Organizations are increasingly turning to artificial intelligence (AI) to support a variety of functions within their business, from delivering more personalized experiences to improving operations and productivity to assisting organizations in making better and faster decisions in today’s competitive labor market and hybrid work environment. IDC predicts that by 2024, the global market for AI software, hardware, and services would exceed $500 billion.
However, many businesses aren’t prepared—and shouldn’t—to have their AI systems operate autonomously and completely without human interaction.
Because AI technologies are so complicated, businesses frequently lack the necessary knowledge of the systems they employ. In other cases, basic AI is included in business software. These can be somewhat static and take away control over the data’s constraints, which is what most enterprises require. However, in order to minimize risks and maximize AI’s advantages, even the most AI-savvy firms continue to involve humans in the process.
Keep humans in the loop for obvious ethical, legal, and reputational concerns. Over time, inaccurate data can be introduced, which can result in bad decisions or, in certain cases, dire situations. Biases may potentially enter the system through the training of the AI model, changes to the training environment, or trending bias, in which the AI system responds more strongly to recent than to earlier behaviors. Furthermore, AI frequently fails to grasp the nuances of a moral choice.
Consider the healthcare industry. The sector exemplifies how AI and people can cooperate to improve outcomes or lead to serious harm if people are not fully involved in the decision-making process. For instance, AI is perfect for diagnosing or recommending a treatment plan to a doctor, who then assesses whether the recommendation is sound and then offers the patient advice.
It will be possible to prevent errors that could cause injury or catastrophe and to continuously train the models so they get better and better by having a way for people to monitor AI answers and accuracy. IDC anticipates that by 2022, more than 70% of G2000 organizations will have formal procedures in place to check their level of digital trustworthiness.
Conversational AI and Human-in-the-Loop (HitL) Reinforcement Learning are two examples of how human involvement helps AI systems make wiser judgments.
HitL enables AI systems to use machine learning to learn by watching humans interact with work and use cases from the real world. Similar to standard AI models, HitL models are constantly evolving and refining themselves depending on feedback from humans and, in certain situations, enhancing human interactions. It offers a controlled setting that reduces the likelihood of ingrained biases, such as the bandwagon effect, which can have disastrous effects, especially during key decision-making processes.