The Ghost Effect in AI – What is it and why should we care about it?

Article

The "ghost effect" highlights a hidden challenge in predictive modeling, where outdated data continues to shape decisions, even as AI systems like large language models evolve to handle human-like interactions. How do we manage these spectral influences while advancing in empathy and precision?

Nov 25, 2024

Chandrakant Maheshwari, FVP, Lead Model Validator, Flagstar Bank

The Ghost Effect in AI – What is it and why should we care about it?

The views and opinions expressed in this content are those of the thought leader as an individual and are not attributed to CeFPro or any other organization

The ghost effect describes how historical data influences predictive models, even when it no longer reflects current realities.
Large language models (LLMs) are especially susceptible to the ghost effect due to their reliance on vast datasets, potentially perpetuating biases.
The ethical and practical implications of ghosting grow as AI tools take on roles requiring empathy and nuanced understanding.
Addressing the ghost effect requires balancing data-driven precision with adaptability to human behavior and evolving circumstances.