AI-Based Reconstruction from Inherited Personal Data: Analysis, Feasibility, and Prospects

TL;DR

This paper investigates the feasibility of creating AI-based digital replicas of deceased researchers using inherited personal data, discussing technical, ethical, and future collaborative aspects.

Contribution

It demonstrates that sufficient personal data can fine-tune AI models to replicate a researcher's style and expertise, proposing new possibilities for digital legacy preservation.

Findings

Approximately one million words are sufficient for high-fidelity AI replication.

Including non-textual data can further enhance AI representation.

Potential for AI-driven collaboration and strategic decision-making among digital copies.

Abstract

This article explores the feasibility of creating an "electronic copy" of a deceased researcher by training artificial intelligence (AI) on the data stored in their personal computers. By analyzing typical data volumes on inherited researcher computers, including textual files such as articles, emails, and drafts, it is estimated that approximately one million words are available for AI training. This volume is sufficient for fine-tuning advanced pre-trained models like GPT-4 to replicate a researcher's writing style, domain expertise, and rhetorical voice with high fidelity. The study also discusses the potential enhancements from including non-textual data and file metadata to enrich the AI's representation of the researcher. Extensions of the concept include communication between living researchers and their electronic copies, collaboration among individual electronic copies, as well as the creation and interconnection of organizational electronic copies to optimize information access and strategic decision-making. Ethical considerations such as ownership and security of these electronic copies are highlighted as critical for responsible implementation. The findings suggest promising opportunities for AI-driven preservation and augmentation of intellectual legacy.