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Predictive Analytics for City Agencies: Lessons from Children's Services.

Ravi Shroff1

  • 1Center for Urban Science and Progress, New York University , Brooklyn, New York.

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Summary
This summary is machine-generated.

Municipal agencies can improve decision-making using citizen data and machine learning. Data scientists must navigate social and technical tradeoffs, emphasizing communication, resources, and ethics for responsible algorithm implementation.

Keywords:
big data industry standardsdata acquisition and cleaningdata protection, privacy, and policypredictive analytics

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Area of Science:

  • Public Administration
  • Data Science
  • Social Science

Background:

  • Municipal agencies collect extensive citizen interaction data.
  • Machine learning and statistical methods offer potential for enhanced decision-making and service delivery.
  • Data scientists face complex choices with significant real-world impacts when implementing predictive algorithms.

Purpose of the Study:

  • To illustrate the social and technical tradeoffs in data analysis for municipal agencies.
  • To provide recommendations for agencies using data scientists and predictive algorithms.
  • To highlight the responsibilities of data scientists in public service.

Main Methods:

  • A case study approach using experience with New York City's Administration for Children's Services.
  • Analysis of choices made during data analysis steps.
  • Identification of underlying themes influencing tradeoffs.

Main Results:

  • Choices in data analysis involve social and technical tradeoffs.
  • Frequent communication between data scientists, leadership, and domain experts is crucial.
  • Agency resources, organizational constraints, and ethical frameworks significantly impact outcomes.

Conclusions:

  • Agencies must support data scientists with clear ethical guidelines and resources.
  • Effective implementation of predictive algorithms requires interdisciplinary collaboration.
  • Data scientists play a critical role in ensuring equitable and efficient public service delivery.