Algorithms and venture investment decisions: better, fairer or biased?

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Abstract

Purpose

This paper is a rejoinder to the work of Blohm, Antretter, and colleagues recently published in both Entrepreneurship Theory and Practice and Harvard Business Review titled “It's a Peoples Game, Isn't It?! A Comparison Between the Investment Returns of Business Angels and Machine Learning Algorithms” and “Do Algorithms Make Better – and Fairer – Investments than Angel Investors?”, respectively.

Design/methodology/approach

While we agree with authors of prior scholarship on the importance of counteracting human biases, honing expert intuition and optimizing the odds of success in investment decision-making contexts, in the spirit of open academic discourse, this paper respectfully challenges some of the underlying assumptions concerning algorithmic bias on which prior work is based.

Findings

Investing remains part art and part science, and while algorithms may begin to play a more significant role in investment decision-making, human intuition remains hard to imitate. In both people and in algorithms, sources of bias remain both implicit and explicit and often have systemic roots, so more research continues to be needed to fully understand why algorithms produce potentially biased outcomes across a wide array of contexts.

Originality/value

This paper contributes to our collective understanding on the use of algorithms in making investment decisions, highlighting the fact that bias exists in humans and algorithms alike, even when the best of intentions are present.

Related collections

Most cited references 20

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Judgment under Uncertainty: Heuristics and Biases.

A Tversky, D Kahneman (1974)

This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.