How AI Writes Alt-Text: Assessing Quality, Understanding Limits, and the Need for Human Review

As the web becomes ever more visual, the demand for accessible content has never been higher. For content creators and web developers alike, generating accurate and meaningful alt-text is both a legal and ethical responsibility. Enter AI alt-text tools: promises of automated image analysis that can swiftly label thousands of visuals, making digital spaces more inclusive for those relying on screen readers. Yet, as these technologies proliferate, questions about bias in AI, alt-text quality, and the role of human review are coming to the fore.

Imagine uploading a team photo to your website. The AI alt-text generator labels it simply as “people.” While technically correct, it misses key context—such as diversity represented or the event’s significance—details that matter both for accessibility and for conveying your message authentically. This micro-scenario underscores the limits of current AI accessibility solutions and highlights why alt-text editing by humans remains vital.

In this article, we’ll explore how AI writes alt-text, assessing its strengths and shortcomings. We’ll delve into how image analysis works, where bias in AI can creep in, and why evaluating alt-text quality is far from straightforward. Finally, we’ll discuss best practices for integrating human review into your workflow, ensuring your content meets both legal standards and genuine user needs.

Introduction: The Role of Alt-Text in Accessibility

Alt-text is fundamental for making digital images accessible to individuals with visual impairments. The emergence of AI accessibility tools has enabled the automated generation of alt-text by analysing images and applying natural language processing. While these systems increase efficiency, the quality of AI alt-text can be inconsistent, sometimes introducing bias in AI outputs or missing critical contextual details. AI image analysis typically identifies obvious elements within an image, such as “a red apple on a white plate.” However, it may overlook nuance, misrepresent intent, or reinforce stereotypes—issues that can only be mitigated through human review and careful alt-text editing. For example, an AI might label a group photo merely as “people standing,” omitting information about their roles or emotional interactions that a person would recognise. To implement reliable AI alt-text workflows: - Use AI tools to generate initial alt-text drafts. - Assign staff to review and edit AI-generated descriptions for accuracy and context. - Prioritise human oversight for images with complex content or social context. - Regularly assess your workflow for recurring errors or signs of bias in AI outputs. A practical example for integrating high-quality alt-text in HTML:

<img src="team_photo.jpg" alt="Three engineers collaborating around a laptop during a product design meeting.">

This alt-text provides specific context about the individuals’ activity and environment—details often missed by AI alone. Sustaining robust alt-text quality requires combining automated tools with human expertise to ensure digital content remains genuinely accessible.

How AI Generates Alt-Text: An Overview

AI alt-text generation relies on advanced image analysis techniques, often using convolutional neural networks to interpret visual content. These systems detect objects, scenes, and sometimes context or emotion within an image, producing concise descriptions for users of assistive technologies. For example, uploading a product photo to an online shop may yield AI-generated alt-text such as “Red athletic shoes on white background,” demonstrating the system’s ability to merge object identification with contextual cues. Despite these capabilities, issues like bias in AI and limited training data can affect accuracy. AI might misidentify items (e.g., labelling a croissant as a bread roll) or overlook essential context. This highlights the ongoing need for human review and alt-text editing to ensure descriptions are accurate and relevant. Automated systems cannot always discern an image’s purpose or importance within a page—such as distinguishing between decorative graphics and key promotional banners—which impacts overall alt-text quality. To integrate AI accessibility tools into your workflow, you might use a Python script that submits images to a captioning API:

from PIL import Image
import requests

img = Image.open('shoes.jpg')
response = requests.post('https://api.example.com/caption', files={'image': img})
print(response.json()['alt_text'])

This approach streamlines initial alt-text creation but requires manual review before publishing. To enhance the effectiveness of AI-generated alt-text:

• Review each description for accuracy and relevance
• Edit auto-generated text to reflect image context
• Monitor for recurring errors caused by bias in AI
• Prioritise human oversight for critical images

Combining automated generation with thorough human review is essential for reliable AI accessibility and high-quality digital content.

Evaluating the Quality of AI-Generated Alt-Text

AI alt-text tools use image analysis to produce descriptions quickly, yet the output often varies in accuracy and usefulness. Assessing alt-text quality requires attention to detail beyond basic correctness: professionals should evaluate how well the description reflects the image’s context, its specificity, and whether it avoids unnecessary assumptions. For instance, “A person standing in front of a building” is technically accurate but lacks context; “A woman in a red coat stands outside the British Museum” offers more relevant information for users. Implementing a structured human review process is essential for improving AI accessibility. After generating initial alt-text, reviewers should check for bias in AI outputs—such as unwarranted assumptions about identity or actions—and make sure each description aligns with the image’s role on the page. Alt-text editing should focus on clarity and relevance, ensuring that automated content meets practical accessibility standards. A concise workflow for maintaining high alt-text quality includes:

• Review each AI-generated alt-text entry.
• Edit descriptions for accuracy, context, and brevity.
• Identify and correct any bias in AI-generated content.
• Test with screen readers to verify usability.

To support this process programmatically, developers can integrate checks for common issues such as excessive length. For example:

// Flag alt-text over 125 characters
if (altText.length > 125) {
  alert('Alt-text too long: please revise');
}

This code flags alt-text that may be too lengthy for effective screen reader output, prompting human review. Combining these technical measures with systematic human review ensures that AI-generated alt-text delivers meaningful accessibility benefits and reduces potential bias in AI outputs.

Common Limitations and Pitfalls of AI Image Analysis

AI alt-text generation tools offer a rapid way to describe images, but their outputs are far from flawless. One major issue is the tendency for generic or vague descriptions, such as “a person sitting at a table,” which lack context or detail valuable for accessibility. This can be particularly problematic for web developers aiming to improve site inclusivity, as alt-text quality directly impacts user experience for visually impaired visitors. Another pitfall is bias in AI. For instance, image analysis systems may misidentify people’s gender, age, or ethnicity based on limited or biased training data. Imagine uploading a photo of a woman in a laboratory: the AI might simply output “a person in a room” or mistakenly label her as “a man with equipment,” failing both in accuracy and inclusivity. Such errors highlight why human review and alt-text editing are essential before publishing. AI models also struggle with complex scenes, cultural references, and text within images. They cannot reliably interpret infographics or memes, often omitting key details. For example, an AI alt-text tool might ignore embedded chart data or the humour in a meme, leaving users with incomplete information. To mitigate these pitfalls:

• Always review and edit AI-generated alt-text before publishing.
• Use AI accessibility tools as a first draft, not a final solution.
• Check for and correct any potential bias or inaccuracies.
• Supplement alt-text manually for complex or informative images.

A practical workflow could look like this:

alt_text = ai_generate_alt_text(image)
if not alt_text_quality_sufficient(alt_text):
    alt_text = manual_edit(alt_text)
publish(alt_text)

This approach ensures that automated image analysis supports accessibility without sacrificing quality or accuracy.

Bias in AI: Impact on Alt-Text Accuracy

Artificial intelligence tools used for generating alt-text often rely on large datasets to learn how to describe images. However, if these datasets lack diversity or contain skewed representations, bias in AI can directly affect the accuracy and inclusivity of AI alt-text. For instance, an image analysis algorithm trained predominantly on Western cultural contexts might mislabel traditional attire from other regions, resulting in misleading or inaccurate alt-text. AI-generated alt-text can also reflect gender, racial, or ability-based biases present in its training data. A practical example is an AI system consistently describing wheelchair users as "patients" rather than "people," which not only reduces the quality of the description but may also cause offence or perpetuate stereotypes. This illustrates why robust human review and regular alt-text editing are essential to ensure descriptions are both accurate and respectful. To mitigate these issues, content creators and web developers should adopt workflows that combine automated generation with manual oversight. One practical method is to use AI accessibility tools as a starting point, then refine their output:

<img src="photo.jpg" alt="A woman using a wheelchair gives a presentation to colleagues">

Here, AI might initially output "A patient in a wheelchair," but a human editor can clarify context and correct bias. Periodic audits of AI-generated alt-text and feedback loops with users with disabilities further improve alt-text quality. Ultimately, while AI streamlines image description, it cannot replace the nuanced judgment required for truly inclusive content.

Case Studies: Successes and Failures of AI Alt-Text

Artificial intelligence has significantly advanced alt-text generation, enabling rapid image analysis across large collections. Nonetheless, the effectiveness of AI alt-text remains inconsistent. For instance, a leading online retailer implemented an AI-driven system to generate product image descriptions. The tool performed well with straightforward items—such as “Navy blue ceramic mug on a wooden table”—but faltered with complex images, often producing vague outputs like “Group of people” without specifying actions or context. A positive example comes from a news organisation that integrated AI alt-text into its editorial workflow. Here, every piece of AI-generated alt-text underwent human review and editing prior to publication. This approach streamlined the process while ensuring descriptions met accessibility standards. Editors made targeted improvements, such as clarifying ambiguous references and adding key contextual information for screen reader users. Conversely, bias in AI was evident in a project where an image recognition algorithm frequently misidentified individuals from minority groups. These errors were traced back to insufficiently diverse training data, underscoring the risk of propagating bias in AI-generated alt-text and the necessity for ongoing evaluation. For effective implementation of AI accessibility measures, establish a quality assurance routine involving both automated checks and human intervention:

# Example: Reviewing AI-generated alt-text
for image in website_images:
    ai_text = generate_ai_alt_text(image)
    if not passes_quality_check(ai_text):
        flag_for_human_review(ai_text)

This code illustrates a practical workflow: after initial generation, each piece of alt-text is checked for adequacy; those that fail are flagged for human review and editing. Regular human oversight is essential for maintaining high alt-text quality and reducing the impact of algorithmic bias.

Why Human Review is Indispensable

AI alt-text generators provide efficient image analysis and initial descriptions, yet human review is essential for maintaining alt-text quality and contextual relevance. Automated systems can overlook nuanced visual cues or perpetuate bias in AI, making it difficult to guarantee that generated descriptions are accurate or inclusive without human intervention. For instance, an AI might produce the alt-text “A person at a desk” for an image depicting a judge in court. A human can revise this to “A judge reading documents in a courtroom,” adding critical context that supports meaningful AI accessibility. This process of alt-text editing ensures that screen reader users receive precise and informative content. Human reviewers are also equipped to identify and correct stereotypes or cultural insensitivities resulting from machine learning models trained on skewed data. To enhance consistency and effectiveness, consider implementing a simple checklist during human review:

• Check for factual accuracy and relevant detail
• Eliminate vague or redundant language
• Align alt-text with surrounding content
• Identify and address biased or exclusionary terms
• Edit for clarity, brevity, and usefulness

A practical integration of this process could involve displaying the AI-generated alt-text within your content management system (CMS) and requiring explicit human approval before publication. For example, the workflow might present the draft to a reviewer and wait for editor confirmation before proceeding, ensuring oversight at each step. This method ensures that AI accessibility solutions are robust, reducing the risk of errors while upholding high editorial standards through human oversight.

Best Practices for Editing and Reviewing Alt-Text

When leveraging AI alt-text generation tools, it's crucial to remember that automated image analysis often produces generic or incomplete descriptions. AI accessibility tools can accelerate workflows, but they may introduce errors or reinforce bias in AI if left unchecked. Human review is essential for ensuring alt-text quality and relevance, especially for content with nuanced context or cultural references. Begin by examining each AI-generated alt-text for accuracy and completeness. Ask: Does it capture the essential information? For example, if AI outputs “a person holding an object”, refine this to “a woman holding a bouquet of sunflowers” if that detail is visible and meaningful. Always tailor alt-text to the context—what’s important in an online shop might differ from a news article. Effective alt-text editing involves both removing unnecessary verbosity and adding missing context. Avoid restating visible text in images unless it’s crucial (e.g., in infographics), and steer clear of subjective interpretations unless relevant to the audience. A practical checklist when reviewing AI alt-text includes:

• Verify factual accuracy against the image.
• Remove redundant phrases (e.g., “image of”).
• Ensure relevance to surrounding content.
• Check for potential bias in AI-generated language.
• Maintain concise but descriptive phrasing.

For bulk updates or programmatic checks, use scripts to flag problematic alt-texts. For example, you can automatically identify alt-text that is too short or uses generic phrases such as “photo of,” which often signals low-quality descriptions. By flagging these cases for human review, you help maintain robust editing standards and ensure high accessibility.

The Future of AI Accessibility Tools

AI-generated alt-text is rapidly transforming how content creators and web developers approach accessibility. With advances in image analysis, modern algorithms can automatically generate descriptive text for images at scale. For example, a content management system might use an AI model to scan uploaded photographs and insert basic descriptions, streamlining workflows for busy teams. However, the quality of AI alt-text remains inconsistent. While AI excels at identifying objects—such as "A red bicycle leaning against a wall"—it often misses context or emotional nuance essential for full accessibility. Bias in AI can also result in generic or culturally skewed descriptions, potentially excluding users with specific needs or perspectives. To maintain high alt-text quality, human review is indispensable. Practical workflows should incorporate alt-text editing checkpoints. For instance, after automated generation, a reviewer can quickly verify and refine the output before publishing. This hybrid approach leverages efficiency without sacrificing accuracy. A practical implementation might look like this in JavaScript:

// Example: Mark AI-generated alt-text for easy review
if (altText.isAIGenerated) {
  altText.value += " [AI]";
}

This snippet tags AI-generated descriptions so editors can identify and prioritise them for human review. Ultimately, AI accessibility tools are most effective when paired with clear editorial oversight. As these systems evolve, specialists should focus on iterative feedback—correcting errors and updating training data—to minimise bias in AI and improve future results.

Conclusion: Striking a Balance Between Automation and Human Insight

AI-generated alt-text has rapidly improved, offering time-saving benefits and broadening accessibility. However, relying solely on AI for image analysis can compromise alt-text quality—especially in nuanced contexts. For example, an AI might generate “A man standing in front of a building” for a press photo, missing critical context like the person’s identity or the significance of the location. Such omissions reveal how bias in AI and limited datasets can yield generic or misleading descriptions. To ensure effective AI accessibility, human review and alt-text editing remain indispensable. A practical workflow for web developers could involve integrating an AI alt-text API during image upload, followed by manual curation. For instance, after an AI suggests an initial description, an editor can refine it to include specific details or correct misinterpretations. Here’s a simple example of leveraging both automation and human oversight in code:

// Pseudocode for hybrid alt-text workflow
let aiAltText = callAIAltTextAPI(image);
let finalAltText = promptHumanEditor(aiAltText);
applyAltTextToImage(image, finalAltText);

In this approach, the AI provides a baseline description, but human review polishes it for accuracy and relevance. Content creators and accessibility specialists should also be aware of common pitfalls—such as cultural context or subtle visual cues that AI may miss. Ultimately, combining AI efficiency with human judgement ensures that alt-text not only meets accessibility standards but also conveys genuine meaning to all users.

As content creators, web developers, and accessibility specialists, your next step is to critically evaluate the alt-text generated by AI on your platforms. While machine learning tools can accelerate workflows, they may overlook context or misinterpret complex visuals—such as charts, memes, or culturally nuanced imagery—potentially introducing ambiguity or inaccuracy. Instead of relying solely on automation, review AI-generated descriptions and refine them to capture specific details and intent. For instance, if an AI labels an infographic as “chart,” clarify its message: “Bar chart comparing annual carbon emissions in the UK, 2020–2023.”

Begin by sampling images across your site and assessing whether the alt-text provides meaningful information for users relying on screen readers. Collaborate with colleagues to establish standards tailored to your audience; for example, ensure product images have concise but descriptive text (“Red wool jumper with cable-knit pattern”) rather than vague generics. By actively intervening where AI falls short and sharing examples of effective practice within your team, you can foster a culture of accessibility that truly supports all users. This hands-on review not only improves user experience but also aligns your digital content with ethical and legal standards.

FAQ

How does AI generate alt-text for images?

AI systems use image recognition algorithms and large datasets to identify objects, people, and scenes within images. These systems then generate descriptive text based on their analysis, aiming to convey the content and context of the image for users who rely on screen readers.

What are the main limitations of AI-generated alt-text?

AI-generated alt-text can miss contextual nuances, misidentify objects, or produce generic descriptions. Biases in training data may also lead to inaccuracies or exclusionary language, making human review essential for quality assurance.

Why is human review necessary after using AI to write alt-text?

Human reviewers can assess context, intent, and subtle details that AI may overlook. They ensure descriptions are accurate, meaningful, and aligned with accessibility guidelines, reducing the risk of misleading or insensitive content.

How can content creators improve the quality of alt-text?

Content creators should edit and refine AI-generated alt-text, tailoring descriptions to the image's purpose and audience. Following established accessibility standards and involving users with disabilities in testing can further enhance quality.

What future developments can we expect in AI accessibility tools?

Advancements in machine learning and natural language processing may yield more accurate and context-aware alt-text. However, ongoing human oversight will remain vital to address evolving accessibility needs and ethical considerations.

Further reading

• Accessibility Guidelines for Web Content
• Best Practices for Alt-Text
• AI Bias in Digital Products