Policing, Sensemaking and the Politics of Artificial Intelligence in Canada
Notice bibliographique
Résumé
One of the most crucial issues of our time for social scientists is to understand how Artificial Intelligence (AI) is transforming democratic societies. Here I study Canadian police policy making in the era of AI. As the police enacts the state monopoly of legitimate violence over a given territory, the ways in which it engages with AI to enhance this poweror notand how society responds to it, are crucial dynamics illustrative of the challenges AI pose for policymakers. I introduce the concept of Police AI Technological Innovations (PAITI): the procurement or use of a new piece of capital equipment that uses algorithms and AI to enhanceand potentially transformpolice decision-making practices. The first contribution of this dissertation is to explore how police leaders and other key policy actors make sense of PAITI. With limited information or technical background in AI, police leaders are tasked with translating complex technologies in policing terms; weight accountability and budgetary considerations; assess the needs and receptivity to change of their members; and consider how various stakeholders will respond to the AI turn in policing. Furthermore, this dissertation examines how this sensemaking impacts the very principles of democratic policing: that police services obey the rule of law (not tyrants), limit interventions in people’s lives, and are ultimately accountable to citizens. PAITI risk embedding police services within urban infrastructures, where they will be less visible or accountable to citizens, but more informed on them. PAITI policy is as such central to the continuous power struggle over the future of democratic policing. In a first theoretical chapter, this dissertation develops an assumption-based model to explore how the police simplifies PAITI according to its preferences. It is rooted in political science, Science and Technology Studies, and police sociology literature on how the police traditionally approaches innovations and organizational changes. I argue police leaders facing complex decisions regarding police AI technologies make sense of them through a simplification process centred on (1) the impact of technologies on traditional policing (enhancement or transformation), and (2) the type of surveillance capacities they enhance (direct or indirect). I introduce these simplifications under the form of two distinct, complementary continuums. On a change continuum, police leaders make sense of PAITI through a simplification process centred on the impact of technologies on traditional policing. Innovations that enhance what is valued as “real” police work by making it more efficient will be more likely to be adopted than innovations that fundamentally transform the nature of police work. On a surveillance continuum, an innovation that develops police surveillance capacities in a way that is visible to the public and habilitates the police to identify individuals directly is less likely to be favoured by police leaders.This theoretical argument is developed through the case of Canadian municipal PAITI policies, in three empirical chapters. Chapter 2 studies how environmental factors influence automatic licence plate readers (ALPR) programmatic dimensions. It fleshes out interactions between sensemaking, technical capacities, and context, by contrasting the Montreal and British Columbia cases. Chapter 3 refines our knowledge of organizations sensemaking of place-based predictive policing (PP). It gives a voice to officers who do not interact with PP. Implemented in 2017, the Vancouver Police Department exemplifies how police services’ technoscientific attitudes of PP risk perpetuating historic flaws and biases of policing under a false sense of algorithmic impartiality. Chapter 4 highlights the political dimension of body-worn cameras (BWC). The chapter notably discusses the case of Toronto, where AI was a key consideration during its 2020 BWC rollout
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,006 |
| Études des sciences et des technologies | 0,038 | 0,023 |
| Communication savante | 0,022 | 0,004 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».