Timo Speith

About Timo Speith

Timo Speith, With an exceptional h-index of 11 and a recent h-index of 11 (since 2020), a distinguished researcher at Universität des Saarlandes, specializes in the field of Computer Ethics, Machine Ethics, Machine Explainability, Explainable Artificial Intelligence, XAI.

His recent articles reflect a diverse array of research interests and contributions to the field:

Unlocking the Potential of Machine Ethics with Explainability

Mapping the Potential of Explainable Artificial Intelligence (XAI) for Fairness Along the AI Lifecycle

Explainable artificial intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

Welcome to the Third International Workshop on Requirements Engineering for Explainable Systems (RE4ES)

Sources of opacity in computer systems: Towards a comprehensive taxonomy

A new perspective on evaluation methods for explainable artificial intelligence (XAI)

Revisiting the performance-explainability trade-off in explainable artificial intelligence (XAI)

Expanding explainability: from explainable artificial intelligence to explainable hardware

Timo Speith Information

University

Position

___

Citations(all)

805

Citations(since 2020)

804

Cited By

21

hIndex(all)

11

hIndex(since 2020)

11

i10Index(all)

11

i10Index(since 2020)

11

Email

University Profile Page

Google Scholar

Timo Speith Skills & Research Interests

Computer Ethics

Machine Ethics

Machine Explainability

Explainable Artificial Intelligence

XAI

Top articles of Timo Speith

Unlocking the Potential of Machine Ethics with Explainability

2024/5/6

Timo Speith
Timo Speith

H-Index: 2

Mapping the Potential of Explainable Artificial Intelligence (XAI) for Fairness Along the AI Lifecycle

arXiv preprint arXiv:2404.18736

2024/4/29

Welcome to the Third International Workshop on Requirements Engineering for Explainable Systems (RE4ES)

2023/9/4

Verena Klös
Verena Klös

H-Index: 6

Timo Speith
Timo Speith

H-Index: 2

Sources of opacity in computer systems: Towards a comprehensive taxonomy

2023/9/4

Timo Speith
Timo Speith

H-Index: 2

A new perspective on evaluation methods for explainable artificial intelligence (XAI)

2023/9/4

Timo Speith
Timo Speith

H-Index: 2

Markus Langer
Markus Langer

H-Index: 8

Revisiting the performance-explainability trade-off in explainable artificial intelligence (XAI)

2023/9/4

Timo Speith
Timo Speith

H-Index: 2

Expanding explainability: from explainable artificial intelligence to explainable hardware

arXiv preprint arXiv:2302.14661

2023/2/28

Building bridges for better machines: from machine ethics to machine explainability and back

2023

Timo Speith
Timo Speith

H-Index: 2

Explainable software systems: from requirements analysis to system evaluation

Requirements Engineering

2022/12

Larissa Chazette
Larissa Chazette

H-Index: 3

Timo Speith
Timo Speith

H-Index: 2

How to Evaluate Explainability?–A Case for Three Criteria

2022/8/15

Timo Speith
Timo Speith

H-Index: 2

A review of taxonomies of explainable artificial intelligence (XAI) methods

2022/6/21

Timo Speith
Timo Speith

H-Index: 2

Quo vadis, explainability?–A research roadmap for explainability engineering

2022/3/9

From Responsibility to Reason-Giving Explainable Artificial Intelligence

Philosophy and Technology

2022/3

Kevin Baum
Kevin Baum

H-Index: 4

Timo Speith
Timo Speith

H-Index: 2

Explainability auditing for intelligent systems: a rationale for multi-disciplinary perspectives

2021/9/20

On the relation of trust and explainability: Why to engineer for trustworthiness

2021/9/20

Markus Langer
Markus Langer

H-Index: 8

Timo Speith
Timo Speith

H-Index: 2

Exploring explainability: a definition, a model, and a knowledge catalogue

2021/9/20

Larissa Chazette
Larissa Chazette

H-Index: 3

Timo Speith
Timo Speith

H-Index: 2

Welcome to the first international workshop on requirements engineering for explainable systems (RE4ES)

2021/9/1

Spare me the details: How the type of information about automated interviews influences applicant reactions

International Journal of Selection and Assessment

2021

What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research

Artificial Intelligence

2021/7/1

See List of Professors in Timo Speith University(Universität des Saarlandes)

Co-Authors

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