Marc P. Hauer

Orcid: 0000-0002-1598-1812

According to our database1, Marc P. Hauer authored at least 16 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Black-Box Testing and Auditing of Bias in ADM Systems.
Minds Mach., June, 2024

Pragmatic auditing: a pilot-driven approach for auditing Machine Learning systems.
CoRR, 2024

Are We Still on Track with Our Responsibility Strategy? Introducing an Internal Assessment of Corporate Digital Responsibility Engagement.
Proceedings of the 54. Jahrestagung der Gesellschaft für Informatik, 2024

2023
Quantitative study about the estimated impact of the AI Act.
CoRR, 2023

Using Assurance Cases to assure the fulfillment of non-functional requirements of AI-based systems - Lessons learned.
Proceedings of the IEEE International Conference on Software Testing, Verification and Validation, ICST 2023, 2023

2022
Fairness by awareness? On the inclusion of protected features in algorithmic decisions.
Comput. Law Secur. Rev., 2022

2021
Diversity in News Recommendation (Dagstuhl Perspectives Workshop 19482).
Dagstuhl Manifestos, 2021

Towards a Common Testing Terminology for Software Engineering and Artificial Intelligence Experts.
CoRR, 2021

Legal perspective on possible fairness measures - A legal discussion using the example of hiring decisions (preprint).
CoRR, 2021

Legal perspective on possible fairness measures - A legal discussion using the example of hiring decisions.
Comput. Law Secur. Rev., 2021

Towards a Common Testing Terminology for Software Engineering and Data Science Experts.
Proceedings of the Product-Focused Software Process Improvement, 2021

Assuring Fairness of Algorithmic Decision Making.
Proceedings of the 14th IEEE International Conference on Software Testing, 2021

2020
Quantitative analysis of automatic performance evaluation systems based on the h-index.
Scientometrics, 2020

Diversity in News Recommendations.
CoRR, 2020

Why Do We Need to Be Bots? What Prevents Society from Detecting Biases in Recommendation Systems.
Proceedings of the Bias and Social Aspects in Search and Recommendation, 2020

2017
Users - The Hidden Software Product Quality Experts?: A Study on How App Users Report Quality Aspects in Online Reviews.
Proceedings of the 25th IEEE International Requirements Engineering Conference, 2017


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