3#14 - Claes Lyth Walsø - Towards a Data-Driven Police Force (Nor)

MetaDAMA - Data Management in the Nordics

«Dataen i seg selv gir ikke verdi. Hvordan vi bruker den, som er der vi kan hente ut gevinster.» / «Data has no inherent value. How we use it is where we can extract profits.»
Embark on an exploration of what a data-driven Police Force can be, with Claes Lyth Walsø from Politiets IT enhet (The Norwegian Police Forces IT unit).
We explore the profound impact of 'Algo-cracy', where algorithmic governance is no longer a far-off speculation but a tangible reality. Claes, with his wealth of experience transitioning from the private sector to public service, offers unique insights into technology and law enforcement, with the advent of artificial intelligence.
In this episode, we look at the necessity of integrating tech-savvy legal staff into IT organizations, ensuring that the wave of digital transformation respects legal and ethical boundaries and fosters legislative evolution. Our discussion continuous towards siloed data systems and the journey towards improved data sharing. We spotlight the critical role of self-reliant analysis for police officers, probing the tension between technological advancement and the empowerment of individuals on the front lines of law enforcement.
We steer into the transformation that a data-driven culture brings to product development and operational efficiency. The focus is clear: it's not just about crafting cutting-edge solutions but also about fostering their effective utilization and the actionable wisdom they yield. Join us as we recognize the Norwegian Police's place in the technological journey, and the importance of open dialogue in comprehending the transformations reshaping public service and law enforcement.
Here are my key takeaways:

• Norwegian police is working actively to analyse risks and opportunities within new technology and methodology, including how to utilize the potential of AI.
• But any analysis has to happen in the right context, compliant within the boundaries of Norwegian and international law.
• Data Scientists are grouped with Police Officers to ensure domain knowledge is included in the work at any stage.
• Build technological competency, but also ensure the interplay with domain knowledge, police work, and law.
• Juridical and ethical aspects are constantly reviewed and any new solution has to be validated against these boundaries.
• The Norwegian Police is looking for smart and simple solutions with great effect.
• The Norwegian Police is at an exploratory state, intending to understand risk profiles with new technology before utilizing it in service.
• There is a need to stay on top of technological development of the Norwegian Police to ensure law enforcement and the security of the citizens. This cannot be reliant on proprietary technology and services.
• Prioritization and strategic alignment is dependent on top-management involvement.
• Some relevant use cases:
  • Picture recognition (not necessarily face-recognition) - how can we effectively use picture material from e.g. crime scenes or large seizure.
  • Language to text services to e.g. transcribe interrogations and investigations.
  • Human errors are way harder to quantify and predict then machine errors.
• This is changing towards more cross-functional involvement.
• The IT services is also moving away from project based work, to product based.
• They are also building up a «tech-legal staff», to ensure that legal issues can be discussed as early as possible, consisting of jurists that have technology experience and understanding.
• Data-driven police is much more than just AI:
  • Self-service analysis, even own the line of duty.
  • Providing data ready for consumption.
  • Business intelligence and data insights.
  • Tackling legacy technology, and handling data that is proprietary bound to outdated systems.

챕터

1. Data-Driven Politics in Norway (00:00:00)

2. Tech Legal Staff for Data Management (00:13:33)

3. Data-Driven Decision Making in Law Enforcement (00:19:40)

4. Transformation Through Data-Driven Culture (00:33:03)