Abstract
Systems based on Artificial Intelligence, namely Data-driven decision systems have been used in the private sector in areas such as retail, finance, and telecommunications. More recently, data-driven decision systems started to be applied in different areas of public interest, such as health, urban planning, education, criminal justice, and public administration. Several countries have been defining their own Artificial Intelligence (AI) Policies, with respective national strategies. Data-driven decision systems are, therefore, becoming an essential part of the operations of different companies and public services, on a daily basis, while creating new challenges for society. Part of those challenges is related to the risks of those systems, namely the dimensions: Bias, Explainability, and Accuracy. The ethical problems that emerge from these risks, in particular, in the public domain, make them a concern and a new challenge for Public Policy decision-makers. The goal of this work is to understand how are Bias, Explainability, and Accuracy addressed in the Cross-Industry Standard Process for Data Mining (CRISP-DM) and the Public Policy (PP) processes, and establish the parallel between these processes. In order to do that, the documents related to these topics that are listed in the “Law and Policy Reading”, published by the “AI Now Research Institute” from New York University are analyzed. In this way, Public Policy decision-makers and developers are able to identify which phases should be looked at, in both processes, when identifying, using, evaluating, and comparing these risks in tools based on Data-driven Decision Models.
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Acknowledgements
This work is a result of the project Operation NORTE-08-5369-FSE-000045 supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Social Fund (ESF). Project “Network Science for urban engineering” under the FCT Arrangement/Agreement—Scientific and technological cooperation FCT/ INDIA-2017/2019 Ref: FCT/4755/3/5/2017/S.
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Teixeira, S., Rodrigues, J.C., Veloso, B., Gama, J. (2022). Challenges of Data-Driven Decision Models: Implications for Developers and for Public Policy Decision-Makers. In: Banerji, P., Jana, A. (eds) Advances in Urban Design and Engineering. Design Science and Innovation. Springer, Singapore. https://doi.org/10.1007/978-981-19-0412-7_7
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