# Measuring Ethical Values with AI for Better Teamwork
## 1 Introduction
#### Foundational Research  
In his seminal research [PERSON] [3] introduced the concept of honest signals. These are small signs of an individual, for instance, body signals, little words, or the way how words are spoken or sent electronically, that give away the true intent, personality, emotion, or personal values of the individual. We have applied this concept of honest signals to email, for instance, measuring the speed of response to a message as a predictor of the passion of an employee, while the speed with which others respond to her is a predictor of the respect she commands [4]. Similarly, the creativity of an individual can be measured by tracking the changes in the network position of the individual in the group network [4]. The validity of these constructs has been verified many times, applying it to employee satisfaction [5], individual and team creativity [6], customer satisfaction, the likelihood of leaving the company [5], personality characteristics, and risk-taking attitude [7]. In another project, we have shown that the moral values of individuals are correlated with their honest signals expressed in their emails [8]. In this project, we will apply these insights to first compute personality characteristics, ethical values, and risk attitudes from the emails of employees and then attempt to uncover the hidden relationship between the values of these individuals and their individual and team performance.  
The present work builds on previous research examining the relationship between the concepts of honest signals and personal values measured with the same surveys as in the present paper. The first relevant study links honest signals to the traits of agreeableness, neuroticism, and openness to experience. Agreeableness, for example, is linked to greater centrality and shorter response time, neuroticism to lower centrality and a lower contribution index, and openness to experience to shorter response time [9]. In a further study, correlation and regression results demonstrate significant connections of honest signals for all five moral foundations of Haidt. According to this study, for example, positive and non-emotional language and \"nudging by email\" are predictive of a person's level of harm or care [8]. In sum, the study showed that honest signals account for 70% of the actors' moral values [8]. In terms of Schwartz's Values, the same study's correlation results show that the value of conservation is linked to increased responsiveness and social capital [8]. Unlike the current paper, the study has not examined the values that underpin Schwartz's dimensions. However, as these are underlying values of similar dimensions, it is presumed that they are likewise linked to honest signals. The final study on which this paper is based analyzes personality through the lens of DOSPERT values. Both email and DOSPERT survey data are collected, as in the other experiments, to investigate their relationship. The regression results suggest that honest signals can create significant predictions of people's risk attitudes [7]. In sum, the described studies indicate that email-based honest signals can be predictive of personal and moral values, as measured by the NEO FFI-R personality survey, the Schwartz's Values survey, the Haidt Moral Foundations questionnaire, and the DOSPERT risk-taking survey. This work builds on this previous research by developing machine learning models which predict individuals' personal and moral values by their honest signals. Hereby, these models serve as an intermediary step to predict the survey scores of employees and link these values to their performance.