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“MISQ Transparency Materials For Why And How Does A Machine Learning Algorithm Co-Exist With Alternative Methods For Identifying Social Welfare Blind Spots?” Metadata:

  • Title: ➤  MISQ Transparency Materials For Why And How Does A Machine Learning Algorithm Co-Exist With Alternative Methods For Identifying Social Welfare Blind Spots?
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  • Internet Archive ID: osf-registrations-um26d-v1

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The Social Security Information Service, a central government agency of South Korea, has implemented an information system using ML algorithms to identify social welfare blind spots. Social welfare blind spots refer to cases where individuals are eligible for social welfare benefits but are not current recipients for various reasons. Even though the ML-based model was performing well, the Korean government developed two additional methods, including a rule-based model and a human-driven heuristic method championed by local governments. Using a multi-method approach, this case study investigates why the ML-based method co-exists with the other two methods and how they complement one another. We found that policy makers’ lack of understanding of ML, local government employees’ perception, and accountability concerns contributed to their co-existence. The three methods had different strengths and weaknesses. The government agencies orchestrated these methods to increase complementarities by leveraging and strengthening different problem-solving approaches.

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  • Added Date: 2024-02-05 21:37:35
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