Transdisciplinary AI Observatory -- Retrospective Analyses and Future-Oriented Contradistinctions

TL;DR

This paper advocates for a transdisciplinary AI observatory approach that integrates retrospective and counterfactual analyses to enhance AI safety, offering practical guidelines and exploring long-term paradigms like artificial stupidity and eternal creativity.

Contribution

It introduces a novel transdisciplinary framework for AI safety observatories, combining retrospective and counterfactual analyses, and discusses long-term paradigms to guide future research.

Findings

Retrospective and counterfactual analyses aid in understanding AI risks.

Differentiated long-term safety paradigms inform future AI safety strategies.

Practical guidelines support implementation of AI observatories.

Abstract

In the last years, AI safety gained international recognition in the light of heterogeneous safety-critical and ethical issues that risk overshadowing the broad beneficial impacts of AI. In this context, the implementation of AI observatory endeavors represents one key research direction. This paper motivates the need for an inherently transdisciplinary AI observatory approach integrating diverse retrospective and counterfactual views. We delineate aims and limitations while providing hands-on-advice utilizing concrete practical examples. Distinguishing between unintentionally and intentionally triggered AI risks with diverse socio-psycho-technological impacts, we exemplify a retrospective descriptive analysis followed by a retrospective counterfactual risk analysis. Building on these AI observatory tools, we present near-term transdisciplinary guidelines for AI safety. As further contribution, we discuss differentiated and tailored long-term directions through the lens of two disparate modern AI safety paradigms. For simplicity, we refer to these two different paradigms with the terms artificial stupidity (AS) and eternal creativity (EC) respectively. While both AS and EC acknowledge the need for a hybrid cognitive-affective approach to AI safety and overlap with regard to many short-term considerations, they differ fundamentally in the nature of multiple envisaged long-term solution patterns. By compiling relevant underlying contradistinctions, we aim to provide future-oriented incentives for constructive dialectics in practical and theoretical AI safety research.