Separated out practices from Ai threats, got the intros working

Author: robmoffat

docs/ai/Practices/Human-In-The-Loop.md

@@ -0,0 +1,23 @@
+---
+title: Human In The Loop
+description:  Consistent human oversight in critical AI systems.
+featured: 
+  class: c
+  element: '<action>Human In The Loop</action>'
+tags: 
+  - Human In The Loop
+  - Practice
+practice:
+  mitigates:
+   - tag: Loss Of Human Control
+     reason: "Maintaining consistent human oversight in critical AI systems, ensuring that final decisions or interventions rest with human operators rather than the AI."
+---
+
+<PracticeIntro details={frontMatter} />
+
+- Maintaining consistent human oversight in critical AI systems, ensuring that final decisions or interventions rest with human operators rather than the AI.  
+- AI may suggest diagnoses or treatments, but a certified professional reviews and confirms before enacting them.  In the above NHS Grampian example, the AI is augmenting human decision making with a third opinion, rather than replacing human judgement altogether (yet).
+- Some proposals mandate that human operators confirm critical actions (e.g., missile launches), preventing AI from unilaterally making life-or-death decisions.  This might work in scenarios where response time isn't a factor.
+
+- **Efficacy:** Medium – Reduces risk by limiting autonomy on high-stakes tasks; however, humans may become complacent or fail to intervene effectively if over-trusting AI.  
+- **Ease of Implementation:** Moderate – Policy, regulatory standards, and user training are needed to embed human oversight effectively.

docs/ai/Practices/Kill-Switch.md

@@ -0,0 +1,27 @@
+---
+title: Kill Switch
+description:  Fail-safe systems capable of shutting down or isolating AI processes if they exhibit dangerous behaviours.
+featured: 
+  class: c
+  element: '<action>Kill Switch Mechanism</action>'
+tags: 
+  - Kill Switch
+  - Practice
+practice:
+  mitigates:
+   - tag: Loss Of Human Control
+     reason: "An explicit interruption capability can avert catastrophic errors or runaway behaviours"
+---
+
+<PracticeIntro details={frontMatter} />
+
+### Kill-Switch Mechanisms
+
+- **Examples:**  
+  - **Google DeepMind’s ‘Big Red Button’ concept** (2016), proposed as a method to interrupt a reinforcement learning AI without it learning to resist interruption.  
+  
+  - **Hardware Interrupts in Robotics:** Physical or software-based emergency stops that immediately terminate AI operation.  
+  
+- **Efficacy:** High – An explicit interruption capability can avert catastrophic errors or runaway behaviours, but it's more likely that they will be employed once the error has started, in order to prevent further harm.
+- **Ease of Implementation:** Medium – Requires robust design and consistent testing to avoid workarounds by advanced AI.
+

docs/ai/Practices/Replication-Control.md

@@ -0,0 +1,27 @@
+---
+title: Replication Control
+description:  TBD.
+featured: 
+  class: c
+  element: '<action>Replication Control</action>'
+tags: 
+  - Replication Control
+  - Practice
+practice:
+  mitigates:
+   - tag: Loss Of Human Control
+     reason: "An explicit interruption capability can avert catastrophic errors or runaway behaviours"
+---
+
+
+
+
+
+### Replication Control
+
+- Replication control becomes relevant when an AI system can duplicate itself—or be duplicated—beyond the reach of any central authority (analogous to a computer virus—though with potentially far greater autonomy and adaptability).  
+- An organization/person builds a very capable AI with some misaligned objectives. If they distribute its model or code openly, it effectively becomes “in the wild.”
+- Could controls be put in place to prevent this from happening? TODO: figure this out.
+
+- **Efficacy:** Medium – Limits the spread of potentially rogue AI copies.  
+- **Ease of Implementation:** Low – In open-source communities or decentralized systems, controlling replication requires broad consensus and technical enforcement measures.

docs/ai/Start.md

@@ -1,6 +1,6 @@
 ---
-title: Artificial Intelligence Risks
-description: Risk-First Track of articles on Bets within Software Development
+title: Artificial Intelligence Threats
+description: Risk-First Track of articles on Artificial Intelligence Threats
 
 
 featured: 
@@ -16,17 +16,15 @@ sidebar_position: 7
 
 A sequence looking at societal-level risks due to Artificial Intelligence (AI). 
 
-![AI Risks Diagram](/img/generated/risks/ai/future_risks.svg)
-
 ## Outcomes
 
 - Understand the main risks we face as society nurturing AI.
 - Understand which risks can be managed, which perhaps can't.
 
-## Risks
+## Threats
 
-<TagList filter="ai" tag="AI-Risk" />
+<TagList filter="ai" tag="AI Threats" />
 
 ## Practices
 
-<TagList filter="ai" tag="AI-Practice" />
+<TagList filter="ai" tag="Practice" />

docs/ai/Risks/Emergent-Behaviour.md renamed to docs/ai/Threats/Emergent-Behaviour.md

@@ -4,14 +4,17 @@ description: AI develops unforeseen behaviours, capabilities, or self-replicatio
 
 featured: 
   class: c
-  element: '<risk class="feature-fit">Emergent Behaviour</risk>'
+  element: '<risk class="feature-fit" /><description>Emergent Behaviour</description>'
 tags:
- - AI-Risk
- - Emergent-Behaviour
+ - AI Threats
+ - Emergent Behaviour
 sidebar_position: 2
 tweet: yes
+part_of: AI Threats
 ---
 
+<AIThreatIntro fm={frontMatter} />
+
 
 **Impact: 3** - While some emergent behaviors may be benign, others could lead to unintended or harmful consequences that are difficult to control.
 

docs/ai/Risks/Loss-Of-Diversity.md renamed to docs/ai/Threats/Loss-Of-Diversity.md

@@ -4,12 +4,16 @@ description: A single AI system dominates globally, leading to catastrophic cons
 
 featured: 
   class: c
-  element: '<risk class="lock-in">Loss Of Diversity</risk>'
+  element: '<risk class="lock-in" /><description>Loss Of Diversity</description>'
 tags:
- - AI-Risk
- - Loss-Of-Diversity
+ - AI Threats
+ - Loss Of Diversity
+part_of: AI Threats
 ---
 
+<AIThreatIntro fm={frontMatter} />
+
+
 **Impact: 3** - A lack of diversity could create system-wide vulnerabilities, where a single flaw in a dominant AI model causes widespread failure.
 
 ## Sources

docs/ai/Risks/Loss-Of-Human-Control.md renamed to docs/ai/Threats/Loss-Of-Human-Control.md

@@ -1,16 +1,21 @@
 ---
-title: Loss of Human Control
+title: Loss Of Human Control
 description: AI systems operating autonomously with minimal human oversight can lead to scenarios where we cannot override or re-align them with human values.
 featured:
   class: c
-  element: '<risk class="process">Loss of Human Control over AI</risk>'
+  element: | 
+    '<risk class="process" /><description style="text-align: center">Loss of 
+    Human Control</description>'
 tags:
- - AI-Risk
- - Loss-of-Human-Control
+ - AI Threats
+ - Loss Of Human Control
 sidebar_position: 3
 tweet: yes
+part_of: AI Threats
 ---
 
+<AIThreatIntro fm={frontMatter} />
+
 AI systems that act without robust human oversight can evolve in ways that defy our attempts at control or correction. In the short term, engineers have to wrestle with new approaches to defining acceptable behaviour (see Amodei et al):  even just cleaning an environment is a hard goal to pin down (clean doesn't mean devoid of any furniture, for example).  How do you allow the AI to learn and improve without enabling "Reward Hacking", where it finds ways to game the reward function (a la Goodhart's law).  
 
 The problem is that human oversight is _expensive_: we want to have a minimum level of oversight without worrying that things will go wrong. 
@@ -30,32 +35,3 @@ The problem is that human oversight is _expensive_: we want to have a minimum le
  - **[Boeing 737 MAX MCAS Issue (2018–2019)](https://mashable.com/article/boeing-737-max-aggressive-risky-ai):** Although not purely an AI system, automated flight software repeatedly overrode pilot inputs, contributing to two tragic crashes—illustrating how over-reliance on opaque automation can lead to disastrous outcomes.  This was caused by systemic failures at Boeing, driven by a cost-cutting culture and short-term focus on shareholder returns.
 
   - **Healthcare Diagnostic Tools:** Systems that recommend or even autonomously administer treatments based on patient data can outpace human doctors’ ability to review every decision, making interventions more difficult if the AI fails.  [NHS Grampian: breast cancer detection.](https://ukstories.microsoft.com/features/nhs-grampian-is-working-with-kheiron-medical-technologies-university-of-aberdeen-and-microsoft-to-support-breast-cancer-detection/)
-
-## Mitigations
-
-### Kill-Switch Mechanisms
-
-- **Description:** Fail-safe systems capable of shutting down or isolating AI processes if they exhibit dangerous behaviours.
-- **Examples:**  
-  - **Google DeepMind’s ‘Big Red Button’ concept** (2016), proposed as a method to interrupt a reinforcement learning AI without it learning to resist interruption.  
-  - **Hardware Interrupts in Robotics:** Physical or software-based emergency stops that immediately terminate AI operation.  
-- **Efficacy:** High – An explicit interruption capability can avert catastrophic errors or runaway behaviours, but it's more likely that they will be employed once the error has started, in order to prevent further harm.
-- **Ease of Implementation:** Medium – Requires robust design and consistent testing to avoid workarounds by advanced AI.
-
-### Human-in-the-Loop Controls
-
-- Maintaining consistent human oversight in critical AI systems, ensuring that final decisions or interventions rest with human operators rather than the AI.  
-- AI may suggest diagnoses or treatments, but a certified professional reviews and confirms before enacting them.  In the above NHS Grampian example, the AI is augmenting human decision making with a third opinion, rather than replacing human judgement altogether (yet).
-- Some proposals mandate that human operators confirm critical actions (e.g., missile launches), preventing AI from unilaterally making life-or-death decisions.  This might work in scenarios where response time isn't a factor.
-
-- **Efficacy:** Medium – Reduces risk by limiting autonomy on high-stakes tasks; however, humans may become complacent or fail to intervene effectively if over-trusting AI.  
-- **Ease of Implementation:** Moderate – Policy, regulatory standards, and user training are needed to embed human oversight effectively.
-
-### Replication Control
-
-- Replication control becomes relevant when an AI system can duplicate itself—or be duplicated—beyond the reach of any central authority (analogous to a computer virus—though with potentially far greater autonomy and adaptability).  
-- An organization/person builds a very capable AI with some misaligned objectives. If they distribute its model or code openly, it effectively becomes “in the wild.”
-- Could controls be put in place to prevent this from happening? TODO: figure this out.
-
-- **Efficacy:** Medium – Limits the spread of potentially rogue AI copies.  
-- **Ease of Implementation:** Low – In open-source communities or decentralized systems, controlling replication requires broad consensus and technical enforcement measures.

docs/ai/Risks/Social-Manipulation.md renamed to docs/ai/Threats/Social-Manipulation.md

@@ -4,14 +4,17 @@ description: AI could predict and shape human behaviour on an unprecedented scal
 
 featured: 
   class: c
-  element: '<risk class="communication">Social Manipulation</risk>'
+  element: '<risk class="communication" /><description>Social Manipulation</description>'
 tags:
- - AI-Risk
- - Social-Manipulation
+ - AI Threats
+ - Social Manipulation
 sidebar_position: 2
 tweet: yes
+part_of: AI Threats
 ---
 
+<AIThreatIntro fm={frontMatter} />
+
 AI systems designed to influence behaviour at scale could (and do) undermine democracy, free will, and individual autonomy.
 
 ## Sources
@@ -24,8 +27,6 @@ AI systems designed to influence behaviour at scale could (and do) undermine dem
 
 - **Nazi Propaganda** [United States Holocaust Memorial Museum](https://encyclopedia.ushmm.org/content/en/article/nazi-propaganda): Examines how the Nazi regime harnessed mass media—including radio broadcasts, film, and print—to shape public opinion, consolidate power, and foment anti-Semitic attitudes during World War.  (Fake content isn't a new problem.)
 
----
-
 ## How This Is Already Happening
 
 ### AI-Powered Targeted Advertising & Manipulation

docs/ai/Risks/Synthetic-Intelligence-Rivalry.md renamed to docs/ai/Threats/Synthetic-Intelligence-Rivalry.md

@@ -4,12 +4,17 @@ description: A single AI system dominates globally, leading to catastrophic cons
 
 featured: 
   class: c
-  element: '<risk class="lock-in">Synthetic Intelligence Rivalry</risk>'
+  element: | 
+    '<risk class="lock-in" /><description style="text-align: center">Synthetic Intelligence 
+    Rivalry</description>'
 tags:
- - AI-Risk
- - Synthetic-Intelligence-Rivalry
+ - AI Threats
+ - Synthetic Intelligence Rivalry
+part_of: AI Threats
 ---
 
+<AIThreatIntro fm={frontMatter} />
+
 **Impact: 3** - If AI entities did emerge as rivals, the consequences could range from economic disruption to conflicts over control of resources.
 
 ## Sources

docs/tags.yml

@@ -490,3 +490,40 @@
 "Lock-In Risk":
   label: "Lock-In Risk"
   permalink: "Lock-In-Risk"
+
+"Human In The Loop":
+  label: "Human In The Loop"
+  permalink: "Human-In-The-Loop"
+  
+"Emergent Behaviour":
+  label: "Emergent Behaviour"
+  permalink: "Emergent-Behaviour"
+
+"Loss Of Diversity":
+  label: "Loss Of Diversity"
+  permalink: "Loss-Of-Diversity"
+  
+"Loss Of Human Control":
+  label: "Loss of Human Control"
+  permalink: "Loss-of-Human-Control"
+
+"Social Manipulation":
+  label: "Social Manipulation"
+  permalink: "Social-Manipulation"
+
+"Synthetic Intelligence Rivalry":
+  label: "Synthetic Intelligence Rivalry"
+  permalink: "Synthetic-Intelligence-Rivalry"
+  
+"AI Threats":
+  label: "AI Threats"
+  permalink: "AI-Threats"
+
+"Kill Switch":
+  label: "Kill Switch"
+  permalink: "Kill-Switch"
+  
+"Replication Control":
+  label: "Replication Control"
+  permalink: "Replication-Control"
+