Why We Need a New Framework for Emotional Intelligence in AI

TL;DR

This paper argues that current AI emotional intelligence evaluation frameworks are inadequate and proposes a refined approach based on a comprehensive understanding of emotion and EI.

Contribution

It develops a new perspective on evaluating AI emotional intelligence by critically analyzing existing frameworks and proposing improvements grounded in emotion theory.

Findings

Existing benchmarks lack a solid foundation in emotion theory

Current EI evaluations do not fully capture AI's emotional sensing and response capabilities

Proposed strategies aim to improve AI EI assessment methods

Abstract

In this paper, we develop the position that current frameworks for evaluating emotional intelligence (EI) in artificial intelligence (AI) systems need refinement because they do not adequately or comprehensively measure the various aspects of EI relevant in AI. Human EI often involves a phenomenological component and a sense of understanding that artificially intelligent systems lack; therefore, some aspects of EI are irrelevant in evaluating AI systems. However, EI also includes an ability to sense an emotional state, explain it, respond appropriately, and adapt to new contexts (e.g., multicultural), and artificially intelligent systems can do such things to greater or lesser degrees. Several benchmark frameworks specialize in evaluating the capacity of different AI models to perform some tasks related to EI, but these often lack a solid foundation regarding the nature of emotion and what it is to be emotionally intelligent. In this project, we begin by reviewing different theories about emotion and general EI, evaluating the extent to which each is applicable to artificial systems. We then critically evaluate the available benchmark frameworks, identifying where each falls short in light of the account of EI developed in the first section. Lastly, we outline some options for improving evaluation strategies to avoid these shortcomings in EI evaluation in AI systems.