Accessibility compliance

Practical accessibility patterns for journalism and academic web publishing.

When to activate

• Building or auditing news websites
• Writing alt text for article images
• Creating accessible data visualizations
• Developing tools that journalists use
• Ensuring multimedia content is accessible
• Meeting legal accessibility requirements
• Publishing academic content online

WCAG essentials for news sites

WCAG 2.2 became a W3C Recommendation in October 2023 and is backwards-compatible with 2.1. Targeting 2.2 AA is the right default for new work; 2.1 AA remains the floor in most legal regimes (see "Legal requirements" below).

WCAG 2.2 added nine criteria over 2.1. Most relevant for news sites: 2.5.8 Target Size (Minimum) AA — interactive targets at least 24×24 CSS pixels; 2.4.11 Focus Not Obscured (Minimum) AA — focused element not fully hidden by sticky headers / chat widgets; 3.3.8 Accessible Authentication AA — no cognitive function tests (e.g., transcribing distorted text) without an alternative; 3.3.7 Redundant Entry A — don't ask users to re-enter the same data within a session.

The four principles (POUR)

## WCAG 2.2 AA checklist (journalism focus)

### Perceivable
- [ ] Images have meaningful alt text
- [ ] Videos have captions
- [ ] Audio has transcripts
- [ ] Color isn't the only way to convey info
- [ ] Text can be resized to 200% without breaking

### Operable
- [ ] All functions work with keyboard only
- [ ] No keyboard traps
- [ ] Skip links to main content
- [ ] Page titles describe content
- [ ] Focus visible on all interactive elements

### Understandable
- [ ] Language is declared in HTML
- [ ] Navigation is consistent
- [ ] Error messages are clear
- [ ] Labels describe form fields

### Robust
- [ ] Valid HTML
- [ ] ARIA used correctly (or not at all)
- [ ] Works with screen readers
- [ ] Doesn't break with zoom/text resize

Image accessibility

Alt text for journalism

## Alt text decision tree

### News photos
- **WHO** is in the image (if identifiable and relevant)
- **WHAT** is happening (the action or situation)
- **WHERE** (if location matters to story)
- **Don't**: Repeat caption text verbatim

### Examples

PHOTO: Protesters holding signs outside courthouse

BAD: "Protesters"
BAD: "Image of protest" (redundant "image of")
GOOD: "Approximately 50 protesters hold signs reading 'Justice Now' outside the federal courthouse in downtown Seattle"

PHOTO: Headshot of interview subject

BAD: "Photo"
GOOD: "Dr. Sarah Chen, epidemiologist at Johns Hopkins"

PHOTO: Chart embedded as image

BAD: "Chart showing data"
GOOD: "Bar chart showing unemployment rising from 3.5% to 8.2% between March and June 2020. Full data in table below."

Alt text for AI-generated images

Newsroom transparency policies generally require disclosing that an image is AI-generated; that disclosure belongs in the caption AND the alt text, because screen-reader users see only the alt text. Describe the visual content first, then note the provenance. Don't lead with "AI-generated image of..." — describe the subject the way you would for any photo, then add the AI source.

GOOD: "Illustration of a flooded downtown street with abandoned cars, water reaching first-floor windows. AI-generated by [tool] for editorial use."

GOOD (decorative AI illustration): "Decorative AI-generated abstract pattern in blue and orange." — but consider whether the image should have empty alt (alt="") since it carries no information.

For AI-generated images of real people or events, AP and most newsroom guidelines treat the image as a manipulation, not a photograph — disclosure in alt text is required, not optional.

Alt text Python helper

def generate_alt_text_prompt(context: dict) -> str:
    """Generate prompt for AI alt text assistance."""
    return f"""
    Write alt text for a news image.

    Story context: {context.get('headline', 'Unknown')}
    Image type: {context.get('image_type', 'photo')}
    Caption (if any): {context.get('caption', 'None')}

    Guidelines:
    - Be concise (under 125 characters if possible)
    - Don't start with "Image of" or "Photo of"
    - Include relevant details for story context
    - Don't duplicate caption exactly
    - Describe what's visually important

    If this is decorative only, respond: ""
    """

def is_decorative(image_context: str) -> bool:
    """Check if image is purely decorative (empty alt appropriate)."""
    decorative_indicators = [
        'decorative',
        'separator',
        'background',
        'spacer',
        'border'
    ]
    return any(ind in image_context.lower() for ind in decorative_indicators)

Accessible data visualization

Chart accessibility checklist

## Making charts accessible

### Essential elements
- [ ] Text alternative describing the key insight
- [ ] Data table available (visible or linked)
- [ ] Colors have sufficient contrast
- [ ] Patterns/textures supplement color coding
- [ ] Labels directly on chart (not legend-only)
- [ ] Title describes what chart shows

### Interactive charts
- [ ] Keyboard navigable
- [ ] Focus indicators visible
- [ ] Screen reader announces data points
- [ ] Tooltips accessible via keyboard
- [ ] Zooming doesn't break layout

Accessible chart component

<!-- Accessible chart pattern -->
<figure role="figure" aria-labelledby="chart-title" aria-describedby="chart-desc">
  <figcaption>
    <h3 id="chart-title">Unemployment Rate 2020-2024</h3>
    <p id="chart-desc">
      Line chart showing unemployment starting at 3.5% in January 2020,
      spiking to 14.7% in April 2020, and gradually declining to 3.9% by 2024.
    </p>
  </figcaption>

  <!-- The chart itself -->
  <div id="chart" role="img" aria-label="Interactive line chart. Data table available below.">
    <!-- Chart renders here -->
  </div>

  <!-- Always provide data table -->
  <details>
    <summary>View data table</summary>
    <table>
      <caption>Monthly unemployment rate data</caption>
      <thead>
        <tr>
          <th scope="col">Month</th>
          <th scope="col">Unemployment Rate (%)</th>
        </tr>
      </thead>
      <tbody>
        <tr><td>Jan 2020</td><td>3.5</td></tr>
        <tr><td>Apr 2020</td><td>14.7</td></tr>
        <!-- etc -->
      </tbody>
    </table>
  </details>
</figure>

Color-blind safe palettes

# Safe color palettes for data visualization
COLOR_PALETTES = {
    # Paul Tol's color schemes - widely tested for accessibility
    'bright': [
        '#4477AA',  # Blue
        '#EE6677',  # Red
        '#228833',  # Green
        '#CCBB44',  # Yellow
        '#66CCEE',  # Cyan
        '#AA3377',  # Purple
        '#BBBBBB',  # Grey
    ],

    # Categorical (safe for most color blindness)
    'categorical': [
        '#332288',  # Indigo
        '#88CCEE',  # Cyan
        '#44AA99',  # Teal
        '#117733',  # Green
        '#999933',  # Olive
        '#DDCC77',  # Sand
        '#CC6677',  # Rose
        '#882255',  # Wine
    ],

    # Sequential (single hue)
    'sequential_blue': [
        '#f7fbff',
        '#deebf7',
        '#c6dbef',
        '#9ecae1',
        '#6baed6',
        '#4292c6',
        '#2171b5',
        '#084594',
    ],

    # Diverging (for data with meaningful midpoint)
    'diverging': [
        '#d73027',  # Red (negative)
        '#f46d43',
        '#fdae61',
        '#fee08b',
        '#ffffbf',  # Neutral
        '#d9ef8b',
        '#a6d96a',
        '#66bd63',
        '#1a9850',  # Green (positive)
    ]
}

def validate_contrast(color1: str, color2: str) -> float:
    """Calculate WCAG contrast ratio between two colors."""
    def hex_to_rgb(hex_color):
        hex_color = hex_color.lstrip('#')
        return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))

    def relative_luminance(rgb):
        r, g, b = [x / 255.0 for x in rgb]
        r = r / 12.92 if r <