What is Human-in-the-Loop (HITL), and Why Does it Matter?

Humans are, no doubt, critical for the training and development of AI models. However, the concept of human-in-the-loop (HITL) goes much further because human-AI collaboration is vital in every use case to mitigate risk and maximize the opportunity of this technology. 

Generative AI is not artificial general intelligence (AGI), not yet. That means, in most cases, its output or activities aren’t better than capable human output. AI can perform certain tasks quickly, competently, and, in some cases, expertly, but the best outputs arrive with human feedback and iteration.

First, we’ll define HITL from a development perspective, then discuss how it applies to using an AI application in the workplace or using a custom GPT chatbot. 

What Does Human-in-the-Loop (HITL) Mean?

HITL is a concept developed with machine learning and AI. In it, human experts perform tasks like data labeling and providing feedback on output to train and improve models. A continuous loop of output-feedback-output is critical to their effective design. 

In 2017, OpenAI published “Learning from human preferences” as an example of the “work done by OpenAI’s safety team.” In the post, the learning algorithm OpenAI was training required 900 “bits” of “feedback from a human evaluator” to learn to backflip. It’s an example that illustrates how models are “trained” and improved. 

Why Does HITL Matter to AI Users?

For the purpose of this article, we’re assuming you’re not developing a model but instead using AI, either a public platform, an off-the-shelf model, or a CustomGPT chatbot. 

The term HITL is now also being used to describe human-AI collaboration when using AI for tasks and output. Broadly interpreted, the premise is that humans are required to request or develop output, automation, and so on, and provide oversight to ensure outputs are of the best quality and accuracy and to mitigate any potential risks of AI. 

McKinsey, after US media day “Halftime Report: A Mid-Year Update on the CEO Agenda,” published an update titled, “A human in the loop is critical” after the gathering of journalists and leaders which discussed generative AI as well as the future of work and the crucial role of productivity to drive economic growth. 

“For most generative AI insights, a human must interpret them to have impact. The notion of a human in the loop is critical.”

-Alex Singla, McKinsey senior partner and QuantumBlack leader

McKinsey CTO Jacky Wright noted that AI’s ability to take over simple and repetitive tasks opened up possibilities for workers “to engage in more creative or cognitively focused pursuits.” And, new roles created by AI would include ones focused on how to “best employ generative AI.”

Udo Sglavo, VP of SAS Analytics, describes HITL as “mission critical,” adding:

“In the ever-evolving landscape of artificial intelligence (AI), the ‘Human in the Loop’ (HITL) paradigm has emerged as a pivotal force, spotlighting the essential collaboration between advanced algorithms and human expertise.”

HITL capitalizes on the strengths of humans and AI but also nurtures trust, per Sglavo. Acknowledging human responsibility and accountability is a paramount consideration for AI. 

“As generative AI evolves and undertakes more intricate tasks, the human expert serves as a critical overseer, assuring that decisions align with ethical standards and societal values.”

HITL is not just about ethics and risk, although those are essential factors. It is about quality, relevancy, and accuracy, providing human customer service and sales where needed and adding the qualities AI lacks, such as emotional intelligence, reasoning, advanced problem-solving, nuanced decision-making, and so forth. 

Becoming HITL – the Overseer

AI isn’t a tool that can be added to a tech stack and left to be used freely, like a new productivity platform five or so years ago. Its impact, risks, and benefits are all too great to leave unmanaged. 

Implementing AI requires a careful strategy, just like any business transformation. This strategy will include alignment to organizational goals, budget consideration, the capability of technology infrastructure, integration with systems and processes, AI’s purpose, the business size, specific risk and compliance analysis, data preparation, and more besides. 

One of the most critical aspects of AI adoption, however, is the human side. This includes reassuring team members that AI is not a threat to their jobs and teaching them how to use new technologies effectively and safely. 

It’s also very likely that organizations will need to evaluate and even reinvent job roles and workflows to account for new processes, efficiencies, and time savings. Part of this reinvention is for human workers and leaders to become the HITL wherever necessary. 

For human workers, effective human-AI collaboration can consist of the following:

  • Understanding relevant AI system capabilities and limitations
  • Knowing AI risks and how to identify and mitigate problems
  • Protecting the interests of colleagues, organization, stakeholders, and customers
  • Leveraging and/or safeguarding data 
  • Learning how to generate the best output from AI, including prompts
  • Overseeing AI activities and outputs
  • Accountability and responsibility for AI’s “work”
  • Utilising time saved for expert and creative tasks

Being the HITL with a CustomGPT.ai Chatbot

In future posts, we’ll consider HITL for several scenarios. But we’ll start with a basic example of HITL with a CustomGPT.ai bot. 

Firstly, creating a CustomGPT.ai bot requires human input to determine the data the bot will use to formulate its answers. Creating a CustomGPT.ai bot is a fast (think minutes) zero-code experience, and CustomGPT.ai bots have guardrails, meaning they use ChatGPT functionality but can be populated with just relevant business data to prevent hallucinations. 

Then, the bot needs specific instructions to customize its behaviour and style, its persona. Following that, it’s a simple process of testing and refining to ensure the bot will answer user requests effectively. 

In the case of a CustomGPT.ai chatbot for customer service, the last elements of HITL are being available to action customer questions and problems escalated past the bot, action any potential leads and further business opportunities, and continuously monitor a bot’s effectiveness, customer satisfaction, and the goldmine of customer intelligence evident from a CustomGPT’s chat logs.

https://www.techopedia.com/definition/human-in-the-loop-hitl
https://www.supportninja.com/articles/enhancing-customer-experience-with-human-in-the-loop-hitl
https://aibusiness.com/ml/human-in-the-loop-mission-critical-for-ai-usage-and-evaluation
https://www.mckinsey.com/about-us/new-at-mckinsey-blog/keep-the-human-in-the-loop
https://research.aimultiple.com/human-in-the-loop
https://levity.ai/blog/human-in-the-loop
https://openai.com/research/learning-from-human-preferences
https://www.mckinsey.com/about-us/new-at-mckinsey-blog/keep-the-human-in-the-loop
https://russewell.medium.com/the-human-ai-collaboration-how-humans-and-machines-are-working-together-e2cdc9b36a2f
https://community.pmi.org/blog-post/76431/human-in-the-loop-what-project-managers-need-to-know/#_=_

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