In this conversation with Rick Muir, Professor Paul Taylor, the Chief Scientific Advisor to Policing discusses his background and the role of the Chief Scientific Advisor.

He also addresses the challenges and barriers to technological advancement in policing, the importance of collaboration between industry and policing, and the need to support SMEs.

He highlights the significance of interoperability and the potential impact of technologies such as drones, machine learning, and biometrics in policing. He also expresses concerns about deep fake technology.

The conversation explores the challenges and opportunities of AI in policing, with a focus on data quality and workforce development. It emphasizes the importance of good data quality for AI and analytics, as well as the need for national and local efforts to improve data management.

The conversation also discusses the ethical concerns related to AI and the importance of public awareness and evidence-based decision-making. The role of the workforce in understanding and utilizing AI is highlighted, along with the need for specialized skills and training. The conversation concludes with discussions on public engagement, scrutiny practices, and the need for evidence-based evaluations and knowledge gaps in police science and technology.

Takeaways

• The role of the Chief Scientific Advisor is to help the policing system leverage science and technology to overcome operational challenges.
• Policing faces barriers to technological advancement, including a focus on here-and-now systems, complexity, and a culture of failure.
• Collaboration between industry and policing is crucial, and efforts should be made to improve understanding and partnership between the two.
• Interoperability is essential for innovation in policing, and efforts should be made to break down barriers and share data.
• Technologies such as drones, machine learning, and biometrics have the potential to significantly impact policing.
• Deep fake technology poses a concern for policing and requires attention and mitigation strategies.
• Good data quality is crucial for AI and analytics in policing, and efforts should be made to improve data management at both national and local levels.
• Ethical concerns related to AI can be addressed through good data quality and evidence-based decision-making.
• The workforce in policing needs to evolve and develop specialized skills to effectively utilize AI and other emerging technologies.
• Public engagement is important in decision-making around testing and implementing emerging tech in policing.
• Scrutiny practices, both internal and external, need to evolve to keep up with developing technologies and new ways of working.
• There is a need for evidence-based evaluations and knowledge gaps in police science and technology, particularly in terms of cost-benefit evaluations and robust evidence for decision-making.

Sound Bites

• “I think the dichotomy is probably more complex than people realise.”
• “Local things become nationally much more organically.”
• “Interoperability is absolutely critical and something we’re driving forward.”
• “Development is going increasingly at pace and is only going to in the world of fraud and cyber fraud and other similar areas going to make things difficult.”
• “The biggest impact of AI on policing has nothing to do with AI and everything to do with data quality.”
• “The biggest challenge is to get a grip, a national grip and a local grip on data quality and data management.”

Chapters
00:00.  Introduction and Background
09:07.  The Role of the Chief Scientific Advisor
10:03.  Barriers to Technological Advancement
13:12.  Collaboration between Industry and Policing
17:48.  The Importance of Interoperability
28:16.  Technologies with Potential Impact
29:44.  Concerns about Deep Fake Technology
31:05.  The Importance of Data Quality in AI and Analytics
33:26.  Workforce Development and Specialized Skills
38:08.  Ethical Concerns and Evidence-Based Decision-Making in AI
44:46.  Public Engagement in Testing and Implementing Emerging Tech
50:34.  Evolving Scrutiny Practices for Developing Technologies
53:11.  The Need for Evidence-Based Evaluations in Police Science and Technology