

Black

Brandão

Cocarascu

et al. 2022

AIC

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Current work on multi-agent systems at King’s College London is extensive, though largely based in two research groups within the Department of Informatics: the Distributed Artificial Intelligence (DAI) thematic group and the Reasoning & Planning (RAP) thematic group. DAI combines AI expertise with political and economic theories and data, to explore social and technological contexts of interacting intelligent entities. It develops computational models for analysing social, political and economic phenomena to improve the effectiveness and fairness of policies and regulations, and combines intelligent agent systems, software engineering, norms, trust and reputation, agent-based simulation, communication and provenance of data, knowledge engineering, crowd computing and semantic technologies, and algorithmic game theory and computational social choice, to address problems arising in autonomous systems, financial markets, privacy and security, urban living and health. RAP conducts research in symbolic models for reasoning involving argumentation, knowledge representation, planning, and other related areas, including development of logical models of argumentation-based reasoning and decision-making, and their usage for explainable AI and integration of machine and human reasoning, as well as combining planning and argumentation methodologies for strategic argumentation.

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