Toxic leadership involves behaviors that create a work atmosphere, impacting employees’ well-being and organizations’ effectiveness. Extensive research has delved into this issue emphasizing its effects, on individuals and businesses such as reducing job satisfaction, heightening stress levels, and increasing turnover rates and quiet quitting. Recognizing and addressing leadership is crucial to nurturing a work environment for employee development and organizational prosperity. Research has shed light on destructive leadership characteristics and provided frameworks for identifying and dealing with behaviors.
One way to predict toxic leadership and avoid potential harm is by using machine learning predictive models. Machine learning introduces a data driven element to this effort improving the capacity to anticipate harassment and identify leadership trends within companies. It’s vital to grasp the deviant behaviors to address toxic leadership effectively. Studies have demonstrated the potential of machine learning techniques like natural language processing, classification models, and deep learning in interpreting communication patterns that signal toxic practices.
If traditional statistical methods can capture associations and correlations, they sometimes need help identifying patterns and interactions between factors contributing to toxic leadership. Indeed, machine learning excels at detecting relationships between variables and picking up on signals. It assigns levels of importance to factors like narcissistic traits, undermining behaviors, fear of retribution and inconsistent leadership, giving insights into the multifaceted and complex nature of toxic leadership. Among all machine learning models used to capture toxic pattern behaviors, narcissistic traits are consistently mentioned as one of the most powerful predictive signals. Usually, narcissistic personality disorder manifests itself in several ways. The narcissistic person overestimates his or her accomplishments and expects to be recognized as superior, without accomplishing anything that would warrant such recognition. She also constantly highlights certain successes or certain strengths without nuance and amplifies these accomplishments. Narcissistic people are absorbed in fantasies of unlimited success, power, splendor, beauty, or ideal love. Very often dissatisfied with their everyday life, they have an entirely unrealistic ideal of life. They think they are special and unique and believe they can only be understood by special institutions or people. They will not agree to interact with people they consider inferior. They have an excessive need to be admired. This is an essential driving force in one’s professional or personal life.
The narcissistic people think that everything is owed to them. They unreasonably expect to receive particularly favorable treatment and that their desires will be automatically satisfied. They exploit others in their interpersonal relationships. They use others to achieve their ends and lacks empathy. They are unwilling to acknowledge or share the feelings and needs of others. They are also incapable of understanding what the other person feels in this situation. The narcissistic person displays arrogant and haughty attitudes and behaviors. They despise others and display a feeling of dominance.
Integrating AI presents opportunities in the battle against toxic leadership. Combined with reliable employee feedback systems, AI predictive analytics can uncover and counteract unhealthy leadership practices.
Rachid Alami
Dr. Rachid Alami is the Associate Dean of Graduate School for the MBA and Master in Information Technology Management Programs at Canadian University Dubai. Dr. Rachid obtained his PhD and DBA in Management Sciences from Paris Dauphine University. He is certified in Applied Machine Learning from MIT Boston and a certified executive coach from Cergy University Paris France. Dr. Rachid recently obtained a master’s degree in applied psychology from Anglia Ruskin University in Cambridge and a postgraduate certificate in Applied Research in Education from UCL London.
