Preventing Sam Bankman-Fried: A Business Model for using LLMs in Psychometric Testing

Abstract

This white paper addresses the critical issue of preventing large-scale fraud in organizations by introducing a new approach. We propose the use of Large Language Models (LLMs), like ChatGPT, to capture subtleties in language usage and detect variations in behavioral traits related to empathy. By measuring and quantifying empathy, organizations can reduce employee turnover, improve company sales, and mitigate the risk of fraud. This paper outlines the business model for implementing LLM-based behavioral testing, discusses its benefits, and offers insights into its potential applications.

Introduction

In today's business landscape, large-scale fraud, unethical behavior, and misconduct have far-reaching consequences for organizations, shareholders, and society as a whole. These issues are often linked to a lack of empathy, and individuals who exhibit signs of sociopathy or narcissism who are naturally drawn to leadership positions. Identifying and addressing such individuals within an organization is a challenge. What if this could be prevented by measuring empathy in the micro-aggressions of human speech? This paper presents an innovative approach to tackle these challenges using LLMs in behavioral testing.

LLMs Can Detect Empathy

Large Language Models, such as ChatGPT, possess an unprecedented capacity to understand the nuances of social exchanges. They can detect differences in behavioral traits based on language usage.

For example, LLMs can distinguish the context and emotional aspects of a scenario, like the differences in helping an old woman, a child, or an athletic teenage boy after they've fallen on a sidewalk. They can identify the subtleties in how each individual would feel and the empathy associated with providing assistance.

I conducted an experiment in which I created various scenarios where an individual was hurt. I then constructed various written apologies and had Chat GPT rank these apologies based on their empathy. ChatGPT correctly ranked the apologies by empathy.

Empathy as a Pound of Prevention

Criminals, sociopaths, and individuals with low empathy often reveal their lack of empathy in their explanations for their actions. They struggle to satisfactorily address how their actions affect their victims. To prevent large-scale fraud, organizations can implement a behavioral test that measures empathy and issues percentiles, comparing individuals to their peer group and then national averages. Individuals who perform in the lower percentiles compared to their peer group would be at higher risk of taking actions that have negative consequences on the organization and people. The test would be similar to current assessments for cognitive abilities and problem-solving skills, like the Cognitive Aptitude test(CCAT), but focused on empathy.

Behavioral Testing Implementation

Behavioral testing using LLMs can be conducted by prompting individuals to answer as many open-ended scenarios as possible within a specific time frame, e.g., 10 minutes. LLMs would then identify empathetic speech patterns for each individual and produce percentile rankings for everyone within an organization, as well as comparisons to national averages.

Sample test-bank questions:

1. How do I know if I've acted inappropriately?

2. Scenario X: If I've hurt this person by doing Y, how would I apologize?

3. Scenario X: How does person A feel? How does person B feel?

4. Scenario X: How does this benefit person A? How is person B hurt?

Nuances of Empathy and Test Bank Questions

To comprehensively assess empathy, the test bank needs to include questions specifically examining negative consequences, the willingness to take responsibility, and the ability to make amends. These aspects represent different dimensions of the construct we identify as empathetic and ethical behavior. While individuals with sociopathic or narcissistic traits may find it relatively easy to provide correct answers verbally, they often struggle to recognize the negative consequences or actions required to take responsibility for situations that have caused harm to an organization or an individual. These behavioral patterns remain consistent across various aspects of a person's life when they exhibit low empathy. Even when choosing to engage in charitable activities, they tend to favor activities that do not have a direct human benefit, such as participating in beach cleanups over volunteering at a food bank. (Not that beach cleanups aren’t amazing charity work, just pointing out a difference for an example).

Market Potential and Cost Analysis

Potential customers:

1.     Employers

2.     Government

3.     Military

4.     College Admissions

5.    Finance

6.     Investment

Test takers:

1.     Anyone in charge of significant financial resources

2.     Individuals in potentially ethically challenging roles (e.g., doctors and lawyers)

3.     Leadership Roles

4.     Sales Professionals

5.     Consultants

6.     Customer Facing Professionals

The cost to build the electronic test is estimated at $1 million.

Per-use cost of $0.50 per test taker for computational resources.

The potential user base in the US is substantial, with an average charge for an employment test being $25 per test taker.

Case Study in Dating Apps

To illustrate the importance of such testing, we present a case study involving an individual with an Ivy League background who also served as a founding member of a prominent blockchain company. In his interactions with potential romantic partners, this individual exhibited speech patterns lacking in empathy. He applied skillsets acquired during his business school negotiations and military leadership training to engage in inappropriate behavior. His actions led to a situation that resulted in a traumatic experience for a woman, an outcome that could have been predicted through behavioral testing. The absence of empathy displayed by this individual underscores the necessity of proactive testing and its application in dating apps. Furthermore, this presents an opportunity for dating apps to enhance their revenue by offering users the option to include their test results in their profiles. Additionally, users interested in filtering potential matches based on a specific percentile ranking compared to other users could be charged for this premium feature. A lot of people would pay for something like this!

Conclusion

Fraud, unethical behavior, and a lack of empathy can have devastating consequences for businesses and individuals. Implementing behavioral testing using LLMs presents an opportunity to identify individuals with low empathy and take preventative measures. As we continue to develop tools to navigate the complex landscape of modern business, proactive and cost-effective measures such as this test should be seriously considered to foster more ethical organizations.

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