Timo Speith

1.7k total citations · 3 hit papers
17 papers, 866 citations indexed

About

Timo Speith is a scholar working on Artificial Intelligence, Safety Research and Information Systems. According to data from OpenAlex, Timo Speith has authored 17 papers receiving a total of 866 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 9 papers in Safety Research and 6 papers in Information Systems. Recurrent topics in Timo Speith's work include Explainable Artificial Intelligence (XAI) (13 papers), Ethics and Social Impacts of AI (8 papers) and Software Engineering Research (6 papers). Timo Speith is often cited by papers focused on Explainable Artificial Intelligence (XAI) (13 papers), Ethics and Social Impacts of AI (8 papers) and Software Engineering Research (6 papers). Timo Speith collaborates with scholars based in Germany, Spain and Italy. Timo Speith's co-authors include Kevin Baum, Markus Langer, Daniel Oster, Eva Schmidt, Holger Hermanns, Lena Kästner, Andreas Sesing-Wagenpfeil, Richard Jiang, Mario Brčić and Roberto Confalonieri and has published in prestigious journals such as Artificial Intelligence, Information Fusion and Ethics and Information Technology.

In The Last Decade

Timo Speith

17 papers receiving 841 citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

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

2021 305 citations Markus Langer, Daniel Oster et al. Artificial Intelligence profile →
A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

2022 172 citations Timo Speith Publications of the UdS (Saarland University) profile →
Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

2024 172 citations Luca Longo, Mario Brčić et al. Information Fusion profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Timo Speith	584	226	176	81	57	17	866
Ashraf Abdul	657 1.1×	304 1.3×	173 1.0×	99 1.2×	90 1.6×	7	956
Gagan Bansal	471 0.8×	239 1.1×	111 0.6×	62 0.8×	44 0.8×	23	770
Fernando Martínez‐Plumed	331 0.6×	89 0.4×	70 0.4×	104 1.3×	42 0.7×	35	807
Shivani Kapania	233 0.4×	172 0.8×	61 0.3×	77 1.0×	45 0.8×	14	610
Tianyu Wu	347 0.6×	61 0.3×	242 1.4×	76 0.9×	28 0.5×	8	820
Manjeet Singh	272 0.5×	253 1.1×	79 0.4×	92 1.1×	17 0.3×	34	804
Nikica Hlupić	454 0.8×	70 0.3×	80 0.5×	61 0.8×	17 0.3×	17	709
Süleyman Uslu	196 0.3×	104 0.5×	76 0.4×	67 0.8×	20 0.4×	10	439
Davinder Kaur	183 0.3×	104 0.5×	76 0.4×	57 0.7×	19 0.3×	8	401
Enis Karaarslan	279 0.5×	67 0.3×	278 1.6×	176 2.2×	11 0.2×	37	744

Countries citing papers authored by Timo Speith

Since Specialization

Citations

This map shows the geographic impact of Timo Speith's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Timo Speith with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Timo Speith more than expected).

Fields of papers citing papers by Timo Speith

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Timo Speith. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Timo Speith. The network helps show where Timo Speith may publish in the future.

Co-authorship network of co-authors of Timo Speith

This figure shows the co-authorship network connecting the top 25 collaborators of Timo Speith. A scholar is included among the top collaborators of Timo Speith based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Timo Speith. Timo Speith is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

Speith, Timo, et al.. (2024). Explainability as a Requirement for Hardware: Introducing Explainable Hardware (XHW). 354–362. 2 indexed citations

Speith, Timo, et al.. (2024). Conceptualizing understanding in explainable artificial intelligence (XAI): an abilities-based approach. Ethics and Information Technology. 26(2). 6 indexed citations

Schneider, Kurt, et al.. (2024). The X Factor: On the Relationship between User eXperience and eXplainability. 1–12. 5 indexed citations

Longo, Luca, Mario Brčić, Federico Cabitza, et al.. (2024). Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions. Information Fusion. 106. 102301–102301. 172 indexed citations breakdown →

Kästner, Lena, et al.. (2023). Sources of Opacity in Computer Systems: Towards a Comprehensive Taxonomy. 337–342. 5 indexed citations

Speith, Timo, et al.. (2023). Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI). 316–324. 16 indexed citations

Speith, Timo & Markus Langer. (2023). A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI). FreiDok plus (Universitätsbibliothek Freiburg). 325–331. 9 indexed citations

Speith, Timo, et al.. (2023). Welcome to the Third International Workshop on Requirements Engineering for Explainable Systems (RE4ES). 307–308. 1 indexed citations

Chazette, Larissa, et al.. (2022). Explainable software systems: from requirements analysis to system evaluation. Requirements Engineering. 27(4). 457–487. 13 indexed citations

10.

Speith, Timo. (2022). How to Evaluate Explainability? - A Case for Three Criteria. 92–97. 7 indexed citations

11.

Baum, Kevin, et al.. (2022). From Responsibility to Reason-Giving Explainable Artificial Intelligence. Philosophy & Technology. 35(1). 55 indexed citations

12.

Speith, Timo. (2022). A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods. Publications of the UdS (Saarland University). 2239–2250. 172 indexed citations breakdown →

13.

Langer, Markus, Daniel Oster, Timo Speith, et al.. (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. 296. 103473–103473. 305 indexed citations breakdown →

14.

Chazette, Larissa, et al.. (2021). Welcome to the First International Workshop on Requirements Engineering for Explainable Systems (RE4ES). Chalmers Research (Chalmers University of Technology). 157–158. 2 indexed citations

15.

Langer, Markus, et al.. (2021). Spare me the details: How the type of information about automated interviews influences applicant reactions. International Journal of Selection and Assessment. 29(2). 154–169. 27 indexed citations

16.

Baum, Kevin, et al.. (2019). Explainability as a Non-Functional Requirement. FreiDok plus (Universitätsbibliothek Freiburg). 363–368. 64 indexed citations

17.

Baum, Kevin, Holger Hermanns, & Timo Speith. (2018). From Machine Ethics To Machine Explainability and Back.. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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