5 Things Your AI/ML Training Data Is Lacking

How can companies ensure that they are deploying AI that is inclusive and efficient? Erik Vogt, VP of Enterprise Solutions, Appen, shares five considerations that will help businesses understand what their data might be missing, and offer ideas on how businesses can fill in current dataset gaps.

For businesses to successfully deploy and leverage AI and machine learning models, they must have a repeatable, efficient, and scalable process for AI training. This may seem simple in theory; however, companies face challenges when deploying AI models due to several common mistakes. By identifying five key areas that are often common weaknesses for AI teams, business leaders can be sure they are training their AI models to create more equitable, safer, and efficient AI for all. 

1. Eliminating Bias

Unfortunately, socioeconomic and demographic biases are inherent in the data teams use when training and deploying AI models. Bias is, and always will be, a risk associated with all forms of data. Given that data originates from real human interactions, which carry biases in and of themselves, this is an unavoidable scenario. However, what differentiates top data science teams from the rest of the industry is understanding and managing biases in a way that mitigates risk. This complex issue requires AI practitioners to admit one thing: the most cost-effective solution on paper – removing humans from the AI training process – is not realistic. 

Advancing AI training requires businesses to thread a delicate needle: apply innovative human thinking to AI while accounting for human bias through alternative methods. Pairing this mindset with the value humans add to anti-bias efforts will allow data science teams to mitigate risk, collect data in a more representative manner, fill in under-represented groups, and do sampling that correctly weighs different groups.

One way in which bias can be accounted for is through the use of synthetic data. Rather than data that is pulled from real-world interactions, synthetic data is artificially manufactured information that, when used judiciously, can help create models that become less biased. By effectively combining human innovation and synthetic data, AI models will become less biased and offer a more holistic and accurate view of the world. 

2. Accounting for Edge Case Scenarios

Edge cases, or scenarios that do not yet have sufficient real-world data to account for, are another challenge practitioners face when training and deploying AI models. Think of how AI can help power a self-driving car; mountains of data are needed to prepare self-driving vehicles to hit the roads, but some more complex data is not as readily accessible. 

This is another scenario in which synthetic data can be immensely helpful. By applying edge case scenarios – consider rare, potentially dangerous circumstances such as accidents, pulling over for emergency vehicles, stopping for sudden-braking cars, crossing pedestrians, and other rapid-reaction scenarios in the aforementioned driverless car use case. Accounting for these scenarios when training their AI will allow businesses to enable their AI to account for even the most far-reaching scenarios.

3. Implementing Continuous Learning

The world around us is constantly evolving, so your AI models must do the same. By implementing continuous learning, AI and machine learning teams will ensure their AI models are not solely relying on outdated and often inaccurate data when deployed. The danger is that training and retraining AI models take time and effort that is not often readily available.

By implementing monitoring programs that evaluate when AI performance may deteriorate, data science teams can create a continuous feedback loop that alerts them on when it’s time or past to update training corpora. In turn, data scientists will find that their AI models are better prepared to adapt to a constantly changing and developing world.

4. Protecting User Privacy

A chief concern about AI is that it is often too intrusive for some consumers’ liking, leading to concerns about how user privacy will be protected in a world that is rapidly adapting artificial intelligence into day-to-day functions. One crucial step for AI and machine learning teams to alleviate some of these concerns is ensuring that their data is both scalable and secure.

The importance of protecting contributor data is both increasingly challenging and a business-critical issue that companies must address while scaling and deploying AI. The way companies handle contributor data will be an increasingly critical differentiator for data collection firms – especially, for example, in the European Union (EU), where GDPR establishes strict guidelines for companies that operate or collect data from the EU.

To protect privacy while also ensuring AI can be deployed at scale, businesses can use anonymous, pre-labeled datasets to meet both of these needs. Anonymized data can inherently protect user privacy and, when deployed in large amounts, ensure enough data is available to make accurate and well-informed AI models.

5. Having a Holistic Solution

Bringing together training data for AI and machine learning models is a tall task, as it often requires practitioners to collect data sets from several different regions, use cases, and scenarios. For example, creating AI models for speech recognition requires companies to pull together data from a wide range of regions, collecting datasets that reflect different accents, dialects, or speech impediments to serve the broadest number of users and, more importantly, not to discriminate by only working for a narrow demographic. Pulling this disparate data can prove challenging, but it can be made easier if businesses have the tools to bring all their data into one place.

To solve this, companies must seek technologies and partners that help bring together disparate data into a single source. If businesses can unite their data under a single source of truth, it will become more feasible to train, deploy, and implement new data into AI and machine learning models moving forward.

Identifying solutions for these five common problems is key for executives looking to create more robust solutions for training their AI models. Solving these problems will help eliminate bottlenecks in the AI training process and help businesses leveraging AI in machine learning become more operationally efficient in the future.

Which best practices have you followed to make your AI training data efficient? Share with us on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window . We’d love to know!

Image Source: Shutterstock

MORE ON TRAINING

• Using Virtual Reality To Improve Training Processes
• Upskilling at Scale: How Virtual Reality Is Revolutionizing Employee Training
• How to Best Engage Virtual Learners in a Corporate Training Event
• Training AI To Learn Like a Child: 3 Ways To Avoid Bad Habits