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Incorporating Human Input in Artificial Intelligence

Incorporating Human Input in Artificial Intelligence

Artificial intelligence is increasingly becoming a core part of modern organizations. The technology is applied in different sectors, including healthcare, manufacturing, and customer service. As AI performs roles that humans traditionally perform, there is fear that it may replace human labor across the economy. Some companies incorporate AI to replace human labor, but an increasing body of research shows that such entities will attain short-term gains. In order to attain optimum benefits from artificial intelligence, there is a need to adopt collaborative intelligence, which combines human input and AI capabilities. Humans will provide creativity, social skills, leadership, and teamwork skills, whereas AI offers speed and quantitative scalability. Hire our assignment writing services in case your assignment is devastating you.

Background Information

Adopting AI into the mainstream economy means constant interaction between humans and AI will exist. However, one key issue emerging from the development is that there will be a need for high-level trust if AI is to be adopted successfully (Asan et al., 2020). Human interactions largely depend on trustworthiness; hence, there is a need to introduce a framework that bolsters trust. Notably, the use of AI in sensitive sectors such as healthcare, which means a matter of life and death, calls for utmost trust. AI is used for diagnosis and medical imaging, among other purposes. The goal is to overcome the nondeterministic AI algorithm, which means the technology may bring different results for the same input data at different times (Asan et al., 2020). Besides, human characteristics such as personal biases, educational level, and past experiences may influence their attitude toward AI. For instance, if a patient remembers an instance where AI provided a wrong diagnosis, it may be difficult for them to trust the technology again. Organizations looking forward to adopting AI ought to address the lack of trust occasioned by the nature and novelty of AI to facilitate successful human-AI interactions.

Supervisory control is also becoming important as the use of automated systems grows. Customers rely on AI to operate automated systems, but AI is only partially reliable, depending on the reputation of an organization and the nature of the online interaction (Cheng et al., 2019). A perfect example is the online fintech sector, which is characterized by uncertainty. Customers making financial investments through online systems may grow increasingly anxious due to the extent of such investments and the firms they interact with. There is a need to introduce a supervisory control system that allows humans to monitor how AI systems perform their functions to reduce the anxiety characterized by such investments (Cheng et al., 2019). The trust level increases when humans are allowed to monitor AI systems.

Another notable aspect as AI takes root is a need to apply the technology to perform beyond what humans can manage. Artificial intelligence is traditionally designed to perform what humans can do faster and with higher quantity scalability (Reddy, 2018). However, a large body of research suggests that collaborative AI can be applied to achieve better outcomes than humans alone. To understand this idea, the analogy of a bird comes in. While the design features of planes were primarily derived from a bird, human ingenuity has been applied to improve what a plane can do. The plane has better performance features beyond what is seen in a bird. To that end, humans can improve AI to the extent that it performs way more than humans could traditionally achieve (Reddy, 2018). For instance, AI-enabled devices may be used to improve patient diagnosis, such that the technology discovers a health issue before the patient feels any symptoms. To that end, AI will solve later diagnosis, a common problem among cancer patients.



There is a need to train machine learning algorithms to improve their interactions with humans. For instance, customer service AI algorithms need training in social intelligence to ensure optimal interaction with customers. Also, machine learning algorithms in the financial sector require extensive training with the objective of helping them make sound decisions based on reigning market conditions de (Nobrega & Rutkowski, 2022). For instance, Microsoft’s Cortana bot was extensively trained by humans to improve its personality. Due to such training, the bot expresses itself in a confident, caring, and helpful tone and is not bossy. Besides, Amazon’s Alexa and Apple’s Siri have received extensive training in improving human interactions. Apple’s Siri tends to express sassiness, a characteristic that Apple customers admire. Additionally, more customer service bots are trained to express sympathy when interacting with human customers. Training AI bots on social intelligence improves customer service and customer loyalty.


Another way collaborative intelligence may be practiced is by incorporating human explainers for AI-based decisions. Explainers are particularly useful in evidence-based industries like medicine, law, and law enforcement. AI makes decisions through opaque processes, which may prompt non-experts to seek further clarification (Juho, 2019). For instance, lawyers will chip in and explain the basis for such decisions if AI is used to deliver a judgment. Incorporating explainers solves the trust issues associated with AI and simultaneously alleviates the fear of human job losses due to AI adoption.


Thirdly, incorporating human sustainers is essential to avoid potential issues that may arise from AI applications. Sustainers are tasked with monitoring the engineering perspectives of AI. As mentioned above, the financial sector is quite sensitive and requires constant monitoring. In an age where cyberattacks are on the rise, sustainers are required to monitor AI systems for potential mistakes (Juho, 2019). In case sustainers notice abnormal activity, they can arrest it before it escalates and causes unnecessary losses. Sustainers may also be used to alleviate ethical issues associated with AI use (Juho, 2019). For instance, credit systems that rely on AI have previously been accused of discriminating against people from specific population segments. Sustainers will quickly notice such inconsistencies and take corrective action.


In conclusion, AI adoption is on the rise globally, with increased use in healthcare, customer service, manufacturing, and the services sector. However, it has emerged that AI systems will not replace human input but rather create a collaborative approach. That is because the sole reliance on AI has already proved to cause issues such as distrust, producing nondeterministic outcomes. There is a need to embrace collaborative AI, which allows humans and AI algorithms to collaborate in task performance and decision-making. Humans can train machine-learning algorithms with the objective of delivering effective outcomes. Also, humans can be used as explainers to explain AI-delivered outcomes to non-experts comprehensively. Another critical role humans will play is sustaining, which involves detecting and correcting potential engineering-related AI issues. As AI enhances speed and quantity scalability, humans will provide social intelligence, leadership, creativity, and team spirit to attain optimal outcomes.


Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians. Journal of Medical Internet Research, 22(6), e15154.

Cheng, X., Guo, F., Chen, J., Li, K., Zhang, Y., & Gao, P. (2019). Exploring the Trust Influencing Mechanism of Robo-Advisor Service: A Mixed Method Approach. Sustainability, 11(18), 4917.

de Nobrega, K. M., & Rutkowski, A. F. (2022, January). The AI Family: The Information            Security Managers Best Frenemy?. In HICSS (pp. 1-10).

Juho, V. (2019). Ethics of AI technologies and organizational roles: Who is accountable for the ethical conduct? In Conference on Technology Ethics.

Reddy, S. (2018). Use of Artificial Intelligence in Healthcare Delivery. EHealth – Making Health Care Smarter.


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Incorporating Human Input in Artificial Intelligence

Incorporating Human Input in Artificial Intelligence

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