The paradox of the artificial intelligence system development process: the use case of corporate wellness programs using smart wearables

Author:

Angelucci AlessandraORCID,Li ZiyueORCID,Stoimenova NiyaORCID,Canali StefanoORCID

Abstract

AbstractArtificial intelligence (AI) systems have been widely applied to various contexts, including high-stake decision processes in healthcare, banking, and judicial systems. Some developed AI models fail to offer a fair output for specific minority groups, sparking comprehensive discussions about AI fairness. We argue that the development of AI systems is marked by a central paradox: the less participation one stakeholder has within the AI system’s life cycle, the more influence they have over the way the system will function. This means that the impact on the fairness of the system is in the hands of those who are less impacted by it. However, most of the existing works ignore how different aspects of AI fairness are dynamically and adaptively affected by different stages of AI system development. To this end, we present a use case to discuss fairness in the development of corporate wellness programs using smart wearables and AI algorithms to analyze data. The four key stakeholders throughout this type of AI system development process are presented. These stakeholders are called service designer, algorithm designer, system deployer, and end-user. We identify three core aspects of AI fairness, namely, contextual fairness, model fairness, and device fairness. We propose a relative contribution of the four stakeholders to the three aspects of fairness. Furthermore, we propose the boundaries and interactions between the four roles, from which we make our conclusion about the possible unfairness in such an AI developing process.

Funder

Politecnico di Milano

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Human-Computer Interaction,Philosophy

Reference52 articles.

1. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M (2016) Tensorflow: a system for large-scale machine learning. 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), 265–283

2. Abu-Elyounes D (2020) Contextual fairness: a legal and policy analysis of algorithmic fairness. U Ill JL Tech Pol’y 1:2

3. Ajunwa I (2020a) The “black box” at work. Big Data Soc 7(2):1–6. https://doi.org/10.1177/2053951720938093

4. Ajunwa I (2020b) The “black box” at work. Big Data Soc 7(2):205395172096618. https://doi.org/10.1177/2053951720938093

5. Ajunwa I, Crawford K, Ford JS (2016) Health and big data: an ethical framework for health information collection by corporate wellness programs. J Law Med Ethics 44(3):474–480

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