Shareholder Democracy with AI Representatives

TL;DR

This paper proposes using AI-enabled representatives trained on individual shareholder preferences to improve voting accuracy and shareholder democracy in mutual funds, addressing current control and representation issues.

Contribution

It introduces a novel AI-based proxy system for shareholder voting, offering a new approach to enhance retail investor influence and decision-making in corporate governance.

Findings

AI models can predict shareholder votes accurately.

AI proxies could improve shareholder engagement.

Potential risks and benefits of AI representation are discussed.

Abstract

A large share of retail investors hold public equities through mutual funds, yet lack adequate control over these investments. Indeed, mutual funds concentrate voting power in the hands of a few asset managers. These managers vote on behalf of shareholders despite having limited insight into their individual preferences, leaving them exposed to growing political and regulatory pressures, particularly amid rising shareholder activism. Pass-through voting has been proposed as a way to empower retail investors and provide asset managers with clearer guidance, but it faces challenges such as low participation rates and the difficulty of capturing highly individualized shareholder preferences for each specific vote. Randomly selected assemblies of shareholders, or ``investor assemblies,'' have also been proposed as more representative proxies than asset managers. As a third alternative, we propose artificial intelligence (AI) enabled representatives trained on individual shareholder preferences to act as proxies and vote on their behalf. Over time, these models could not only predict how retail investors would vote at any given moment but also how they might vote if they had significantly more time, knowledge, and resources to evaluate each proposal, leading to better overall decision-making. We argue that shareholder democracy offers a compelling real-world test bed for AI-enabled representation, providing valuable insights into both the potential benefits and risks of this approach more generally.