From 3cfb032015bbb91066098800905d6ed8df5a417f Mon Sep 17 00:00:00 2001
From: Anssi Kostiainen <anssi.kostiainen@intel.com>
Date: Tue, 3 May 2022 13:23:41 +0000
Subject: [PATCH] Merge pull request #13 from
 webmachinelearning/WCAG_replacing_FAST

SHA: 25a8ba5bc35df37e4c5d9f4092b5ee182ec7f226
Reason: push, by @anssiko

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
---
 index.html | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/index.html b/index.html
index 40a6631..f93fb48 100644
--- a/index.html
+++ b/index.html
@@ -8,7 +8,7 @@
   <link href="https://www.w3.org/2008/site/images/favicon.ico" rel="icon">
   <meta content="Bikeshed version fb1e763a4, updated Tue Mar 1 13:13:50 2022 -0800" name="generator">
   <link href="https://w3.org/TR/ethical-webmachinelearning" rel="canonical">
-  <meta content="701c9234d709624a278b4abefc7ec80f7ac14356" name="document-revision">
+  <meta content="25a8ba5bc35df37e4c5d9f4092b5ee182ec7f226" name="document-revision">
 <style>/* style-autolinks */
 
 .css.css, .property.property, .descriptor.descriptor {
@@ -406,7 +406,7 @@
   <div class="head">
    <p data-fill-with="logo"><a class="logo" href="https://www.w3.org/"> <img alt="W3C" height="48" src="https://www.w3.org/StyleSheets/TR/2021/logos/W3C" width="72"> </a> </p>
    <h1 class="p-name no-ref" id="title">Ethical Principles for Web Machine Learning</h1>
-   <p id="w3c-state"><a href="https://www.w3.org/standards/types#ED">Editor’s Draft</a>, <time class="dt-updated" datetime="2022-05-02">2 May 2022</time></p>
+   <p id="w3c-state"><a href="https://www.w3.org/standards/types#ED">Editor’s Draft</a>, <time class="dt-updated" datetime="2022-05-03">3 May 2022</time></p>
    <details open>
     <summary>More details about this document</summary>
     <div data-fill-with="spec-metadata">
@@ -657,7 +657,7 @@ <h4 class="no-num heading settled" id="principle-2-fairness-and-non-discriminati
    <p>ML actors should minimize and avoid reinforcing or perpetuating bias and discrimination, particularly against vulnerable and historically marginalised groups. This includes but is not limited to bias based on gender, gender identity and gender expression, sexual orientation, disability (both visible and invisible), mental health, neurotype, physical appearance, body, age, race, socio-economic status, ethnicity, caste, nationality, language, or religion.</p>
    <p>ML actors should ensure that the outcomes of ML applications are fair, and that effective remedy is available against discrimination and biased algorithmic determination. ML systems should work equally well for all users, and where this is not true for <a class="property css" data-link-type="property">outliers</a>, fallback solutions should be provided.</p>
    <p>This principle also applies to access to web ML systems: they should be fully accessible to people with disabilities, appropriately localized, and accommodate people on low bandwidth networks and with low specification equipment.</p>
-   <p>For further guidance on accessibility, see  the draft checklist to support Framework for Accessibility in the Specification of Technologies <a data-link-type="biblio" href="#biblio-fast">[FAST]</a>.</p>
+   <p>For further guidance on accessibility, see the Web Content Accessibility Guidelines (WCAG) 2.1 <a data-link-type="biblio" href="#biblio-wcag">[WCAG]</a>, or consult the Accessible Platform Architectures (APA) Working Group.</p>
    <h4 class="no-num heading settled" id="principle-3-autonomy"><span class="content">PRINCIPLE 3) Autonomy</span><a class="self-link" href="#principle-3-autonomy"></a></h4>
    <p>ML systems should respect and enhance human autonomy. This includes enabling meaningful agency, control and choice. Users should give informed consent before Web ML is used.</p>
    <p>ML systems could be used to manipulate and deceive people, complicate isolation, and encourage addictive behaviors. ML actors should mitigate against these potential abuses and patterns when creating ML systems, and avoid introducing technologies that increase the chance of people being harmed in this way.</p>
@@ -1039,8 +1039,6 @@ <h3 class="no-num no-ref heading settled" id="informative"><span class="content"
    <dd>Crawford, K. <a href="https://www.youtube.com/watch?v=fMym_BKWQzk"><cite>The trouble with bias</cite></a>. URL: <a href="https://www.youtube.com/watch?v=fMym_BKWQzk">https://www.youtube.com/watch?v=fMym_BKWQzk</a>
    <dt id="biblio-egtai">[EGTAI]
    <dd><a href="https://www.aepd.es/sites/default/files/2019-12/ai-ethics-guidelines.pdf"><cite>Ethics Guidelines for Trustworthy AI</cite></a>. URL: <a href="https://www.aepd.es/sites/default/files/2019-12/ai-ethics-guidelines.pdf">https://www.aepd.es/sites/default/files/2019-12/ai-ethics-guidelines.pdf</a>
-   <dt id="biblio-fast">[FAST]
-   <dd><a href="https://w3c.github.io/apa/fast/checklist.html"><cite>Framework for Accessibility in the Specification of Technologies</cite></a>. URL: <a href="https://w3c.github.io/apa/fast/checklist.html">https://w3c.github.io/apa/fast/checklist.html</a>
    <dt id="biblio-fatml">[FATML]
    <dd><a href="https://www.fatml.org/resources/principles-for-accountable-algorithms"><cite>Principles for Accountable Algorithms</cite></a>. URL: <a href="https://www.fatml.org/resources/principles-for-accountable-algorithms">https://www.fatml.org/resources/principles-for-accountable-algorithms</a>
    <dt id="biblio-gall">[Gall]
@@ -1063,6 +1061,8 @@ <h3 class="no-num no-ref heading settled" id="informative"><span class="content"
    <dd>Vallor; Green; Raicu. <a href="https://www.scu.edu/ethics-in-technology-practice/overview-of-ethics-in-tech-prac"><cite>Overview of Ethics in Tech Practice</cite></a>. URL: <a href="https://www.scu.edu/ethics-in-technology-practice/overview-of-ethics-in-tech-prac">https://www.scu.edu/ethics-in-technology-practice/overview-of-ethics-in-tech-prac</a>
    <dt id="biblio-vaughan">[Vaughan]
    <dd>Wortman; Vaughan; Wallach. <a href="http://www.jennwv.com/papers/intel-chapter.pdf"><cite>A Human-Centered Agenda for Intelligible Machine Learning</cite></a>. URL: <a href="http://www.jennwv.com/papers/intel-chapter.pdf">http://www.jennwv.com/papers/intel-chapter.pdf</a>
+   <dt id="biblio-wcag">[WCAG]
+   <dd><a href="https://www.w3.org/TR/WCAG21"><cite>Web Content Accessibility Guidelines (WCAG) 2.1</cite></a>. URL: <a href="https://www.w3.org/TR/WCAG21">https://www.w3.org/TR/WCAG21</a>
    <dt id="biblio-weidinger">[Weidinger]
    <dd>Weidinger et al. <a href="https://arxiv.org/pdf/2112.04359.pdf"><cite>Ethical and social risks of harm from Language Models</cite></a>. URL: <a href="https://arxiv.org/pdf/2112.04359.pdf">https://arxiv.org/pdf/2112.04359.pdf</a>
    <dt id="biblio-xue">[Xue]