Abstract
The growing use of data-driven decision systems based on Artificial Intelligence (AI) by governments, companies and social organizations has given more attention to the challenges they pose to society. Over the last few years, news about discrimination appeared on social media, and privacy, among others, highlighted their vulnerabilities. Despite all the research around these issues, the definition of concepts inherent to the risks and/or vulnerabilities of data-driven decision systems is not consensual. Categorizing the dangers and vulnerabilities of data-driven decision systems will facilitate ethics by design, ethics in design and ethics for designers to contribute to responsible AI. The main goal of this work is to understand which types of AI risks/ vulnerabilities are Ethical and/or Technological and the differences between human vs machine classification. We analyze two types of problems: (i) the risks/ vulnerabilities classification task by humans; and (ii) the risks/vulnerabilities classification task by machines. To carry out the analysis, we applied a survey to perform human classification and the BERT algorithm in machine classification. The results show that even with different levels of detail, the classification of vulnerabilities is in agreement in most cases.
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Acknowledgments
The research reported in this work was partially supported by the European Commission funded project “Humane AI: Toward AI Systems That Augment and Empower Humans by Understanding Us, our Society and the World Around Us” (grant #820437). The support is gratefully acknowledged.
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Teixeira, S., Veloso, B., Rodrigues, J.C., Gama, J. (2023). Ethical and Technological AI Risks Classification: A Human Vs Machine Approach. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1752. Springer, Cham. https://doi.org/10.1007/978-3-031-23618-1_10
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DOI: https://doi.org/10.1007/978-3-031-23618-1_10
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