AI and Fire Investigation: Navigating The Reliability And Legal Considerations

The Use of Artificial Intelligence in Fire Investigations: Insights presented by Vithyaa Thavapalan at the AAFI Biannual Conference.

On October 22, 2024, I had the opportunity to speak at the Australasian Association of Fire Investigators (AAFI) Biannual Conference, hosted by the New South Wales Association of Fire Investigators in Penrith. My presentation focused on using Artificial Intelligence (AI) in fire investigations, exploring both the vast potential and the legal issues that come with incorporating AI technologies into our work.

With any new technology, the integration of AI introduces several legal and ethical issues that must be carefully navigated. This article provides an overview of the key points discussed during the presentation, focusing on the benefits of AI in fire investigations, its challenges, and how we can ensure its responsible use. Benefits of AI in Fire Investigation

AI technologies can significantly enhance fire investigations, helping investigators process large amounts of data, identify patterns, and predict potential risks. Here are the key benefits discussed:

Enhanced Analytical Capabilities

AI can process and analyse vast datasets much faster than investigators, such as historical fire reports, environmental conditions, and witness testimonies. This allows investigators to uncover patterns that may not be immediately visible, leading to more informed conclusions about the causes and circumstances of fire incidents.

Predictive Analytics

AI can analyse historical fire data through machine learning algorithms to forecast potential risks. By identifying trends and correlations, AI can help fire investigators, fire engineers and those within the fire protection industry to take proactive measures, preventing future incidents and enhancing public safety.

Increased Efficiency

Automating routine investigative tasks frees up valuable time for investigators to focus on more complex aspects of their work. This increases the speed of investigations and helps reduce the strain on resources, ensuring faster responses in time-sensitive situations where swift action can prevent further damage.

Learning & Development

AI can play a role in the development of fire investigators by enhancing training methods and providing them with tools to better identify fire patterns, causes, and behaviours. AI can be used to create detailed simulations of various fire scenarios, enabling trainees to observe and interact with different fire patterns. By analysing large datasets of historical fire incidents, AI systems can identify recurring patterns of fire behaviour, such as burn patterns, spread trajectories, and ignition sources. These patterns can then be presented to trainees through realistic simulations or case studies, helping them recognise similar signs in real-world investigations.

AI-powered learning platforms, combined with virtual reality (VR), can provide an immersive training experience. Trainees can virtually "walk through" fire scenes, examining burn patterns, structural damage, and other critical indicators. VR simulations, powered by AI, can create multiple fire scenarios with varying conditions, giving trainees the opportunity to analyze different cases in a controlled setting. This kind of hands-on training accelerates learning and improves decision-making skills.

AI can assist in training investigators to make decisions based on comprehensive data analysis whilst incorporating the fire origin matrix and hypothesis testing methods. By using AI to analyze fire incident data, trainees can practice determining the most likely cause of a fire, identify the origin point, and predict fire spread patterns under different conditions. AI can also provide multiple scenarios for trainees to evaluate, offering them the chance to test their judgment and refine their decision-making skills in real-time.

Legal Issues and Challenges

While AI brings significant benefits to fire investigations, it also presents a range of legal and ethical challenges that need to be addressed:

1. The Black Box Problem

One of the biggest challenges with AI is the "black box" phenomenon. AI systems can generate results without clearly explaining how those results were reached. In fire investigations, this lack of transparency can pose legal risks. If AI-generated findings are challenged in court, the inability to explain how the AI arrived at its conclusions could undermine the credibility of the evidence.

2. Learning Processes and Transparency

AI systems "learn" from vast datasets to make predictions and decisions. However, the AI may produce skewed or incorrect results if the training data contains biases or inaccuracies. Fire investigators must critically assess the data used to train AI systems, ensuring the datasets are accurate, comprehensive, and free from bias. This is key to maintaining the investigation's integrity and upholding ethical standards.

3. Data Privacy and Protection

The use of AI often involves processing sensitive information, which brings data privacy concerns to the forefront. Investigators must adhere to data protection regulations to ensure the data that is collected, stored and analysed is compliant. However, in many countries, there are yet to be clear regulations in place for the use of AI in many industries, including fire investigation. This is where the legal complications may arise.

4. Evidence Integrity

For AI-generated evidence to be admissible in court, it must maintain its integrity. Investigators need to have a clear understanding of the algorithms and processes behind AI tools to ensure the evidence they present is reliable. If the validity of AI findings is questioned, it can potentially undermine the investigation and result in legal disputes. To ensure the evidence is admissible the current recommendation is to document and record the entire process including the input and be prepared to present this in court.

5. Liability and Accountability

AI raises questions around liability in the event of errors or misinterpretations. Determining who is responsible for AI-driven decisions—the investigator, the organisation, or the AI developer—can be complex. Establishing clear guidelines and accountability frameworks is essential to mitigate potential risks. This is when we remind ourselves that AI is a tool and the investigator must never entirely rely on it.

As the fire investigation field continues to evolve, we must continue the conversation around the use of AI. Collaboration between investigators, legal experts, and AI developers is essential for creating a framework that promotes innovation while ensuring the ethical use of AI technologies.

In a future post, we will showcase a variety of AI tools currently being used in fire investigations. Are you already using an AI tool in your work? Let us know by completing this short form (it’ll take less than two minutes). Your anonymity is guaranteed, and we’re eager to share your knowledge and insights within the industry.