Artificial intelligence (AI) systems are on the rise, and AI is being integrated into various aspects of our lives. However, as these systems become more complex and automated, there are growing concerns about their accountability, transparency, and bias.
One solution to address these challenges is Human-in-the-Loop (HITL) systems, where human input and oversight are incorporated into the AI development process.
If you’re interested in developing AI and machine learning (ML) responsibly, understanding how HITL works is crucial. That’s why at CareerFoundry we include it as a central part of our Machine Learning Specialization Course, as well as in our teaching of AI tools in our programs.
In this article, I’ll introduce you to this concept, including:
- What is Human-in-the-Loop?
- Benefits of Human-in-the-Loop
- Challenges of Human-in-the-Loop
- How data analysts and scientists can use Human-in-the-Loop
- Final thoughts
- Human-in-the-Loop FAQ
1. What is Human-in-the-Loop?
Human-in-the-Loop, often abbreviated as HITL, is an approach that involves human input and oversight in the development and operation of AI and ML systems.
Put simply, it means that humans are “in the loop” or actively involved in the decision-making process of these systems.
A 2022 survey of HITL for machine learning shows that HITL can be used widely in computer vision, natural language processing, and data processing.
This can include tasks such as:
- Data annotation
- Model validation
- Monitoring for bias
- Providing feedback and corrections
- Making final decisions based on AI recommendations
The level of human involvement can vary depending on the system’s complexity and its intended use. In some cases, humans may only be involved in the initial stages of development, while others may require continuous input from humans throughout their operation.
The importance of Human-in-the-Loop in machine learning
Now, let’s consider how this all fits into the applications of machine learning.
In traditional machine learning systems, data is fed into an algorithm which then produces a model that can make predictions or decisions.
However, without human involvement and oversight, these machine learning models may be biased or make incorrect decisions based on incomplete or flawed data.
Incorporating HITL into the process allows for continuous human input and monitoring, ensuring that the system is producing accurate and fair results.
Human-in-the-Loop vs Human-out-of-the-Loop
In modern AI model frameworks, Human-in-the-Loop vs Human-out-of-the-Loop is always considered.
Here’s a simple answer about their differences:
Human-out-of-the-Loop systems are fully automated and do not require any human input or oversight, which can lead to potential issues with accountability, transparency, and bias.
On the other hand, Human-in-the-Loop systems incorporate human input and oversight, ensuring that any issues are addressed and mitigated.
Human-in-the-Loop examples
Some common examples of Human-in-the-Loop systems include:
- Customer service chatbots: Chatbots are used to provide basic customer support and according to Gartner will continue to gain popularity over the next five years. However, when a complex issue arises or the chatbot cannot understand the query, a human agent can take over.
- Medical diagnosis systems: Doctors review and validate the decisions made by AI models for chest radiograph diagnosis.
- Autonomous vehicles: Self-driving cars like such as Tesla use AI and sensors to navigate roads, but they still require human intervention in certain situations.
These are just a few examples, and HITL can be applied in various industries and use cases.
Stay tuned for our next section on the benefits of Human-in-the-Loop in AI and ML to see why this approach is gaining popularity.
2. Benefits of Human-in-the-Loop
Now, on to the benefits: why is Human-in-the-Loop becoming increasingly important in AI and ML development?
1. Improved accuracy
One of the main benefits of incorporating human input into AI systems is improved accuracy.
Human oversight can help catch errors or biases in the data, ensuring that the system’s decisions are based on accurate and reliable information.
Additionally, humans can provide feedback and corrections to improve the system’s performance, leading to more accurate results.
2. Increased transparency
Transparency is crucial for building trust in AI systems. When humans are “in the loop,” they can understand how decisions are being made and ensure that the system is not making biased or unfair decisions.
This also allows for better explanations of how a decision was reached, which is particularly important in highly regulated industries like finance or healthcare.
This is also a key element in keeping to AI regulations set out by the AI Bill of Rights, which ensures AI systems are developed responsibly for humans.
3. Reduced bias
AI systems are only as good as the data they are fed, and unfortunately, data can be biased due to human error or historical inequalities.
By incorporating Human-in-the-Loop, you can continuously monitor for bias and make corrections when necessary. This results in more fair and ethical AI systems. This is in line with the Algorithmic Discrimination Protections section of the AI Bill of Rights.
4. Better efficiency
One might argue that having humans involved in the process could slow things down, but in reality, it can lead to better efficiency.
For example, in data annotation, humans can quickly identify and label patterns that may be challenging for machines to detect. This reduces the amount of time and resources needed for model training.
Moreover, by catching errors early on, it can save time and resources in the long run by avoiding costly mistakes or inaccurate results.
3. Challenges of Human-in-the-Loop
While there are many benefits to incorporating Human-in-the-Loop, there are also challenges that come with this approach.
1. Cost and resources
Having humans involved in the process means additional costs and resources, such as hiring data annotators or having a team of experts continuously monitoring the system.
This can be challenging for smaller companies or startups with limited budgets.
For example, with the recent emergence of large language models (LLMs), such as OpenAI’s ChatGPT, the cost of training and maintaining these models is mostly only open to large organizations with huge funding and backing for their research and development.
2. Human error
While humans can catch errors or biases in data, they are also susceptible to making mistakes themselves.
This is why it’s crucial to have proper checks and balances in place and continuously monitor for human error.
3. Difficulty scaling
As AI systems become more complex and require larger datasets, it can be challenging to scale the Human-in-the-Loop approach.
This is why it’s essential to have a systematic and efficient process in place for incorporating human input and feedback into the system.
4. How data analysts and scientists can use Human-in-the-Loop
Data analysts and scientists play a crucial role in the Human-in-the-Loop approach.
They are responsible for designing, developing, and monitoring AI systems and working closely with human annotators or experts.
If you’re a data professional, here are some tips to incorporate HITL into your work:
- Collaborate with domain experts to ensure data quality and accuracy
- Continuously monitor models for bias or errors and make necessary adjustments
5. Final thoughts
When working with machine learning models, it’s crucial to understand that humans and machines each have their strengths and limitations.
This is especially so for data professionals, who have a critical role in designing and monitoring AI systems.
Combining AI’s power with human oversight can create more accurate, transparent, and ethical systems.
As AI advances and becomes more integrated into our daily lives, incorporating Human-in-the-Loop will become increasingly important for ensuring its safe and responsible use.
Are you thinking of getting a good foundation in machine learning? You might like CareerFoundry’s free five-day data analytics short course.
If you’re interested in machine learning, do check out other related articles:
- What’s the Difference Between Machine Learning and Deep Learning?
- Expert Interview: Beyond the Buzzword – Understanding the Ethical Implications of AI
- 12 Machine Learning Skills to Power Your Career
6. Human-in-the-Loop FAQ
What is AI governance?
AI governance refers to the process of managing and regulating artificial intelligence systems, including the data used for training and decision-making algorithms. It involves setting ethical standards, ensuring transparency and accountability, and safeguarding against potential biases or harmful outcomes.
Human-in-the-Loop is often considered an essential component of AI governance.
Does Human-in-the-Loop mean humans are controlling the AI?
No, Human-in-the-Loop does not mean that humans have complete control over the AI system. Instead, it refers to incorporating human input and oversight into the development and decision-making processes to improve accuracy, transparency, and fairness.
How can Human-in-the-Loop be integrated into existing AI systems?
The integration of Human-in-the-Loop will depend on the specific use case and system. However, some common approaches include:
- Adding human annotation or review steps in data preprocessing.
- Creating feedback loops between humans and AI models to continuously improve performance.
- Designing systems that allow for human intervention in certain situations.
- Regularly audit and monitor AI systems for bias or errors.