The Growing Importance of Ethical Data Labeling

AI systems don’t run on computer knowledge alone. Humans in the loop must train them to ensure that they function properly, identify patterns and objects, and help us automate our daily lives. That training is done by people who filter through millions of images and pieces of data to both label the content and teach the AI models. As such, data labeling plays a critical role.

Consider driverless cars—how does a driverless car know how to identify a red light or a green light? What is a traffic sign on the side of the road, or what is a pedestrian to be cautious of? Autonomous vehicles do not understand these nuances intuitively. They must be trained by people who manually label a large pool of images to help the AI identify patterns and trigger their next action or actions accordingly: turning, slowing down, stopping, or proceeding on the road.

Who is behind this data training? Hundreds of thousands of people all over the world. In 2020, global data collection and labeling was already a $1.3 million market and is projected to continue to expand at a rate of over 25% per year. Billions of people rely on data labelers every day to ensure their interactions with social media, online search, voice assistants, job searches, and health care diagnoses are not only effective and accurate but also safe.

While data labeling is a rapidly growing field that provides steady employment opportunities to individuals all over the world, there are situations in which conditions are less than ideal for the people who are doing this work. It’s up to all businesses to ensure the data labeling they employ is being conducted in an ethical way through safe, sustainable, and fair employment practices.

There are two key hallmarks of ethical data labeling. Diversity and inclusion in data labeling ensures that the data represents all individuals and cultures affected by the AI models and fairness and dignity for data labelers themselves as they complete the work.

See also: Algorithmic Destruction: The Ultimate Consequences of Improper Data Use

Diversity and inclusion in data labeling

A common concern about AI models is the biases that are inadvertently introduced by the people who train them. These biases can have a vast, real-world impact on people and industries that increasingly rely on AI to complete tasks like helping pair job seekers with open roles or identifying and diagnosing diseases.

When businesses ensure diversity and inclusion among the team that has trained the models through data labeling, they can also ensure inherent biases resulting from differences in language, race, culture, age, or gender will not be instilled in the AI they use. Diverse data sets ultimately protect all users from discrimination or false representation.

Fairness and dignity for data labelers

An unfortunate result of a rapidly expanding data labeling industry is that it can sometimes result in poor working conditions such as low pay, insecure employment, lack of support from employers, and little opportunity for career advancement.

As some of the most innovative and impactful companies in the world rely on data labeling to power their businesses, they must also ensure that they are partnering with ethical data labeling service providers that connect people all over the world with opportunities that provide living wages and dignified working conditions.

If your business relies on automated tools and practices, take a moment to reflect on the full supply chain that results in that automation and the human touch that makes it possible. One must always remember that ethical data labeling goes beyond diverse representation in both the businesses’ and providers’ supply chain; it provides a better customer experience and positively impacts and transforms the industry as a whole.

For data labeling to be ethical and truly successful, organizations need to be responsible and accountable in ensuring that everybody, especially the people behind the tech, feel that they are involved in the world they are creating.