The recent use of generative artificial intelligence (AI) in paranormal investigations highlights how this rapidly evolving technology is finding its way into increasingly unexpected applications, beyond established uses in content generation, customer support and across industries such as healthcare, finance and manufacturing.
New applications are using smartphone sensors, image generation and audio analysis to identify and interpret supposed paranormal activity. However, when AI is involved in generating or interpreting an output, questions arise over what those results actually represent and how much confidence users should place in them.
AI-powered apps for paranormal investigations
GhostTube Seer, developed by Amy Waine and Jarrad Cutting, a team of paranormal investigators, uses signals from a smartphone's built-in magnetometer to select words from a database before using an image generator to produce AI-generated visuals. Google Play metrics show that the application has over a million downloads.
Spirit Phone Innovations uses AI to analyse audio signals and search for electronic voice phenomena (EVP). This refers to sounds which ghost hunters interpret as voices captured through electronic recording equipment.
Authenticity and reliability of AI-generated outputs
Critics question whether AI-generated results could reinforce expected outcomes rather than provide reliable evidence. For example, in paranormal investigations, sceptics argue that the technology may use available data to generate images or interpret audio in ways that support users’ existing expectations.
But questions around the reliability and interpretation of AI outputs extend far beyond paranormal investigation.
According to research from USwitch, more than five million people in the UK have experienced problems after following incorrect AI chatbot advice, including health scares, relationship problems and financial losses.
In healthcare, concerns have also emerged around AI-generated health content, including synthetic doctor personas and misleading information on social media platforms such as TikTok.
In finance, consumers remain cautious about relying on AI for financial advice. These concerns have been reinforced by examples of general-purpose AI tools providing inaccurate financial information. For example, AI models including ChatGPT and Copilot supplied incorrect information that could potentially have caused UK savers to breach HMRC Individual Savings Account (ISA) limits.
Why transparency matters as AI reaches new areas
Although new applications demonstrate the versatility of AI, an AI-generated or AI-interpreted output should not automatically be treated as reliable evidence or advice.
Transparency around data sources, processing methods and generated results remains essential as AI moves into increasingly diverse applications. Whether AI is interpreting sensor readings, answering financial questions or generating online content, understanding how an output has been produced is crucial to deciding how much confidence to place in it.
Ghost hunting may be one of AI's more unusual applications, but it highlights a much broader challenge: understanding the difference between what AI can generate, what it can interpret and what the available evidence can actually support.
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