Clicky Skip to main content
  • SHARE

We may all be wondering about the astounding results that modern AI tools like ChatGPT, Dall-E & lot more deliver. To provide such results, they should be backed with appropriate data on which machine learning models do the job of interpretation & explanation. But there are scenarios where there isn’t sufficient data available, and machines alone can’t do justice in providing a meaningful context from datasets for AI systems to provide rich results. This necessitates the involvement of humans in building AI systems with better confidence to deliver high-quality results.

What is Human-in-the-Loop?

In general, Human-in-the-loop refers to the involvement of humans in system processes to provide inputs. Under the AI Context, HITL is all about augmenting human intelligence with artificial intelligence to produce accurate results. In HITL, both humans & systems involve in back-and-forth communication, where humans train & tweak ML algorithms with their specific knowledge.

Take the development of a tumor-detecting AI system in the context of healthcare. Here, Cancer surgeons are the subject matter experts. They provide more detailed information about the scan images, which helps train ML models to detect cancers better. Then, using the supplied data, the system tries to spot cancers in scan images and shares the findings for validation with subject matter experts. To increase the accuracy, they evaluate the results and offer more feedback. This cycle keeps going to boost the AI system’s confidence.

Reasons Why Human-in-the-Loop is Important

Better Accuracy

Coupling humans with AI systems results in much-improved accuracy in results. Since the models can be supplemented with rich datasets and feedback like where it makes errors & what needs to be tweaked to perform better. There is room for continuous improvement, making them more reliable for decision-making amidst dynamic scenarios.

Improved Efficiency

Systems are found to be more efficient with human supervision than fully automated systems. With HITL, they can access more specific training data to produce results on time. AI Systems can also become more cost-efficient since they can prevent putting vital computing resources into processing & analyzing data that doesn’t make sense.

More Transparency

Human-in-the-Loop improves the explainability & accountability characteristics of AI systems. We can have extended visibility into what’s happening beneath the AI systems & why they produce specific results for the provided datasets. This visibility matters most in scenarios like macro-level financial planning, health diagnosis & more.

How does HITL help?

The human-in-the-Loop approach can be instrumental across the various stages of AI development. Here are a few vital areas where HITL can be best leveraged.

Data Labelling

This is a crucial and essential step in building machine learning models. Labeling is all about providing machine learning models with a context about the datasets they are about to process. Though automated tools are available to do that, human supervision will be highly instrumental in the case of labeling unstructured data sources like text, images, audio, video, etc.

Training & Testing

HITL improves the overall learning process, the essential stage of building accurate machine learning models. Machine learning algorithms can be trained with sufficient and appropriate information from human resources in dynamic scenarios. Later, the results are scrutinized against predicted results to find areas of improvement. Necessary fine-tunings are also identified to build them better than traditional algorithms.

Deployment

As the machine learning model evolves and matures, the production deployment eventually arrives. Despite enormous automation, this stage still requires human resources to ensure the readiness of the AI systems to provide rich results amidst real-time demands & edge-case scenarios. Post-deployment, humans can analyze results for a minimum threshold of accuracy & further develop the model.

Use-Cases

Social Media

Automated AI systems have limitations in effectively identifying abusive and explicit content in areas like social media. They can be equipped with human interaction, which provides the essential context & certainty about the content to classify them as inappropriate.

Entertainment

For entertainment companies, HITL can be used to analyze the audience’s likings & preferences and develop highly efficient recommendation engines that can suggest more personalized content to them.

Customer Service

In some cases, general chatbots cannot provide accurate results for edge-case scenarios as they mainly depend on statistical data. With HITL, subject matter experts can be deployed to assist chatbots in providing relevant results and better CX.

UI Design & Development

Product development teams can leverage the HITL approach in automating their UI design & development and deliver a personalized experience to their target users.

Computer Vision

Human-in-the-Loop can radically improve AI system capabilities to find visual patterns & provide meaningful information to make inferences in areas like healthcare diagnosis, geographic analysis & sports analytics.

Major challenges in implementing HITL

  • The unavoidable risk of human errors.
  • Biased inputs that could disrupt the credibility of the AI system.
  • The necessity to handle & sort out ethical dilemmas.
  • Possibilities for the slow pace of development due to human interference.
  • Manual Data Labeling which can be time-consuming.
  • Determining and maintaining the data quality thresholds to achieve desired results.
  • In the training phase, it could be challenging to track what works and what doesn’t to make the proper adjustments to the model.
  • Possibilities for reinventing the wheel sometimes which results in waste of resources.
  • Difficulty in integrating and scaling with existing workflows.
  • Difficulty in establishing and sustaining continuous improvement roadmap for the ML models.

Even though AI systems are evolving rapidly, they are still prone to errors, if not more, and they haven’t become certainly fool proof. They still need human interference to become a better version of themselves to provide accurate results. Amidst the ever-changing technological developments, approaches such as Human-in-the-Loop reminds us that machines & humans are interdependent and will coexist throughout the lifecycle of continuous innovation.

Leave a Reply

/* modal contact us form */