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Explainable Agents and Robots: Results from a Systematic Literature Review
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (XAI)ORCID-id: 0000-0002-1232-346X
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (XAI)
(HES-SO)
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (XAI)ORCID-id: 0000-0002-8078-5172
2019 (engelsk)Inngår i: AAMAS '19: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems / [ed] N. Agmon, M. E. Taylor, E. Elkind, M. Veloso, International Foundation for Autonomous Agents and MultiAgent Systems , 2019, s. 1078-1088Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Humans are increasingly relying on complex systems that heavily adopts Artificial Intelligence (AI) techniques. Such systems are employed in a growing number of domains, and making them explainable is an impelling priority. Recently, the domain of eXplainable Artificial Intelligence (XAI) emerged with the aims of fostering transparency and trustworthiness. Several reviews have been conducted. Nevertheless, most of them deal with data-driven XAI to overcome the opaqueness of black-box algorithms. Contributions addressing goal-driven XAI (e.g., explainable agency for robots and agents) are still missing. This paper aims at filling this gap, proposing a Systematic Literature Review. The main findings are (i) a considerable portion of the papers propose conceptual studies, or lack evaluations or tackle relatively simple scenarios; (ii) almost all of the studied papers deal with robots/agents explaining their behaviors to the human users, and very few works addressed inter-robot (inter-agent) explainability. Finally, (iii) while providing explanations to non-expert users has been outlined as a necessity, only a few works addressed the issues of personalization and context-awareness

sted, utgiver, år, opplag, sider
International Foundation for Autonomous Agents and MultiAgent Systems , 2019. s. 1078-1088
Serie
Proceedings, ISSN 2523-5699
Emneord [en]
Explainable AI, goal-based XAI, autonomous agents, human-robot interaction
HSV kategori
Identifikatorer
URN: urn:nbn:se:umu:diva-158024ISI: 000474345000124ISBN: 978-1-4503-6309-9 (tryckt)OAI: oai:DiVA.org:umu-158024DiVA, id: diva2:1303810
Konferanse
18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), Montreal, Canada, May 13–17, 2019
Tilgjengelig fra: 2019-04-10 Laget: 2019-04-10 Sist oppdatert: 2022-08-29bibliografisk kontrollert
Inngår i avhandling
1. Context-based explanations for machine learning predictions
Åpne denne publikasjonen i ny fane eller vindu >>Context-based explanations for machine learning predictions
2022 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Alternativ tittel[sv]
Kontextbaserade förklaringar för maskininlärningsförutsägelser
Abstract [en]

In recent years, growing concern regarding trust in algorithmic decision-making has drawn attention to more transparent and interpretable models. Laws and regulations are moving towards requiring this functionality from information systems to prevent unintended side effects. Such as the European Union's General Data Protection Regulations (GDPR) set out the right to be informed regarding machine-generated decisions. Individuals affected by these decisions can question, confront and challenge the inferences automatically produced by machine learning models. Consequently, such matters necessitate AI systems to be transparent and explainable for various practical applications.

Furthermore, explanations help evaluate these systems' strengths and limitations, thereby fostering trustworthiness. As important as it is, existing studies mainly focus on creating mathematically interpretable models or explaining black-box algorithms with intrinsically interpretable surrogate models. In general, these explanations are intended for technical users to evaluate the correctness of a model and are often hard to interpret by general users.  

Given a critical need for methods that consider end-user requirements, this thesis focuses on generating intelligible explanations for predictions made by machine learning algorithms. As a starting point, we present the outcome of a systematic literature review of the existing research on generating and communicating explanations in goal-driven eXplainable AI (XAI), such as agents and robots. These are known for their ability to communicate their decisions in human understandable terms. Influenced by that, we discuss the design and evaluation of our proposed explanation methods for black-box algorithms in different machine learning applications, including image recognition, scene classification, and disease prediction.

Taken together, the methods and tools presented in this thesis could be used to explain machine learning predictions or as a baseline to compare to other explanation techniques, enabling interpretation indicators for experts and non-technical users. The findings would also be of interest to domains using machine learning models for high-stake decision-making to investigate the practical utility of proposed explanation methods.

sted, utgiver, år, opplag, sider
Umeå: Umeå University, 2022. s. 48
Serie
Report / UMINF, ISSN 0348-0542
Emneord
Explainable AI, explainability, interpretability, black-box models, deep learning, neural networks, contextual importance
HSV kategori
Forskningsprogram
datalogi
Identifikatorer
urn:nbn:se:umu:diva-198943 (URN)978-91-7855-859-9 (ISBN)978-91-7855-860-5 (ISBN)
Disputas
2022-09-26, NAT.D.320, Naturvetarhuset, Umeå, 08:30 (engelsk)
Opponent
Veileder
Forskningsfinansiär
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Tilgjengelig fra: 2022-09-05 Laget: 2022-08-29 Sist oppdatert: 2022-08-30bibliografisk kontrollert

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