6 Most Common Errors When Implementing AI and Machine Learning
Guest contributorModelingposted by April Miller November 16, 2021 April Miller
Artificial intelligence and machine learning are steadily rising in popularity, but how can organizations and businesses avoid errors when implementing AI? New technology can be challenging to navigate at first, especially for organizations that aren’t digitally dextrous to begin with.
Artificial intelligence (AI) and its partner technology machine learning (ML) offer unprecedented opportunities for virtually every industry. Taking advantage of that capability can be easy if users know what pitfalls to look out for.
These are the most common issues and errors when implementing AI that businesses and organizations encounter when trying to implement AI and ML technology.
User-Related AI and ML Errors
AI and ML implementation errors generally fall into one of two categories: user-related errors or design-related errors. The first category, user-related errors, includes issues that arise out of improper or uninformed use of AI and ML technology.
1. Unclear Goals
One of the most common errors when implementing AI and ML is diving in without any definitive plan of attack. This can take a variety of forms. Some organizations attempt to use AI for its own sake, perhaps in an effort to be more technologically savvy. Others know what they want to use AI for but aren’t sure what they’re hoping to achieve through it. It may seem as though you have a goal — use AI to increase productivity, for example. While that is technically a “goal,” it’s so vague that it will be hard to gauge whether you are making progress or not.
A good method for defining goals for AI implementation is to use the SMART goal framework, which has become popular for all manner of aspirations. This goal-setting strategy will help your organization design actionable goals for your AI implementation strategy.
2. Insufficient Budget, Team, or Infrastructure
Many organizations have a good idea of what they hope to do with their AI model and how to go about doing it. When they go to put it into action, however, it simply isn’t performing at the level it was supposed to. This is often a case of insufficient resources of one kind or another. An organization may have a talented, knowledgeable AI specialist but lack the processing power the AI requires to operate at full capacity.
There are solutions to processing power shortcomings, luckily, such as infrastructure management optimization or cloud computing. Solving budget or staffing gaps, however, can be more difficult to achieve. Organizations in this position may want to consider using a pre-existing AI engine rather than designing their own or maybe outsourcing knowledgeable experts or processing infrastructure.
3. Incorrect Implementation Strategy
Sometimes, the reason an AI implementation fails is simply that it wasn’t being used the correct way. This may seem obvious, like attempting to use the wrong TV remote, but it isn’t so black-and-white. Incorrect AI and ML usage can take a variety of forms, from poorly structured datasets to misidentifying the solution that the AI is trying to find. Issues of this category tend to be rooted in a lack of thorough understanding of how AI and ML work or unclear objectives for implementation. Doing more research on both the technology and your organization’s goals can help to clear up these errors.
Design-Related AI and ML Errors
Design-related errors within AI and ML can be harder to identify since they are rooted in the way the AI is fundamentally built. Typically, these issues are due to the learning data the AI was given. In fact, AI training data is at the center of the industry’s most critical debates. It’s no wonder, either, since issues with training data can have far-reaching consequences.
While design-related errors may be more challenging to dig out, knowing what to look for can give you a place to start.
1. Biased Data
Perhaps the most hotly debated issue with AI is biased data. Researchers and engineers “train” AI by showing them large amounts of data and seeing how the AI reacts. Slowly, the AI learns to recognize and respond to data correctly. What some critics are beginning to point out, however, is that the specific data that an AI is trained with can actually produce an inherent bias within it.
For example, a deep-dive into the issue from WIRED describes an AI that is more likely to identify women in medicine as “nurses” and men as “doctors.” While this may not seem like a big deal on the surface, biased data can have a ripple effect that innately transforms the way an AI functions, the results it produces, and ultimately the human experience on the other side.
2. Tainted Data
Cybersecurity should be a top priority for organizations that want to utilize AI and ML. This is because of the covert threat of tainted data. When AI is being trained, a “back door” for hackers can be intuitively programmed in. This is done by teaching the AI to incorrectly respond to a specific “trigger” input that then causes it to break somehow, allowing a breach.
What makes this cyberthreat particularly dangerous is how stealthy it is. It can be exceptionally tricky to notice when a hacker has broken in through tainted data.
3. Insufficient or Incorrect Data
Not all design-related errors are due to cybersecurity threats or skewed data. Sometimes an AI underperforms or misbehaves because it wasn’t given enough training data or the data it was given wasn’t accurate. This issue may be slightly easier to pick up on. For example, it will be noticeable if an AI doesn’t understand input or returns odd results.
Design-related errors may sound intimidating, but avoiding them doesn’t have to be. Often it is a matter of finding a high-quality, trustworthy AI development team. Conducting background checks on AI engineers or outsourced AI developers may not be a bad idea for those who are concerned about hacking and tainted data. Hiring only highly experienced AI developers will help make sure your AI is trained properly, as well.
Innovation, the Right Way
Implementing AI and machine learning strategies in your business or organization can offer a plethora of benefits, from increased productivity to better customer service. The road to those results isn’t without its twists and turns, though. Navigating AI implementation successfully may take more research, resources, or trial and error than initially expected. Investing the time, resources, and effort will pay off handsomely in the long run, especially when you know what errors when implementing AI and pitfalls to keep an eye out for.