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Register a new userImproving Policing with Natural Language Processing
تحسين الشرطة مع معالجة اللغة الطبيعية
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تستكشف هذه المقالة إمكانية معالجة اللغات الطبيعية (NLP) لتمكين نموذج شرطة مركزة وأقل فعالية وأقل من المواجهة التي كانت تستهلك حتى الآن من الموارد لتنفيذ الحجم. الشرطة المنحى للمشاكل (البوب) هي استبدال محتمل، على الأقل جزئيا، بالنسبة للشرطة التقليدية التي تعتمد نهجا تفاعلا، تعتمد اعتمادا كبيرا على نظام العدالة الجنائية. على النقيض من ذلك، يسعى البوب لمنع الجريمة من خلال التلاعب بالظروف الأساسية التي تسمح بالارتكاب الجرائم. يتطلب تحديد هذه الشروط الأساسية فهما مفصلا لأحداث الجريمة - معرفة ضمنية تعقد غالبا من قبل ضباط الشرطة ولكن يمكن أن تكون صعبة للغاية للاستمتاع ببيانات الشرطة المهيكلة. يوجد أحد المصدر المحتمل للنصية في بيانات نصية مجانية غير منظمة تجمعها الشرطة لأغراض التحقيق أو الإدارة. ومع ذلك، فإن وكالات الشرطة لا تحتوي عادة على المهارات أو الموارد لتحليل هذه البيانات على نطاق واسع. في هذه المقالة، نقول أن NLP يقدم القدرة على فتح هذه البيانات غير المنظمة وبالتالي السماح للشرطة بتنفيذ المزيد من مبادرات البوب. ومع ذلك، نحذر أن استخدام نماذج NLP دون معرفة كافية قد يسمح إما بإدخال التحيز داخل البيانات التي تؤدي إلى نتائج غير مواتية.
This article explores the potential for Natural Language Processing (NLP) to enable a more effective, prevention focused and less confrontational policing model that has hitherto been too resource consuming to implement at scale. Problem-Oriented Policing (POP) is a potential replacement, at least in part, for traditional policing which adopts a reactive approach, relying heavily on the criminal justice system. By contrast, POP seeks to prevent crime by manipulating the underlying conditions that allow crimes to be committed. Identifying these underlying conditions requires a detailed understanding of crime events - tacit knowledge that is often held by police officers but which can be challenging to derive from structured police data. One potential source of insight exists in unstructured free text data commonly collected by police for the purposes of investigation or administration. Yet police agencies do not typically have the skills or resources to analyse these data at scale. In this article we argue that NLP offers the potential to unlock these unstructured data and by doing so allow police to implement more POP initiatives. However we caution that using NLP models without adequate knowledge may either allow or perpetuate bias within the data potentially leading to unfavourable outcomes.
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