“We decided to call our theory Semantic personality analysis due to its central hypothesis which states that we are the beliefs that we are constantly repeating, and that we act upon ourselves, others, and the world according to that speech.”Ricardo Michel
Is it possible to measure people´s mental state with Artificial Intelligence?
Last year, my partner Alejandro and I asked ourselves this question while looking at the IBM Watson feature catalog. Its Personality Insights API provides a Big 5 personality analysis that is the dominant model in adults and is based on:
- Openness to new experiences (curious / cautious)
- Discipline (organized / careless)
- Extroversion (energetic / reserved)
- Cooperativity (friendly / detached)
- Neuroticism (nervous / confident)
At first glance it looked good, so we decided to make a minimum viable product (MVP) based on it – which we called Hero Talent – to validate if the market was interested in knowing the personality of its workers. In this version, workers logged in with their email from the platform and with it we provided the same metrics that came from Watson in a dashboard. Fortunately we had a very positive response and we also found that there were already other companies like Crystal Knows that offered similar things. Most of the people I know are afraid of competition, but I am more afraid of a product that nobody wants to copy or that solves a problem for which no one has tried at least one bad trick.
After introducing our product to various HR managers in companies of different sizes, although primarily large corporations, we got very good feedback on what they really wanted to know about the workers’ state of mind. Immediately it became clear that Watson was not going to solve it and neither was the Big 5 model. We decided to inquire about other personality models that could, perhaps, be effective so we came across only two alternatives widely accepted in the scientific and business world:
1. The Myers-Briggs Indicator (MBTI) that measures:
- Extraversion / Introversion
- Preference for (sensory information / analysis and interretation)
- When making decisions (what is logical and consistent / depends on the person and the context)
- Preference for (structure / new information and options)
2. The DISC Model that measures:
- Extroversion / Introversion
- Orientation to tasks or people
We found a public dataset in Kaggle with MBTI personalities enough to train our neural network. Since our product analyzes workers’ emails every hour, we realized that we obtained a different classification when the person was under stress or depending on who they were writing to. These three systems are designed for quizzes-based, and questionnaire-based exams to fit people in one category or another. The model tries to fit the universe in a box, instead of adapting the box to the universe.
After going through all Perlego in search of books on personality theory and devouring all the scientific articles we could find, we realized that no model was going to be enough. People are dynamic and change with their internal context (hunger, thirst, libido, pain, neurotransmitters, hormones, substances) and external (friends, family, economy, trauma, culture). Even worse, how they see themselves (desired personality) is not the same as how others see them (apparent personality), than how they act (real personality).
At the end we concluded that while we cannot dispense with models at all, we must handle them as dynamic belief systems and not as global absolutes. We decided to call our theory Semantic Personality Analysis due to its central hypothesis which states that we are the beliefs that we are constantly repeating, and that we act upon ourselves, others, and the world according to that speech. Therefore, we do not try to enclose workers in a global category, but rather we offer metrics on different aspects of their hourly mental states considering their historical behavior and cultural context.
” We do not try to enclose workers in a global category, but rather we offer metrics on different aspects of their hourly mental states considering their historical behavior and cultural context.”Ricardo Michel
We designed all kinds of scrapers to get texts from books, poems, songs, dialogue from movies and series, social media, forums, and hired a team of psychologists to tag them based on our theory and the metrics we knew were of interest to Human Resources Managers. At the same time, we designed an algorithm to create word vectors that encode their meaning from their co-occurrence in the same context, since neural networks only work with numerical inputs and cannot be given the text without being processed in any way. We also designed other algorithms to remove the noise caused by common words, punctuation, links, etc. We created the architecture of our neural networks to express each metric in terms of risk, re-evaluated the results on new data and retrained the AI until we were satisfied with the result.
Nowadays, Erudit allows users to connect their email and Slack to the platform and our servers obtain messages every hour – without saving any information than the final analysis – that go through a series of neural networks that give parameters about their emotional state (happiness, anger, sadness, fear), well-being (empathy, frustration, loneliness, self-esteem, irritability, resistance to stress), connection with your team, manager, company and your risk of anxiety or depression.
At the same time, these metrics build two super metrics that we call risk of absenteeism and engagement rate. The first indicates how likely it is that the user will call in sick to work due to a poor mental state, and the second indicates how likely it is that the user will quit due to feeling alienated or not belonging. With this, we hope to be able to open the conversation about mental health in companies and turn the attention of human resources managers to those people who need help but perhaps do not know how to ask for it or are too embarrassed to do so.We want to make the world of work more human one mind at a time.
At Erudit we want to help you taking care of your workers in these challenging times, monitoring their levels of motivation, burn out, stress and anxiety (without the need of applying a single survey) and finding ways to maintain a healthy and productive team of collaborators.