Digital Governance and Drug Policy

This study examines the integration of artificial intelligence (AI) into liberal drug policy frameworks, focusing on its emerging applications, normative implications, and governance challenges. Drawing on an intersection-focused qualitative evidence synthesis combined with comparative case study analysis, the study synthesizes emerging applications in drug-use monitoring, regulated supply-chain oversight, and harm reduction. While AI offers significant potential to enhance policy responsiveness and regulatory efficiency, its deployment also raises critical concerns related to algorithmic bias, data protection, and institutional accountability. To address these tensions, the study proposes a multidimensional evaluative framework based on five criteria: effectiveness, fairness, transparency, privacy protection, and ethical alignment. The findings underscore the need for context-sensitive regulatory mechanisms that safeguard human rights, enable participatory oversight, and mitigate algorithmic harms. By integrating normative theory with structured evidence screening and comparative analysis, the study contributes a conceptually grounded framework for evaluating AI-driven governance in high-stakes public health and regulatory contexts

Big data · Citizen journalism · Corporate governance · Data governance · Grounded theory · Harm · Normative · Software deployment · Artificial Intelligence in Healthcare and Education · Cybercrime and Law Enforcement Studies · Ethics and Social Impacts of AI

• The Rise of Big Data Policing

  Andrew Guthrie Ferguson•The Rise of Big Data Policing•2020

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• Public health and international drug policy

  Open Access•Joanne Csete, Adeeba Kamarulzaman et al.•The Lancet•2016

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• Reduction in overdose mortality after the opening of North America's first medically supervised safer injecting facility

  Open Access•Brandon D L Marshall, Brandon DL Marshall et al.•The Lancet•2011

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• Public health implications of legalising the production and sale of cannabis for medicinal and recreational use

  Open Access•Wendy Hall, Wayne Hall et al.•The Lancet•2019

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• Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

  Open Access•Cynthia Rudin•Nature Machine Intelligence•2019

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• Supervised injection services

  Open Access•Chloé Potier, Vincent Laprévote et al.•Drug and Alcohol Dependence•2014

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• Fair Prediction with Disparate Impact

  Open Access•Alexandra Chouldechova•Big Data•2017

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• Reflecting on reflexive thematic analysis

  Braun, Virginia Braun et al.•Qualitative Research in Sport and…•2019

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• An island apart? Risks and prices in the Australian cryptomarket drug trade

  Open Access•Jack Cunliffe, James Martins et al.•International Journal of Drug…•2017

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• The challenges and limits of big data algorithms in technocratic governance

  Open Access•Marijn Janssen, George Kuk•Government Information Quarterly•2016

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• Law as computation in the era of artificial legal intelligence

  Mireille Hildebrandt•University of Toronto Law Journal•2018

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• The New Jim Crow

  M M Alexander, Michelle Alexander•New Jim Crow•2020

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• What Can We Learn From The Portuguese Decriminalization of Illicit Drugs

  C E Hughes, Caitlin Hughes et al.•The British Journal of Criminology•2010

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• Using thematic analysis in psychology

  Open Access•Braun, Virginia Braun et al.•Qualitative Research in Psychology•2006

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• Substance use prediction using artificial intelligence techniques

  Open Access•Ali Unlu, Abdulhamit Subasi•Journal of Computational Social…•2025

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• Algorithmic regulation

  Open Access•Karen Yeung•Regulation & Governance•2018

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• Algorithmic governance

  Open Access•Daria Gritsenko, Matthew Wood•Regulation & Governance•2022

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• The ethics of algorithms

  Open Access•Brent Mittelstadt, Brent Daniel Mittelstadt et al.•Big Data & Society•2016

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• Thematic Analysis

  Open Access•Lorelli Nowell, Jill M Norris et al.•International Journal of…•2017

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