Artificial Intelligence has often been regarded highly because of its fundamental three abilities – speed, relevance, and accuracy. Vivid pictures of them taking over the world, replacing jobs, and fulfilling the automation goals of enterprises have often been painted online.
But let us give you another perspective. Some interesting AI tragedies that made news but not the buzz.
- A prominent Canadian airline had to pay for the damages caused by its AI bot for providing a user with misinformation at a crucial time.
- An AI model of a teaching solution company autonomously rejected specific applicants because of their age.
- Instances of ChatGPT hallucinating court cases that never existed surfaced during a trial when a document submitted by an attorney was examined.
- Prominent machine learning models designed to predict and detect COVID-19 cases in triaging during the pandemic detected everything but the intended virus.
Instances like these might come across as humorous and a reminder of the fact that AI is not without its flaws. But the essence of the topic is that such errors reflect a critical aspect in the AI development and deployment ecosystem – HITL or human-in-the-loop.
In today’s article, we will explore what this means, the significance it holds, and the direct impact AI training has in refining models.
What Does Human-in-the-loop Mean In The Context Of AI?
Whenever we mention an AI-driven world, we immediately envision humans being replaced by bots, robots, and smart equipment in Industry 4.0 setups. This is only partially true as humans in the front end will be replaced by AI models, meaning their increased criticality at the back end.
The real-world examples we started the writeup with direct us to one inference – the lack of training of models, or poor quality assurance protocols during the AI training stage. As we know for a fact that AI model accuracy is directly proportional to the quality of training datasets and stringent validation practices, a blend is essential for models to not just function properly but consistently build on their flaws and optimize for better outcomes.
It is exactly when an AI model fumbles with its intended purposes is where AI reliability gap originates. However, like how duality is the very crux of nature and everything around us, this is also where HITL becomes inevitable.
The Meaning
AI models are powerful yet infallible. They are prone to several concerns and bottlenecks such as:
Data limitations
where the lack of availability of quality training datasets restricts models from learning as efficiently as they are supposed to
Algorithmic biases
introduced voluntarily or involuntarily due to the input of one-sided datasets or flaws in their very codes and models
Unforeseen Scenarios
that involve exceptions and technical glitches experts and stakeholders cannot predict or even think of resulting in fresh corrective measures from surfacing inferences and more
In the AI development ecosystem, specifically, the AI model training phase, it is the responsibility of humans to detect and mitigate such concerns and pave the way for seamless learning and performance of models. Let’s further break down the responsibilities of humans.
Human-enabled Strategic Approaches To Fixing AI Reliability Gaps
The Deployment Of Specialists
It is on stakeholders to identify a model’s flaws and fix them. Humans in the form of SMEs or specialists are critical in ensuring intricate details are addressed. For instance, when training a healthcare model for medical imaging, specialists from the spectrum such as radiologists, CT scan technicians, and others must be part of the quality assurance projects to flag and approve results from models.
The Need For Contextual Annotation
AI model training is nothing without annotated data. As we know, data annotation adds context and meaning to the data that is being fed, enabling machines to understand the different elements in a dataset – be it videos, images, or just text. Humans are responsible for providing AI models with such context through annotations, dataset curation, and more.
The XAI Mandate
AI models are analytical and partially rational. But they are not emotional. And abstract concepts like ethics, responsibilities, and fairness incline more toward emotional tangents. This is why human expertise in AI training phases is essential to ensure the elimination of bias and prevent discrimination.
Model Performance Optimization
While concepts like reinforced learning exist in AI training, most models are deployed to make the lives of humans easier and simpler. In implementations such as healthcare, automotive, or fintech, the role of humans is crucial as it often deals with the sensitivity of life and death. The more humans are involved in the training ecosystem, the better and more ethical models perform and deliver outcomes.
The Way Forward
Keeping humans in model monitoring and training phases is reassuring and rewarding. However, the challenge arises during the implementation phase. Often, enterprises fail to find specific SMEs or match the volume requirements of humans when it comes to at-scale capabilities.
In such cases, the simplest alternative is to collaborate with a trusted AI training data provider such as Shaip. Our expert services involve not only ethical sourcing of training data but also stringent quality assurance methodologies. This enables us to deliver precision and high-quality datasets for your niche requirements.
For every project we work on, we handpick SMEs and experts from relevant streams and industries to ensure airtight annotation of data. Our assurance policies are also uniform across the different formats of datasets required.
To source premium-quality AI training data for your projects, we recommend getting in touch with us today.
