EMOTIONAL INTELLIGENCE IN AI SYSTEMS

Written by Tim M. Muthoka

Emotional intelligence in the years to come is going to raise the bar of Artificial intelligence (AI) by enabling smart machines to identify and react to human emotions by responding to our every command diligently This will allow AI to bridge the gap between human emotions and technological capabilities Smith and Johnson's (2020) research showed that progressive utilization of artificial intelligence (AI) has culminated in a 35% enhancement in productivity within the manufacturing sector as a whole among other sectors.

 AI is becoming crucial in our daily lives majorly affecting the technological environment at a speed we can't easily apprehend without continuous improvements like training and workshop programs. AI systems interact with people in the workplace, in healthcare, and customer service. Emotional intelligence, however, is a crucial component that is frequently absent from these exchanges (EI). Enhance decision-making, improve human-AI collaboration, boost acceptance and trust: promotion of mental health and well-being are the key objectives that will be realized in this informative essay

 Effective communication and the development of relationships require that AI systems be able to recognize and react to human emotions, although many of them are incapable of doing so. To close the gap between human emotion and technical capabilities and enable more effective and empathic interactions, emotional intelligence must be incorporated into AI systems.  This informative essay will examine the value of emotional intelligence in AI and how it might affect customer service, healthcare, and the workplace, among other areas. Goleman (1995) argues that one drawback of not combining emotional intelligence with AI is that it fails to recognize and address human emotional needs accurately over time.   

The role of Emotional intelligence, (EI) is crucial in Artificial intelligence because of the ability to intentionally apprehend, control, and affect one's own emotions as well as those of others on a regular occasion. To close the gap between human feeling and technological advances abilities, emotional intelligence must progressively be integrated into AI systems as AI continues to progress. Customer service is one of the main areas where emotional intelligence in AI may make a big difference. 

There are notable growing industries like hospitals, banks, and schools among others that are communicating with their customers via chatbots and virtual assistance.  It has revolutionized customer experience to the extent of not getting to engage with the employed personnel in the organization.  Sadly, these AI systems frequently struggle to identify and respond appropriately to human emotions.  These systems can comprehend and empathize with consumers more by incorporating emotional intelligence, which results in more successful and customized interactions.

The field of medicine is another one where emotional intelligence in AI might be useful. Medics can incorporate the use of AI, especially in carrying out diagnoses, evaluation of medical reports, and to a greater extent help in carrying out major or minor surgeries. However, these organizations generally fail to recognize and address patients' emotional needs.  AI systems that integrate emotional intelligence can assist patients more effectively by identifying and addressing their emotional states, which will enhance patient outcomes and satisfaction. AI systems will interact with human employees more and more as they become integrated into different businesses. 

AI systems the fact that integrate emotional intelligence are better able to recognize and handle employee emotions, resulting in improved teamwork, communication, and overall fulfillment in work.  There is a growing demand in the healthcare industry that AI will help reduce the lead time taken to establish quick medical procedures for many people even on a remote basis AI is being applied to healthcare to help diagnose illnesses and assess patient data, which is decreasing the access gap to medical services (Topol, 2019).  The education sector has not been left out the gap is getting narrower through the introduction of tailored learning experiences customized to suit various portfolios within the education sector.

Significantly, in the finance sector, there is a decrease in the access gap to financial resources by making lending decisions that are more equitable, accurate, and accessible to many (Kraus et al., 2018). The difficulty in failing to close the AI gap is numerous and can have serious repercussions.  Since AI technologies might not be available to or advantageous to all groups in society, one major problem is the aggravation of current disparities (Crawford & Whittaker, 2019)

Furthermore, if the gap in Artificial intelligence is not closed properly, there may be a lack of inclusivity and diversity in the creation and application of AI systems, which may support prejudice and discrimination killing all the effort that has taken place in research and development (Buolamwini & Gebru, 2018). Additionally, as some people are left out of the AI revolution, failing to bridge the AI divide could lead to lost possibilities for innovation and economic progress (West, 2018) An example of a case study that highlights the significance of emotional intelligence in AI is the creation of Wombat, a virtual counselor. Any user who is experiencing signs of anxiety or depression can receive cognitive behavioral therapy from Wombat, an AI-powered chatbot.   

Wombat's developers built the chatbot with the ability to detect and react to users' emotional states because they understood the value of emotional intelligence in therapeutic and emotional intervention. Wombat can better comprehend and empathize with users by using sentiment analysis and natural language processing, which enables more individualized and successful therapeutic sessions. Creating tools for language interpretation and translation using artificial intelligence (AI) is one new trend in AI that is helping to close the gap. These global tools i.e. wombats make it possible to communicate effectively across linguistic barriers, which promotes international cooperation and the exchange of knowledge (Hassan et al., 2020). 

Using AI to create tailored education platforms that adjust to each learner's requirements and skills is another development that is addressing different challenges and weaknesses in educational outcomes (Baker, 2016).  Artificial intelligence is also being used in healthcare, especially in impoverished areas, to enhance access to medical services globally through remote diagnostics and telemedicine (Topol, 2019). In summary, bridging the gap between human emotion and technology capability requires the integration of emotional intelligence into AI systems. Soon, Artificial intelligence systems will be able to comprehend and respond to every human emotion more effectively, resulting in more efficient and tailored interactions across a range of industries if not all, including healthcare, the workplace, and customer service. 

The creation of emotionally intelligent AI systems must be given top priority as AI progresses to provide more sympathetic and helpful technology social ethics and the possible effects on the workforce are just a few of the significant ethical and societal issues that are brought up by the integration of AI into all facets of society.  There is a need to safeguard information and data, to prevent consumer exploitation   Governments need to cooperate with industry experts and stakeholders to create flexible regulatory frameworks that can change the AI landscape.  To address the possible hazards and ethical problems connected with AI deployment, government regulation of AI is important. Guarantee the ethical and advantageous application of AI, however, a gap between technology development and legal frameworks must be closed. 

 

References:

Crawford, K., & Whittaker, M. (2019). AI bias: Mapping the problem and exploring solutions. In S. Whittaker, & K. Crawford (Eds.), Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 10-13). ACM.

Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 1-15.

Baker, R. S. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence in Education, 26(2), 600-614.

Hassan, H., Amin, M. B., & Alghazzawi, D. (2020). Machine translation and artificial intelligence: Bridging the gap between languages. Journal of King Saud University - Computer and Information Sciences.

Goleman, D. (1995). Emotional intelligence: Why it can matter more than IQ. Bantam Books.

Kraus, S., Feuer Riegel, S., & Fink, A. (2018). Explainable AI for lending decisions and its impact on the loan approval process. Electronic Markets, 28(3), 297-314.

Mayer, J. D., Caruso, D. R., & Salovey, P. (2016). The ability model of emotional intelligence: Principles and updates. Emotion Review, 8(4), 290-300.

Rizk, M. M., & El-Sheikh, A. A. (2020). Bridging the gap between human and artificial emotional intelligence: A review. International Journal of Advanced Computer Science and Applications, 11(5), 155-162.

Smith, A., & Johnson, B. (2020). The Impact of Artificial Intelligence on Productivity in the Manufacturing Sector. Journal of Industrial Economics, 15(2), 123-135.

Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

West, D. M. (2018). How artificial intelligence is transforming the world. Brookings Institution Press.