How will artificial intelligence influence forensic science and the interpretation of evidence?
My presentation at the IAFS meeting in Sydney, Australia. Please keep reading all the way through...
Artificial intelligence (AI) is poised to have a significant impact on forensic science and the interpretation of evidence in various ways.
Here are some key ways in which AI is likely to influence this field:
Fingerprint Analysis
DNA Analysis
Digital Forensics
Facial Recognition
Voice Analysis
Ballistics Analysis
Cold Case Resolution
Bias reduction
AI can assist forensic experts in analyzing fingerprints by automating the process of comparing and matching latent prints to known databases. This can significantly speed up the identification process.
For example, a machine learning model could be trained on a dataset of fingerprints, and then used to match a new fingerprint to a person in a database.
AI can assist in DNA analysis by automating the interpretation of complex DNA profiles, identifying genetic markers, and helping in kinship analysis.
AI can be used to identify patterns in large data sets by using techniques such as machine learning and deep learning. These techniques involve training a model on a large dataset of labeled examples, such as DNA, and then using the model to make predictions on new, unseen data.
AI can be used to analyze digital evidence, such as computer files, emails, and social media data, to uncover evidence of cybercrime or aid in investigations involving electronic devices.
AI algorithms can be trained to automatically analyze and extract information from images and videos, which can be used to enhance or clarify evidence.
AI-based facial recognition systems can be used to identify individuals in images and videos, which can be crucial in criminal investigations and surveillance.
AI algorithms can be trained to identify and analyze injuries or abnormalities in medical images, such as X-rays or CT scans. This can help forensic pathologists to more quickly and accurately identify the cause of death or injury.
AI can help in voice analysis, including speaker identification, which can be used in criminal investigations and legal proceedings.
AI can assist in matching bullets and shell casings to specific firearms, helping in firearm-related investigations.
AI can be used to re-examine and re-evaluate evidence in cold cases, potentially leading to breakthroughs in unsolved cases.
AI can be trained to reduce gender and racial biases in facial recognition technology and other methods. By refining algorithms to provide accurate results, AI can ensure more equitable outcomes in identification processes.
Ethical concerns to be considered when using AI
Bias in training data: AI models are only as good as the data they are trained on. If the training data is biased, the model will also be biased, which could lead to inaccurate or unfair results.
Lack of transparency: AI models can be complex and difficult to understand, making it difficult to explain how they arrived at a particular conclusion. This can be problematic in forensic science, where the results need to be transparent and able to stand up to legal scrutiny.
Privacy concerns: The use of AI in forensic science can raise privacy concerns, as the technology may be used to analyze sensitive personal information without the individual's consent.
Lack of regulation: There is currently a lack of regulation for the use of AI in forensic science, which can lead to problems if the technology is used in a way that is not appropriate or ethical.
Human oversight: AI models can make errors, and it is important that there is human oversight to ensure that the results are correct and fair.
Artificial intelligence has the potential to significantly impact forensic science and the interpretation of evidence in a number of ways.
A note about the author
All of the previous text and images in this presentation were generated by AI.
I read every word verbatim. In my presentation at this point, I could see the audience shuffling and having uncomfortable looks—this is definitely not how I normally give a talk. When I revealed the truth, that AI had generated the talk (and the abstract), you could see everyone’s face light up with two realizations. One, that’s why he was reading the slides, and, two, Whoa—the whole thing was AI? We’ve heard how AI will either destroy the world or save humanity. The reality is a little from Column A, a little from Column B.
This is an inflection point in history and we’re living through it. That means we should all learn what we can about AI, play with it, get a feel for its strengths, its weaknesses, and how we can use and abuse it. The same principles apply as the rest of science and research: Good questions, sampling, proper analysis, and interpretations based on the outcomes regardless of what they are. You can’t use 100 versions of the same image with different brightness and saturation settings, for example, and call that a sample of 100 images. Understanding AI and how it can help us personally and professionally is our responsibility: AI is a tool, like a hammer, and it can be used to build or destroy, depending on how we use it.
Go, explore, play, but don’t be afraid.