Win the Accessibility Game: Combining AI with Human Judgment

There have been many instances when Artificial Intelligence was at the crossroads of trust and peril. It becomes a serious issue when people with disabilities are involved. At one end, AI promises greater inclusion by breaking accessibility barriers and promoting autonomy. On the other hand, it has a higher possibility of misuse, exclusion, and can have bias issues in terms of accessibility. However, the AI transformation potential is much greater when reimagining the world, including specially-abled people.

For example, a blind individual navigating the streets by using AI-driven aid tailored to his/her cognitive patterns. This is not just a dream; this is becoming a reality with AI. But how are we going to ensure the smooth working of such gadgets in terms of accessibility? The answer lies with Accessibility testing. Then another question pops up – What role is AI going to play in accessibility testing and remediation? That’s what we will be figuring out in this blog today.

What is the Role of AI in Accessibility Testing and Remediation?

AI is changing the way software development processes work, and accessibility testing is no exception. With the recent advancements in technology (Metaverse, Autonomous Vehicles, etc.), the digital world demands more inclusivity and accessibility. Due to this, businesses are using AI-powered tools to identify and remediate the accessibility barriers that hinder their progress. From detecting accessibility issues (color contrast, keyboard navigation, content visibility, and screen reader compatibility) to automating complex test cases, AI is transforming how businesses approach accessibility testing.

Now, what if accessibility remediation is automated? Well, an automated remediation will use AI- and ML-powered tools to automatically identify and fix accessibility issues in a web app, mobile app, or computer software. This allows companies to free their development and testing hours for more important and complex issues requiring human expertise. But how does this whole process work?

Organizations use automated scanning and monitoring tools to detect accessibility issues. They run regular test scans for cross-browser compatibility checks, run performance monitoring, validate code, and conduct regression testing over and over again. The end goal is to analyze the context, understand its structure, and identify areas that require immediate remediation. Although AI can handle most of the routine tasks, there’s still a need for human expertise for complex accessibility challenges.

Challenges of AI-Driven Automation in Accessibility

Despite the benefits AI delivers, there are still some issues associated with its workings. The ethical considerations and context-specific issues are just a few examples that require human intervention. There are plenty of instances where AI alone will not be the best fit for accessibility. Let’s take a look at some:

• AI needs a huge amount of user data to perform effectively. This makes it critical to handle data responsibly and securely to protect user privacy, which, if left for AI alone, could be a challenge.
• There are chances of biased data being used unethically or unintentionally when training AI systems. For instance, speech recognition software may not work correctly if the AI is not trained with diverse data.
• Indeed, AI can automatically address various accessibility issues, but certain issues, like ensuring language clarity or semantic structure, would require a human touch to resolve them.
• Being overly dependent on AI can cause a false sense of security, which is not good for achieving accessibility compliance.
• Although AI can replicate basic human interactions, it would face a challenge with complex and dynamic web applications that heavily rely on custom-built JavaScript, requiring manual testing.

Why Human-in-the-Loop Matters in Accessibility Testing?

According to a study, AI tools improve accessibility issues detection by 30% compared to manual testing. Yes, implementing AI in accessibility testing is cost-effective and faster. It does reduce manual rework and help improve user experience (UX), but in the long run, depending solely on AI, can backfire.

Although there are dozens of benefits of automating accessibility remediation with AI, human expertise and judgment are still core aspects for comprehensive testing. Several factors support the human-in-the-loop approach, such as:

User Behavior is Unpredictable:

Automated test scripts follow a pre-defined pattern. However, in reality, users interact with applications in various ways. A specially-abled user might view a web application differently compared to a normal user. AI lacks a dynamic approach to assume such scenarios. Human testers can work on these edge cases to identify and remediate potential usability issues.

Ethical Accessibility Considerations:

Accessibility testing requires adherence to laws like ADA Title II, WCAG 2.0, 2.1, and 2.2 (Level A, AA, and AAA), and various country-specific laws. Human QA experts can use their years of experience and judgment to understand and evaluate accessibility aspects more sincerely compared to AI.

AI Is a Support Tool:

AI-driven solutions are meant to augment human capabilities, not replace them. AI can provide deeper insights and predictions, which human testers can use to understand the full potential of issues and run remediation measures accordingly.

Prevent AI Bias:

AI models are developed using historical data, which sometimes can lead to societal inequality and negative stereotypes. Human insight would prevent such issues and help businesses develop bias-free software/applications.

Enhanced Accuracy:

Human-driven fine-tuning would make AI algorithms more consistent and accurate to test the various accessibility scenarios. One thing everyone should understand is that automation is possible to a certain degree. Human-in-the-loop is essential to flag false positives missed by AI, or to interpret things in multilingual text, etc.

How to Select an Accessibility Partner with AI Capabilities?

An accessibility partner can offer technology to test for, remediate, analyze, and report on the user experience against digital accessibility standards. Its services would include accessibility SLAs, audits, employee training, legal expertise, and consulting. Apart from these, there is a series of questions that you should ask when selecting an AI-driven accessibility testing partner:

In addition to AI-driven tools, methodologies, and technologies, what else will be included in the accessibility testing portfolio? (The vendor must mention manual testing and using assistive technologies for testing.)

• What will be the roadmap to making digital experiences accessible to all?
• Which industry and clients have you worked with, and what type of accessibility experiences have you helped them deliver?
• Do you involve people with disabilities in accessibility testing as well when shaping digital products?
• How will you ensure the implementation of a hybrid approach (AI + Human-in-the-loop) for accessibility testing?
• Do you have any in-house products to test the effectiveness of the test scenarios?
• How much experience do you have working with AI-driven automated QA solutions?

Why Select Tx for Accessibility Testing Solutions?

AI is upscaling accessibility testing by making it cost-effective, precise, and efficient. It will help enterprises make sure their products are accessible to every user and promote digital inclusivity. However, human intelligence still plays a big role in guiding AI towards the right direction. We at Tx adopt a hybrid approach to accessibility testing by combining our AI-driven solutions with a human-in-the-loop approach. Our accessibility testing approach covers the following:

• AI-driven automated audits
• Manual accessibility testing
• Hardware testing
• Reducing false positives and negatives
• Interaction Testing
• Usability Testing
• Human expertise for subjective decision-making
• Routine human audits
• Ensure Adherence with ADA, Section 508, EU AI Act, and WCAG compliances
• Leveraging assistive technologies to test interactive content

Summary

Technology and humans cannot work independently. When technology cannot find the solution, human involvement is a must. Similarly, technology must complete human capabilities, not compete with them. The same is true with AI involvement in accessibility testing. There are more than a billion people globally who live with some kind of disability. So, to ensure the success of your software/mobile or web application accessibility testing, you must ensure the equal partnership between AI and a human-in-the-loop approach. Tx addresses this with its years of expertise in delivering successful accessibility testing projects and combining it with AI-driven capabilities. Contact our experts now to learn how Tx can help you with the hybrid approach.