Automated Scans vs Machine Learning: 6 Tips on Choosing the Right Accessibility Solutions

There has been a huge dilemma among digital content owners regarding which accessibility testing solution is best between automated scans and machine learning. Selecting the best solutions should help your digital content be usable and inclusive for everyone, including people with disabilities. Here are the six most influential tips you need to know to guide you to make informed decisions:

  • Understand Your Specific Needs

Your specific needs regarding your digital content should guide you toward the right accessibility solution. With multiple choices, you can opt for a Siteimprove vs Monsido comparison, two of the best accessibility solution tools. Understand that automated scans, like Siteimprove, are the best when it comes to swiftly pinpointing fundamental accessibility shortcomings through predefined tests. 

These accessibility solutions will find common issues like missing alt text, irregular heading structures, and HTML markup irregularities. They are useful for quickly checking the basic parts of a website or app without necessarily going too deep. On the other hand, machine learning systems, like Monsido, are more sophisticated and can spot complex or subtle accessibility problems that automated scans might miss. 

Their performance is bound to improve with time. Making them appropriate for working with a complex app or needing a detailed evaluation. By considering what you need the systems for, you can choose the right tools that fit your accessibility goals perfectly. 

  • Consider the Type of Content

The type of content you are dealing with plays a crucial role when choosing between automated scans and machine learning systems for accessibility testing. Automated scans are the best tools for the work when dealing with static content like straightforward web pages with limited interactive elements. 

They efficiently identify common accessibility issues in this context. However, if you are dealing with dynamic and interactive content like web applications, you need to consider the sophistication of machine learning. This is because, unlike automated scans, machine learning systems can access situations where user interactions and context play a substantial role in accessibility. 

They are adept at comprehending the intricate relationships between elements, making them more effective in detecting nuanced accessibility challenges. In that case, you need a tool that aligns with the content you are dealing with. 

  • Flexibility and Customization

Opting for a more flexible and customizable tool is the best option in the digital content realm. Automated scans have some limitations when it comes to this. This is because they typically operate based on predefined rules and offer limited room for customization. However, when you choose machine learning, you can be more adaptable. Machine learning can be customized to meet your requirements and adjust to unique contexts. 

  • Evaluate False Positives and Negatives

Each tool of the two has its own strengths and weaknesses when it comes to evaluating false positives and negatives in accessibility testing. Automated scans are efficient in identifying common accessibility issues. However, they can sometimes generate false positives, indicating a problem that does not exist. They may also overlook specific accessibility problems. 

On the other hand, machine learning tools operate per the quality of their training data. With better training data, they can generate fewer false positives and other issues that automated scans may have missed in their analysis. In return, this yields more accurate assessments of accessibility. Therefore, you generate the evaluation of the false positives and negatives to avoid being frustrated by the results. A better tool will save you time and hasten the development process. So, emphasize the importance of balancing accuracy and efficiency in your accessibility testing approach.

  • Integration with Existing Workflow

You need a tool that is easier to integrate into your workflow and one that can be easily taught to your workers. In that case, you need to understand that automated scans blend effortlessly with continuous integration and delivery pipelines. This makes them a natural fit for development workflows. Moreover, they can be easily incorporated, enabling a streamlined and automated approach to accessibility testing. However, integrating machine learning solutions requires a little bit of effort. This is true, especially if custom models need to be trained to meet specific requirements. 

This process can be more intricate and time-consuming than automated scans’ straightforward integration. Assess how each solution aligns with the established workflows of your team. If your team already has well-defined processes, opt for automated scans as they are more convenient for their simplicity.

  • Long-Term Considerations

When it comes to long-term considerations, automated scans offer stability and come with a clearly defined set of features. They can provide consistent performance over time without needing continuous adjustments. Machine learning, on the other hand, requires regular updates and training to meet your long-term needs. With proper training, they can maintain and uphold accuracy and effectiveness.

The decision to select the best accessibility tool between automated scans and machine learning is shaped by the above reasons. Taking into consideration all of the above factors can help you make a decision that will not only make your content accessible but also save you money, time, and other resources. Additionally, with a better tool, you can avoid frustrations associated with complexity and unknown errors. Again, you need to ensure you can get a tool that you or your workers can learn with and understand easily.