In this special guest feature, David Karandish, CEO of Capacity, suggests that while humans are teaching machines to consistently deliver the highest-quality results, we’re also filling in the gaps in the data collection along the way. Capacity is an enterprise AI company building a secure, AI-native support automation platform to help teams do their best work – and save time and money. Before founding Capacity, David was the CEO of Answers Corporation. David and his business partner started the parent company of Answers in 2006 and sold the company to a private equity firm in 2014 for north of $900m. David holds a bachelor’s degree in computer science and a second major in entrepreneurship from Washington University in St. Louis, where he graduated cum laude.
Data, most simply put, is a compilation of information. Both humans and machines collect data, then analyze the findings and turn them into valuable information to guide the decision-making process.
However, unlike their computer counterparts, humans have an innate ability to collect information through different processes at the same time. Just think of the way we use all five senses to understand our surroundings. As much as we’d like to believe machines can handle it all, this is why there is still a human hand in determining what information is relevant and what information should be disregarded.
Without a human in the loop, or HITL, the process can get out of hand. To give you an idea, let’s compare human interference in data collection as if it was an adult supervising kids at daycare; it’s still busy, but far less messy. The same goes for the process of collecting data. While bots have the capability to continue learning after the initial training wheels come off, they will never be able to take over the entire process.
Here’s why we still need human interference in data collection.
Processes Need Humans For Quality Assurance
The first step in your data collection process is to determine how you’re going to collect it. There are three primary ways: outsourcing, crowdsourcing, or in-house labeling. To avoid low-quality data, a lack of accountability and no vision of how the data will be applied once you’ve interpreted it, selecting the appropriate collection method is crucial. You have to understand the bigger picture.
To determine the best approach for your data collection, consider the pros and cons of each process:
- Crowdsourcing: Obtaining data by enlisting a large number of people to source information.
It is a popular way to categorize and label data because it is affordable and allows simple tasks to be delegated to various participants. The downside is that, with so many people involved, there is no guarantee for uniform collection processes. Here, humans play a role in teaching the computers the algorithms needed to collect and compile the data. - Outsourcing: Hiring a company that specializes in categorizing and labeling data to handle your data collection process.
While this can be an expensive option, outsourcing takes data collection off your team’s plate and provides an extra layer of expertise. That said, it’s possible that, in using a third-party source, they may not be able to offer insight into the findings. In outsourcing, a team also helps their computer counterparts collect the data by setting up the proper algorithms. Humans are also there to provide context around the findings. - In-house labeling: Tapping into your own data labelers who are familiar with the industries you serve to provide insight into how you can improve your data.
Of course, this is an affordable and effective way to collect reliable, relevant data, but not every company has its own data experts. Similar to outsourcing, in-house labeling requires involvement from a team (your team) to shape the algorithms so you’re left with the most accurate data.
Yes, machines play a role in each of these processes, but whether crowdsourcing, outsourcing, or using in-house labelers, humans significantly impact the quality of your data collection by ensuring computers are able to pull the best results.
Humans Provide Context
What’s more valuable than being able to produce an answer is being able to shape the conclusion. A huge part of training a machine to analyze data is breaking down the decision-making (or path to the conclusion) into smaller pieces.
In many ways, training a computer is like teaching a child. The difference is that machines are at a disadvantage since they can’t absorb multiple pieces of information at the same time like humans can through visual, audible, and even subconscious cues. One of the biggest hurdles for machine learning models is context.
For that reason, humans must be there to teach machines to replicate the complex ways we digest information. When it comes to context, the human in the loop can work alongside the computer to decipher what the user needs by pairing the available information with context.
Machines Learn From Human Actions
We’ve all heard the age-old saying: “monkey see, monkey do.” Similar to the way we learn by copying the behaviors of others, machine learning models need our guidance on how to behave. Since data can present itself in many different ways — like how we can come up with multiple ways to ask the same question — humans are there to ensure similar data points end up with the same results.
It is the HITL’s goal to train datasets and minimize errors. By taking a deeper look at the data, you can fine-tune results and validate their relevance to continually improve the process. By using tactics like sentiment analysis, where you interpret and classify emotional responses to uncover context around a data set, the computer can build off of the information to deliver the best possible end results.
While humans are teaching machines to consistently deliver the highest-quality results, we’re also filling in the gaps in the data collection along the way. Without a human in the loop, your machine learning models may pollute datasets and deliver poor results. The only way to ensure you receive the best results is for humans to work alongside the machine learning models in your data collection process.
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