@inproceedings{4d885b5920384c24b98420d6e2323946,
title = "Algorithm Debt: Challenges and Future Paths",
abstract = "Technical Debt (TD) is the implied cost of additional rework caused by choosing easier solutions in favour of shorter release time. It impacts software maintainability and evolvability, manifesting as different types (e.g., Code, Test, Architecture). Algorithm Debt (AD) is a new TD type recently identified as sub-optimal implementations of algorithm logic in scientific and Artificial Intelligence (AI) software. Given its newness, AD and its impact on AI-driven software remains a research gap. This poster aims to motivate reflective discussion on AD in AI software, by summarising findings, discussing its possible impact, and outlining future areas of work.",
keywords = "Algorithm Debt, Artificial Intelligence, Code Debt, Scientific Software, Software Engineering, Technical Debt",
author = "Simon, \{Emmanuel Iko Ojo\} and Melina Vidoni and Fard, \{Fatemeh H.\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2nd IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2023 ; Conference date: 15-05-2023 Through 16-05-2023",
year = "2023",
doi = "10.1109/CAIN58948.2023.00020",
language = "English",
series = "Proceedings - 2023 IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "90--91",
booktitle = "Proceedings - 2023 IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023",
address = "United States",
}