- March 19, 2019
-- Casey Family Programs recently supported an independent ethical review of the Predict-Align-
Prevent (PAP) program by Professor Tim Dare of The University of Auckland, to identify ethical considerations and make recommendations to address or mitigate potential risks.
Prevent Program implements a novel continuous quality improvement cycle which includes geospatial machine learning to predict the locations of future child maltreatment events, community-based strategic planning to optimize allocation of existing prevention resources, and longitudinal measurement of population health and safety metrics to determine the effectiveness of aligned prevention resources and supports.
By taking a place-based approach to prevention across multiple jurisdictions nationally, Predict-Align-
Prevent aims to help communities and governments uncover, evaluate, and replicate effective prevention initiatives. Ultimately, Predict-Align-
Prevent is seeking the combination(
s) of programs, services, and infrastructure that reliably prevents child maltreatment and related fatalities across jurisdictions.
In his evaluation, Professor Dare states, "As is almost always the case with social policy uses of predictive analytics, many of the central questions around the ethics of the Predict-Align-
Prevent program are essentially comparative, i.e., about how the program compares, ethically, with alternative approaches. I assume here that doing nothing about child maltreatment is not a plausible option and that at least some degree of targeting is both necessary, given resource constraints, and desirable, given the burden of some child protection initiatives.
If we are committed to at least some degree of targeting, there is reason to consider at least some forms of data analytics, and PAP's geospatial modelling seems in many respects an attractive alternative. Its focus on 'places' rather than individuals avoids many of the privacy and stigmatizing risks of individual or family based modeling, and I think reduces some of the dangers of the inevitable errors to which even the best predictive models are vulnerable."
The ethical review is available at (https://www.predict-align-prevent.org/ethical-review
). The author stated that he is, "satisfied that the PAP Program has the potential to deliver genuine benefits while avoiding some of the familiar risks of alternative approaches to targeting child protection services."
Prevent's most recent work, view the Richmond, Virginia Technical Report at https://www.predict-align-prevent.org/richmond-report
Prevent ( https://www.predict-
align-prevent.org ) is a Texas-based 501(c)(3) nonprofit, that delivers state child welfare agencies solutions surrounding spatial analysis, clinical experience, econometrics, community alignment, and predictive analytics to stop child maltreatment before it happens.
For more information, please contact Danielle VanZorn, Director of Operations via danielle@predict-
align-prevent.org or 941-445-3949.