Building safer AI: a responsible approach to AI in policing

By Huw Bristow, Chief Technology Officer, Altia

Building safer AI: a responsible approach to AI in policing

Artificial intelligence is changing how organisations work, and policing is no exception.

AI has the potential to help investigation teams work more efficiently. It can support repetitive tasks, analyse large datasets and help investigators identify information that may require further attention.

However, policing presents particularly high stakes.

Forces handle sensitive information and make decisions that can significantly affect individuals and communities. Therefore, responsible AI in policing must balance innovation with transparency, privacy, accountability and human oversight.

Understanding the risks of AI in policing

Before organisations adopt AI, they need to understand where risks may arise.

These risks extend beyond the technology itself. They include the data behind AI systems, how teams use their outputs and how the public perceives their use.

For police forces, maintaining public trust makes these considerations particularly important.

Bias and accountability

AI systems depend heavily on the data used to develop and operate them.

Incomplete or unrepresentative data can affect outputs. In an investigative environment, this creates an important challenge because those outputs may influence decisions involving individuals or communities.

Teams therefore need to understand the limitations of AI-generated results.

Transparency also matters. Investigators should be able to understand how technology supports their work rather than treating an automated output as an unquestionable answer.

Human judgement must remain central to the investigative process.

Privacy and data protection

AI systems can process significant volumes of information.

For law enforcement organisations, much of that information may be sensitive. Forces therefore need strong controls around how teams collect, process and use data.

The challenge is to gain value from technology without compromising privacy or appropriate data handling.

Organisations also need to consider whether data is suitable for use within AI systems, particularly when it could contribute to training or development.

Over-reliance on technology

AI can help investigators work more efficiently. However, it should support professional judgement rather than replace it.

Over-reliance on automated outputs can create new risks.

Investigators still need to question findings, understand context and apply their own expertise. Training is therefore essential when organisations introduce AI-powered tools.

The objective should be to enhance human capability, not remove humans from important decisions.

Building responsible AI into investigative workflows

Managing these risks requires more than a policy document.

Organisations need to consider responsible AI throughout the technology lifecycle, from design and implementation to everyday use.

Regular testing and auditing can help teams identify potential problems in datasets and AI outputs. Clear documentation can also help organisations understand how systems operate and where human review is required.

Meanwhile, training can give users the confidence to question automated outputs rather than accepting them without scrutiny.

This combination of technology, governance and human oversight creates a stronger foundation for responsible AI in policing.

Keeping humans at the centre

Human oversight is particularly important in investigations.

AI can help process information or highlight areas that may deserve attention. However, an investigator understands the wider context of a case.

Technology cannot replace that professional experience.

Therefore, organisations should design AI-enabled workflows around the investigator. Users should remain able to review outputs, challenge results and make the final decision.

This principle also helps protect against deskilling. Instead of removing expertise from investigations, technology should give skilled professionals more time to apply it.

Using AI to support investigative work

Used appropriately, AI can support several areas of investigative work.

For example, it can help teams process large volumes of text, support transcription and identify information within complex datasets.

AI-assisted tools may also reduce time spent on repetitive administrative work. As a result, investigators can focus more attention on analysis and decision-making.

Altia’s Transcribe supports investigation teams by turning recorded information into usable text.

More broadly, Altia HQ provides a secure platform for investigation teams to access connected investigative capabilities. Explore Altia HQ.

The value of these technologies comes from supporting investigators, not attempting to replace their judgement.

Transparency and public trust

Technical performance is only one measure of successful AI adoption.

Police forces also need to consider how communities understand and perceive the technology.

Public concern can grow when people do not understand where organisations use AI or how it influences decisions. Clear communication can therefore play an important role in building trust.

Forces should be able to explain why they use AI, what safeguards are in place and where humans remain responsible for decisions.

Openness does not remove every concern. However, it can help organisations demonstrate that they have considered the risks rather than adopting technology without appropriate scrutiny.

Keeping pace with regulation

AI governance continues to develop.

Police forces and technology providers therefore need to keep pace with relevant legal, regulatory and organisational requirements.

Regular reviews can help organisations identify changes that affect how they use AI. Clear documentation can also demonstrate how teams manage risks and maintain oversight.

Collaboration matters too.

Technology providers, law enforcement organisations, regulators and other industry bodies can share knowledge and contribute to stronger standards for responsible AI.

Seizing the opportunity responsibly

AI presents a significant opportunity for policing.

It can help teams process information faster, reduce repetitive work and make better use of large datasets. However, efficiency cannot come at the expense of accountability.

The strongest approach combines technology with human expertise.

By prioritising transparency, strong data practices and meaningful human oversight, organisations can manage risk while still benefiting from innovation.

Ultimately, responsible AI in policing is not about choosing between technology and people. It is about designing technology that supports people to make better-informed decisions.

That approach can help forces benefit from AI while protecting the principles of fairness, accountability and trust that effective policing depends on.

About Huw

Huw Bristow is Chief Technology Officer at Altia, a UK-based provider of intelligence and investigation software.

Altia develops technology for law enforcement agencies, government departments and private sector organisations.