# Reasons underdetermination in meaningful human control

## TL;DR

This paper explores a gap in the Meaningful Human Control framework for AI systems by examining how conflicting reasons can prevent clear decisions.

## Contribution

The paper introduces the concept of 'reasons underdetermination' as a novel challenge for the Meaningful Human Control framework.

## Key findings

- The paper identifies a critical gap in the MHC framework regarding conflicting reasons.
- Reasons underdetermination challenges the achievement of Tracking and Tracing conditions in MHC.

## Abstract

The rapid proliferation of AI systems has raised many concerns about safety and responsibility in their design and use. The philosophical framework of Meaningful Human Control (MHC) was developed in response to these concerns, and tries to provide a standard for designing and evaluating such systems. While promising, the framework still requires further theoretical and practical refinement. This paper contributes to that effort by drawing on research in axiology and rational decision theory to identify a critical gap in the framework. Specifically, it argues that while ‘reasons’ play a central role in MHC, there has been little discussion of the possibility that, when weighed against each other, reasons may not always point to a single, rationally preferable course of action. I refer to these cases as instances of reasons underdetermination, and this paper discusses the need to address this issue within the MHC framework. The paper begins by providing an overview of the key concepts of the MHC framework and then examines the role of ‘reasons’ in the framework’s two main conditions - Tracking and Tracing. It then discusses the phenomenon of reasons underdetermination and shows how it poses a challenge for the achievement of both Tracking and Tracing.