How Can We Develop Explainable Systems? Insights from a Literature Review and an Interview Study

Author:

Chazette Larissa1,Klünder Jil1,Balci Merve2,Schneider Kurt1

Affiliation:

1. Software Engineering Group, Leibniz University Hannover, Germany

2. Leibniz University Hannover, Germany

Publisher

ACM

Reference72 articles.

1. How do software architects consider non-functional requirements: An exploratory study

2. Josh Andres , Christine  T. Wolf , Sergio Cabrero Barros , Erick Oduor , Rahul Nair , Alexander Kjærum , Anders Bech Tharsgaard , and Bo Schwartz Madsen . 2020 . Scenario-Based XAI for Humanitarian Aid Forecasting. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems ( Honolulu, HI, USA) (CHI EA ’20). Association for Computing Machinery, New York, NY, USA, 1–8. https://doi.org/10.1145/3334480.3382903 10.1145/3334480.3382903 Josh Andres, Christine T. Wolf, Sergio Cabrero Barros, Erick Oduor, Rahul Nair, Alexander Kjærum, Anders Bech Tharsgaard, and Bo Schwartz Madsen. 2020. Scenario-Based XAI for Humanitarian Aid Forecasting. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI EA ’20). Association for Computing Machinery, New York, NY, USA, 1–8. https://doi.org/10.1145/3334480.3382903

3. Oren Barkan , Yonatan Fuchs , Avi Caciularu , and Noam Koenigstein . 2020. Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering . Association for Computing Machinery , New York, NY, USA , 468–473. https://doi.org/10.1145/3383313.3412226 10.1145/3383313.3412226 Oren Barkan, Yonatan Fuchs, Avi Caciularu, and Noam Koenigstein. 2020. Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering. Association for Computing Machinery, New York, NY, USA, 468–473. https://doi.org/10.1145/3383313.3412226

4. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

5. Software fairness

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1. Explanations in Everyday Software Systems: Towards a Taxonomy for Explainability Needs;2024 IEEE 32nd International Requirements Engineering Conference (RE);2024-06-24

2. Candidate Solutions for Defining Explainability Requirements of AI Systems;Lecture Notes in Computer Science;2024

3. Do stakeholder needs differ? - Designing stakeholder-tailored Explainable Artificial Intelligence (XAI) interfaces;International Journal of Human-Computer Studies;2024-01

4. Explanations on Demand - a Technique for Eliciting the Actual Need for Explanations;2023 IEEE 31st International Requirements Engineering Conference Workshops (REW);2023-09

5. Designing End-User Personas for Explainability Requirements Using Mixed Methods Research;2023 IEEE 31st International Requirements Engineering Conference Workshops (REW);2023-09

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