Bridging Prediction and Intervention Problems in Social Systems

Fuente: arXiv

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Bibliographic Details
Main Authors:	Liu, Lydia T., Raji, Inioluwa Deborah, Zhou, Angela, Guerdan, Luke, Hullman, Jessica, Malinsky, Daniel, Wilder, Bryan, Zhang, Simone, Adam, Hammaad, Coston, Amanda, Laufer, Ben, Nwankwo, Ezinne, Zanger-Tishler, Michael, Ben-Michael, Eli, Barocas, Solon, Feller, Avi, Gerchick, Marissa, Gillis, Talia, Guha, Shion, Ho, Daniel, Hu, Lily, Imai, Kosuke, Kapoor, Sayash, Loftus, Joshua, Nabi, Razieh, Narayanan, Arvind, Recht, Ben, Perdomo, Juan Carlos, Salganik, Matthew, Sendak, Mark, Tolbert, Alexander, Ustun, Berk, Venkatasubramanian, Suresh, Wang, Angelina, Wilson, Ashia
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Computers and Society Applications
Online Access:	Acceder al recurso
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_version_	1866908751586394112
author	Liu, Lydia T. Raji, Inioluwa Deborah Zhou, Angela Guerdan, Luke Hullman, Jessica Malinsky, Daniel Wilder, Bryan Zhang, Simone Adam, Hammaad Coston, Amanda Laufer, Ben Nwankwo, Ezinne Zanger-Tishler, Michael Ben-Michael, Eli Barocas, Solon Feller, Avi Gerchick, Marissa Gillis, Talia Guha, Shion Ho, Daniel Hu, Lily Imai, Kosuke Kapoor, Sayash Loftus, Joshua Nabi, Razieh Narayanan, Arvind Recht, Ben Perdomo, Juan Carlos Salganik, Matthew Sendak, Mark Tolbert, Alexander Ustun, Berk Venkatasubramanian, Suresh Wang, Angelina Wilson, Ashia
author_facet	Liu, Lydia T. Raji, Inioluwa Deborah Zhou, Angela Guerdan, Luke Hullman, Jessica Malinsky, Daniel Wilder, Bryan Zhang, Simone Adam, Hammaad Coston, Amanda Laufer, Ben Nwankwo, Ezinne Zanger-Tishler, Michael Ben-Michael, Eli Barocas, Solon Feller, Avi Gerchick, Marissa Gillis, Talia Guha, Shion Ho, Daniel Hu, Lily Imai, Kosuke Kapoor, Sayash Loftus, Joshua Nabi, Razieh Narayanan, Arvind Recht, Ben Perdomo, Juan Carlos Salganik, Matthew Sendak, Mark Tolbert, Alexander Ustun, Berk Venkatasubramanian, Suresh Wang, Angelina Wilson, Ashia
contents	Many automated decision systems (ADS) are designed to solve prediction problems -- where the goal is to learn patterns from a sample of the population and apply them to individuals from the same population. In reality, these prediction systems operationalize holistic policy interventions in deployment. Once deployed, ADS can shape impacted population outcomes through an effective policy change in how decision-makers operate, while also being defined by past and present interactions between stakeholders and the limitations of existing organizational, as well as societal, infrastructure and context. In this work, we consider the ways in which we must shift from a prediction-focused paradigm to an intervention-oriented paradigm when considering the impact of ADS within social systems. We argue this requires a new default problem setup for ADS beyond prediction, to instead consider predictions as decision support, final decisions, and outcomes. We highlight how this perspective unifies modern statistical frameworks and other tools to study the design, implementation, and evaluation of ADS systems, and point to the research directions necessary to operationalize this paradigm shift. Using these tools, we characterize the limitations of focusing on isolated prediction tasks, and lay the foundation for a more intervention-oriented approach to developing and deploying ADS.
format	Preprint
id	arxiv_https___arxiv_org_abs_2507_05216
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Bridging Prediction and Intervention Problems in Social Systems Liu, Lydia T. Raji, Inioluwa Deborah Zhou, Angela Guerdan, Luke Hullman, Jessica Malinsky, Daniel Wilder, Bryan Zhang, Simone Adam, Hammaad Coston, Amanda Laufer, Ben Nwankwo, Ezinne Zanger-Tishler, Michael Ben-Michael, Eli Barocas, Solon Feller, Avi Gerchick, Marissa Gillis, Talia Guha, Shion Ho, Daniel Hu, Lily Imai, Kosuke Kapoor, Sayash Loftus, Joshua Nabi, Razieh Narayanan, Arvind Recht, Ben Perdomo, Juan Carlos Salganik, Matthew Sendak, Mark Tolbert, Alexander Ustun, Berk Venkatasubramanian, Suresh Wang, Angelina Wilson, Ashia Machine Learning Computers and Society Applications Many automated decision systems (ADS) are designed to solve prediction problems -- where the goal is to learn patterns from a sample of the population and apply them to individuals from the same population. In reality, these prediction systems operationalize holistic policy interventions in deployment. Once deployed, ADS can shape impacted population outcomes through an effective policy change in how decision-makers operate, while also being defined by past and present interactions between stakeholders and the limitations of existing organizational, as well as societal, infrastructure and context. In this work, we consider the ways in which we must shift from a prediction-focused paradigm to an intervention-oriented paradigm when considering the impact of ADS within social systems. We argue this requires a new default problem setup for ADS beyond prediction, to instead consider predictions as decision support, final decisions, and outcomes. We highlight how this perspective unifies modern statistical frameworks and other tools to study the design, implementation, and evaluation of ADS systems, and point to the research directions necessary to operationalize this paradigm shift. Using these tools, we characterize the limitations of focusing on isolated prediction tasks, and lay the foundation for a more intervention-oriented approach to developing and deploying ADS.
title	Bridging Prediction and Intervention Problems in Social Systems
topic	Machine Learning Computers and Society Applications
url	https://arxiv.org/abs/2507.05216