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Added missing i18n attributes to 3 sections: 1. "Our Research Focus" section (lines 88-102): - Added data-i18n to heading - Added data-i18n-html to 4 paragraphs - New keys: research_focus.heading, research_focus.paragraph_1-4 2. Te Tiriti paragraph (line 134): - Added data-i18n-html="core_values.te_tiriti.paragraph_2_full" - Paragraph wrapper now translatable 3. GDPR rights paragraph (line 216): - Added data-i18n-html="data_practices.rights_full" - Paragraph wrapper now translatable These sections will now support language switching once translations are added to de/about.json and fr/about.json. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
390 lines
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390 lines
25 KiB
HTML
<!DOCTYPE html>
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<html lang="en" data-page="about">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>About | Tractatus AI Safety Framework</title>
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<meta name="description" content="Learn about the Tractatus Framework: our mission, values, team, and commitment to preserving human agency through structural AI safety.">
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a:focus, button:focus, input:focus, select:focus, textarea:focus {
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a:focus-visible { outline: 3px solid #3b82f6; outline-offset: 2px; }
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<ol class="flex items-center space-x-2 text-sm">
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<li><a href="/" class="hover:underline transition-colors text-tractatus-link">Home</a></li>
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<li class="text-gray-400">/</li>
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<li class="text-gray-900 font-medium" aria-current="page">About</li>
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<!-- Hero Section -->
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<div class="bg-gradient-to-br from-blue-50 to-purple-50 py-20">
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<div class="text-center">
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<h1 class="text-5xl font-bold text-gray-900 mb-6" data-i18n="header.title">
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About Tractatus
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</h1>
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<p class="text-xl text-gray-600 max-w-3xl mx-auto" data-i18n="header.subtitle">
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A framework for AI safety through architectural constraints, preserving human agency where it matters most.
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</p>
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</div>
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</div>
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</div>
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<!-- Mission Section -->
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<main id="main-content" class="max-w-4xl mx-auto px-4 sm:px-6 lg:px-8 py-16">
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<section class="mb-16">
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<h2 class="text-3xl font-bold text-gray-900 mb-6" data-i18n="mission.heading">Our Mission</h2>
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<div class="prose prose-lg text-gray-600">
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<p class="mb-6" data-i18n="mission.democratic_legitimacy">
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As AI systems make increasingly consequential decisions—medical treatment, hiring, content moderation, resource allocation—a fundamental question emerges: whose values guide these decisions? Current AI alignment approaches embed particular moral frameworks into systems deployed universally. When they work, it's because everyone affected shares those values. When they don't, someone's values inevitably override others'.
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</p>
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<p class="mb-6" data-i18n-html="mission.intro">
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The Tractatus Framework exists to address a fundamental problem in AI safety: current approaches rely on training, fine-tuning, and corporate governance—all of which can fail, drift, or be overridden. We propose <strong>safety through architecture</strong>.
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</p>
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<p class="mb-6" data-i18n-html="mission.wittgenstein">
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Inspired by Ludwig Wittgenstein's <em>Tractatus Logico-Philosophicus</em>, our framework recognizes that some domains—values, ethics, cultural context, human agency—cannot be systematized. What cannot be systematized must not be automated. AI systems should have structural constraints that prevent them from crossing these boundaries.
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</p>
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<blockquote class="border-l-4 border-blue-600 pl-6 italic text-gray-700 my-6">
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<span data-i18n-html="mission.quote">"Whereof one cannot speak, thereof one must be silent."</span><br>
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<span data-i18n-html="mission.quote_source">— Ludwig Wittgenstein, <em>Tractatus</em> (§7)</span>
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</blockquote>
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<p class="mb-4" data-i18n-html="mission.applied">
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Applied to AI: <strong>"What cannot be systematized must not be automated."</strong>
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</p>
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</div>
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</section>
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<!-- Our Research Focus -->
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<section class="mb-16">
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<h2 class="text-3xl font-bold text-gray-900 mb-6" data-i18n="research_focus.heading">Our Research Focus</h2>
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<div class="prose prose-lg text-gray-600">
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<p class="mb-6" data-i18n-html="research_focus.paragraph_1">
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Tractatus emerged from a simple but urgent question: as AI systems become more capable, how do we preserve human control over the decisions that matter? Not technical decisions—moral ones. Decisions about whose privacy matters more. Whose needs come first. What trade-offs are acceptable.
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</p>
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<p class="mb-6" data-i18n-html="research_focus.paragraph_2">
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This is fundamentally a problem of <strong>moral philosophy</strong>, not management science. Different communities hold genuinely different, equally legitimate values. You cannot rank "family privacy" against "community safety" on a single scale—they are incommensurable. Any system claiming to do so is simply imposing one community's values on everyone else.
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</p>
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<p class="mb-6" data-i18n-html="research_focus.paragraph_3">
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The framework's core insight comes from attending carefully to what humans actually need from AI: not another authority making decisions for them, but systems that recognize when a decision requires human deliberation across different perspectives. The <strong>PluralisticDeliberationOrchestrator</strong> represents our primary research focus—a component designed to detect when AI encounters values conflicts and coordinate deliberation among affected stakeholders rather than making autonomous choices.
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</p>
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<p class="mb-4" data-i18n-html="research_focus.paragraph_4">
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Traditional organizational theory addresses authority through hierarchy. But post-AI contexts require something different: authority through appropriate deliberative process. Not "AI decides for everyone," but "AI recognizes when humans must decide together."
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</p>
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</div>
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</section>
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<!-- Why This Matters -->
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<section class="mb-16">
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<h2 class="text-3xl font-bold text-gray-900 mb-6" data-i18n="why_this_matters.heading">Why This Matters</h2>
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<div class="prose prose-lg text-gray-600">
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<p class="mb-6" data-i18n="why_this_matters.paragraph_1">
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AI systems are amoral hierarchical constructs, fundamentally incompatible with the plural, incommensurable values human societies exhibit. A hierarchy can only impose one framework and treat conflicts as anomalies. You cannot pattern-match your way to pluralism.
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</p>
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<p class="mb-6" data-i18n="why_this_matters.paragraph_2">
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Human societies spent centuries learning to navigate moral pluralism through constitutional separation of powers, federalism, subsidiarity, and deliberative democracy. These structures acknowledge that legitimate authority over value decisions belongs to affected communities, not distant experts claiming universal wisdom.
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</p>
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<p class="mb-6" data-i18n="why_this_matters.paragraph_3">
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AI development risks reversing this progress. As capability concentrates in a few labs, value decisions affecting billions are being encoded by small teams applying their particular moral intuitions at scale. Not through malice—through structural necessity. The architecture of current AI systems demands hierarchical value frameworks.
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</p>
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<p class="mb-4" data-i18n="why_this_matters.paragraph_4">
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The Tractatus Framework offers an alternative: separate what must be universal (safety boundaries) from what should be contextual (value deliberation). This preserves human agency over moral decisions while enabling AI capability to scale.
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</p>
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</div>
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</section>
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<!-- Core Values -->
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<section class="mb-16">
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<h2 class="text-3xl font-bold text-gray-900 mb-6" data-i18n="core_values.heading">Core Values</h2>
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<!-- Te Tiriti Summary -->
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<div class="bg-blue-50 border-l-4 border-blue-600 p-6 rounded-r-lg mb-8">
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<h3 class="text-xl font-bold text-gray-900 mb-3" data-i18n="core_values.te_tiriti.heading">Digital Sovereignty & Te Tiriti o Waitangi</h3>
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<p class="text-gray-700 mb-3" data-i18n="core_values.te_tiriti.paragraph_1">
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The principle that communities should control their own data and technology isn't new—it has deep roots in indigenous frameworks that predate Western tech by centuries. The Tractatus Framework is developed in Aotearoa New Zealand, and we recognize Te Tiriti o Waitangi (the Treaty of Waitangi, 1840) as establishing principles of partnership, protection, and participation that directly inform how we think about AI sovereignty.
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</p>
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<p class="text-gray-700" data-i18n-html="core_values.te_tiriti.paragraph_2_full">
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<span data-i18n="core_values.te_tiriti.paragraph_2">This isn't performative acknowledgment. Concepts like</span> <em data-i18n="core_values.te_tiriti.rangatiratanga">rangatiratanga</em> <span data-i18n="core_values.te_tiriti.rangatiratanga_translation">(self-determination)</span>, <em data-i18n="core_values.te_tiriti.kaitiakitanga">kaitiakitanga</em> <span data-i18n="core_values.te_tiriti.kaitiakitanga_translation">(guardianship)</span>, and <em data-i18n="core_values.te_tiriti.mana">mana</em> <span data-i18n="core_values.te_tiriti.mana_translation">(authority and dignity)</span> <span data-i18n="core_values.te_tiriti.paragraph_2_end">provide concrete guidance for building AI systems that respect human agency across cultural contexts.</span> <a href="/about/values.html#te-tiriti" class="text-blue-600 hover:text-blue-700 font-medium underline" data-i18n="core_values.te_tiriti.read_more_link">Read our complete approach to Te Tiriti and indigenous data sovereignty →</a>
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</p>
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</div>
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<div class="space-y-6">
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<div class="bg-white rounded-lg shadow-sm p-6 border-l-4 border-green-600">
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<h3 class="text-xl font-bold text-gray-900 mb-3" data-i18n="core_values.sovereignty_title">Sovereignty</h3>
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<p class="text-gray-600" data-i18n="core_values.sovereignty_desc">
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Individuals and communities must maintain control over decisions affecting their data, privacy, and values. AI systems must preserve human agency, not erode it.
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</p>
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</div>
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<div class="bg-white rounded-lg shadow-sm p-6 border-l-4 border-blue-600">
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<h3 class="text-xl font-bold text-gray-900 mb-3" data-i18n="core_values.transparency_title">Transparency</h3>
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<p class="text-gray-600" data-i18n="core_values.transparency_desc">
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All AI decisions must be explainable, auditable, and reversible. No black boxes. Users deserve to understand how and why systems make choices, and have power to override them.
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</p>
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</div>
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<div class="bg-white rounded-lg shadow-sm p-6 border-l-4 border-purple-600">
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<h3 class="text-xl font-bold text-gray-900 mb-3" data-i18n="core_values.harmlessness_title">Harmlessness</h3>
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<p class="text-gray-600" data-i18n="core_values.harmlessness_desc">
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AI systems must not cause harm through action or inaction. This includes preventing drift, detecting degradation, and enforcing boundaries against values erosion.
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</p>
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</div>
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<div class="bg-white rounded-lg shadow-sm p-6 border-l-4 border-orange-600">
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<h3 class="text-xl font-bold text-gray-900 mb-3" data-i18n="core_values.community_title">Community</h3>
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<p class="text-gray-600" data-i18n="core_values.community_desc">
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AI safety is a collective endeavor. We are committed to open collaboration, knowledge sharing, and empowering communities to shape the AI systems that affect their lives.
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</p>
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</div>
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<div class="bg-white rounded-lg shadow-sm p-6 border-l-4 border-teal-600">
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<h3 class="text-xl font-bold text-gray-900 mb-3" data-i18n="core_values.pluralism_title">Pluralism</h3>
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<p class="text-gray-600" data-i18n="core_values.pluralism_desc">
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Different communities hold different, equally legitimate values. AI systems must respect this pluralism structurally, not by pretending one framework can serve all contexts. Value decisions require deliberation among affected stakeholders, not autonomous AI choices.
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</p>
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</div>
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</div>
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<div class="mt-8 text-center">
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<a href="/about/values.html" class="inline-block bg-blue-600 text-white px-6 py-3 rounded-lg font-semibold hover:bg-blue-700 transition" data-i18n="core_values.read_values_btn">
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Read Our Complete Values Statement →
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</a>
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</div>
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</section>
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<!-- Data Practices Section -->
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<section class="mb-16">
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<h2 class="text-3xl font-bold text-gray-900 mb-6" data-i18n="data_practices.heading">Tractatus Data Practices</h2>
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<div class="prose prose-lg text-gray-600 mb-6">
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<p data-i18n="data_practices.intro">
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We practice what we preach—transparent data handling with architectural constraints:
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</p>
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</div>
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<div class="grid md:grid-cols-2 gap-6 mb-6">
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<div class="border-l-4 border-blue-600 pl-4">
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<h3 class="text-xl font-bold text-gray-900 mb-2" data-i18n="data_practices.what_data_title">What Personal Data?</h3>
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<p class="text-gray-700" data-i18n="data_practices.what_data_desc">
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Audit logs may contain: usernames, timestamps, session IDs, action descriptions. No tracking cookies, no behavioral profiling, no cross-site data collection.
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</p>
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</div>
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<div class="border-l-4 border-blue-600 pl-4">
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<h3 class="text-xl font-bold text-gray-900 mb-2" data-i18n="data_practices.why_needed_title">Why Needed?</h3>
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<p class="text-gray-700" data-i18n="data_practices.why_needed_desc">
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Framework operation requires audit trails for governance decisions. BoundaryEnforcer logs blocked actions, CrossReferenceValidator logs instruction conflicts.
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</p>
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</div>
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<div class="border-l-4 border-blue-600 pl-4">
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<h3 class="text-xl font-bold text-gray-900 mb-2" data-i18n="data_practices.retention_title">How Long Retained?</h3>
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<p class="text-gray-700" data-i18n="data_practices.retention_desc">
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Configurable retention (default 90 days). Organizations can set retention based on their compliance requirements.
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</p>
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</div>
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<div class="border-l-4 border-blue-600 pl-4">
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<h3 class="text-xl font-bold text-gray-900 mb-2" data-i18n="data_practices.rights_title">Your Rights (GDPR)</h3>
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<p class="text-gray-700" data-i18n-html="data_practices.rights_full">
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<span data-i18n="data_practices.rights_desc">Access (Article 15), Deletion (Article 17), Portability (Article 20).</span>
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Contact: <a href="mailto:john.stroh.nz@pm.me" class="text-blue-600 hover:underline" data-i18n="data_practices.contact_email">john.stroh.nz@pm.me</a>
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</p>
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</div>
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</div>
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<p class="text-sm text-gray-600 italic" data-i18n="data_practices.architectural_note">
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Architectural principle: Data minimization is a system constraint, not a policy hope. BoundaryEnforcer prevents PII exposure structurally—audit trails provide compliance evidence.
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</p>
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</section>
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<!-- How It Works -->
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<section class="mb-16">
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<h2 class="text-3xl font-bold text-gray-900 mb-6" data-i18n="how_it_works.heading">How It Works</h2>
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<div class="prose prose-lg text-gray-600">
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<p class="mb-6" data-i18n="how_it_works.intro">
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The Tractatus Framework consists of six integrated components that work together to enforce structural safety:
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</p>
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</div>
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<div class="grid grid-cols-1 md:grid-cols-2 gap-6 mt-6">
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<div class="bg-gray-50 rounded-lg p-6">
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<h3 class="text-lg font-bold text-gray-900 mb-2" data-i18n="how_it_works.classifier_title">InstructionPersistenceClassifier</h3>
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<p class="text-sm text-gray-600" data-i18n="how_it_works.classifier_desc">
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Classifies instructions by quadrant (Strategic, Operational, Tactical, System, Stochastic) and determines persistence level (HIGH/MEDIUM/LOW/VARIABLE).
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</p>
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</div>
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<div class="bg-gray-50 rounded-lg p-6">
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<h3 class="text-lg font-bold text-gray-900 mb-2" data-i18n="how_it_works.validator_title">CrossReferenceValidator</h3>
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<p class="text-sm text-gray-600" data-i18n="how_it_works.validator_desc">
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Validates AI actions against stored instructions to prevent pattern recognition bias (like the 27027 incident where AI's training patterns immediately overrode user's explicit "port 27027" instruction).
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</p>
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</div>
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<div class="bg-gray-50 rounded-lg p-6">
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<h3 class="text-lg font-bold text-gray-900 mb-2" data-i18n="how_it_works.boundary_title">BoundaryEnforcer</h3>
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<p class="text-sm text-gray-600" data-i18n="how_it_works.boundary_desc">
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Ensures AI never makes values decisions without human approval. Privacy trade-offs, user agency, cultural context—these require human judgment.
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</p>
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</div>
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<div class="bg-gray-50 rounded-lg p-6">
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<h3 class="text-lg font-bold text-gray-900 mb-2" data-i18n="how_it_works.pressure_title">ContextPressureMonitor</h3>
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<p class="text-sm text-gray-600" data-i18n="how_it_works.pressure_desc">
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Detects when session conditions increase error probability (token pressure, message length, task complexity) and adjusts behavior or suggests handoff.
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</p>
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</div>
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<div class="bg-gray-50 rounded-lg p-6">
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<h3 class="text-lg font-bold text-gray-900 mb-2" data-i18n="how_it_works.metacognitive_title">MetacognitiveVerifier</h3>
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<p class="text-sm text-gray-600" data-i18n="how_it_works.metacognitive_desc">
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AI self-checks complex reasoning before proposing actions. Evaluates alignment, coherence, completeness, safety, and alternatives.
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</p>
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</div>
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<div class="bg-gray-50 rounded-lg p-6">
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<h3 class="text-lg font-bold text-gray-900 mb-2" data-i18n="how_it_works.deliberation_title">PluralisticDeliberationOrchestrator</h3>
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<p class="text-sm text-gray-600" data-i18n="how_it_works.deliberation_desc">
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When AI encounters values decisions—choices with no single "correct" answer—coordinates deliberation among affected stakeholders rather than making autonomous choices. Preserves human agency over moral decisions.
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</p>
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</div>
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</div>
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<div class="mt-8 text-center">
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<a href="/implementer.html" class="inline-block bg-purple-600 text-white px-6 py-3 rounded-lg font-semibold hover:bg-purple-700 transition" data-i18n="how_it_works.read_technical_btn">
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Read Technical Documentation & Implementation Guide →
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</a>
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</div>
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</section>
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<!-- Origin Story -->
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<section class="mb-16">
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<h2 class="text-3xl font-bold text-gray-900 mb-6" data-i18n="origin_story.heading">Origin Story</h2>
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<div class="prose prose-lg text-gray-600">
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<p class="mb-6" data-i18n="origin_story.paragraph_1">
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The Tractatus Framework emerged from real-world AI failures experienced during extended Claude Code sessions. The "27027 incident"—where AI's training patterns immediately overrode an explicit instruction (user said "port 27027", AI used "port 27017")—revealed that traditional safety approaches were insufficient. This wasn't forgetting; it was pattern recognition bias autocorrecting the user.
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</p>
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<p class="mb-6" data-i18n="origin_story.paragraph_2">
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After documenting multiple failure modes (pattern recognition bias, values drift, silent degradation), we recognized a pattern: AI systems lacked structural constraints. They could theoretically "learn" safety, but in practice their training patterns overrode explicit instructions, and the problem gets worse as capabilities increase.
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</p>
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<p class="mb-4" data-i18n="origin_story.paragraph_3">
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The solution wasn't better training—it was architecture. Drawing inspiration from Wittgenstein's insight that some things lie beyond the limits of language (and thus systematization), we built a framework that enforces boundaries through structure, not aspiration.
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</p>
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The Tractatus Framework is open source under the <strong>Apache License 2.0</strong>. We encourage:
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<a href="/LICENSE" class="text-blue-600 hover:text-blue-700 font-medium" data-i18n="license.view_license_link">View full Apache 2.0 License →</a>
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