Mykhailo Danilevskyi
ABSTRACT
In recent years, there has been a lot of discussion around ethics in IT and AI. Researchers and organizations have proposed guidelines to address privacy, fairness, and explainability challenges for creating trustworthy AI.
In this work, we outline the importance of compliance with the above-mentioned ethical principles and their influence on the quality of AI systems. We map the relationship between available approaches for compliance with privacy, fairness, explainability principles and the accuracy of AI system decisions.
Additionally, we introduce the difference between ensuring fairness for phenomena presented with tabular data and text. Tabular data may contain protected attributes such as gender, age, or race as well as the decision made historically in relation to the people concerned. Data presented in text is not structured and requires sense perception by AI systems to detect bias or unfairness. In the poster, we compare available approaches and present experiments for measuring bias in text data.
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Cross Ref
- High-Level Expert Group on AI. 2019. Ethics guidelines for trustworthy AI. Report. European Commission, Brussels. https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-aiGoogle Scholar
- Dirk Hovy and Shrimai Prabhumoye. 2021. Five sources of bias in natural language processing. Language and Linguistics Compass 15, 8 (2021), e12432. https://doi.org/10.1111/lnc3.12432 arXiv:https://compass.onlinelibrary.wiley.com/doi/pdf/10.1111/lnc3.12432Google ScholarCross Ref
- Bo Li, Peng Qi, Bo Liu, Shuai Di, Jingen Liu, Jiquan Pei, Jinfeng Yi, and Bowen Zhou. 2023. Trustworthy AI: From Principles to Practices. ACM Comput. Surv. 55, 9, Article 177 (jan 2023), 46 pages. https://doi.org/10.1145/3555803Google ScholarDigital Library
- Shaina Raza, Deepak John Reji, and Chen Ding. 2022. Dbias: detecting biases and ensuring fairness in news articles. International Journal of Data Science and Analytics (2022), 1 – 21. https://api.semanticscholar.org/CorpusID:247050244Google ScholarCross Ref
- Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, and Michael Madaio. 2023. Fairlearn: Assessing and Improving Fairness of AI Systems. arxiv:2303.16626 [cs.LG]Google Scholar
Index Terms
- On the compliance with ethical principles in AI
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