Introduction to AI Safety, Ethics and Society
Dan Hendrycks
This audiobook is narrated by a digital voice.
As AI technology is rapidly progressing in capability and being adopted more widely across society, it is more important than ever to understand the potential risks AI may pose and how AI can be developed and deployed safely. Introduction to AI Safety, Ethics, and Society offers a comprehensive and accessible guide to this topic.
This book explores a range of ways in which societies could fail to harness AI safely in coming years, such as malicious use, accidental failures, erosion of safety standards due to competition between AI developers or nation-states, and potential loss of control over autonomous systems. Grounded in the latest technical advances, this book offers a timely perspective on the challenges involved in making current AI systems safer. Ensuring that AI systems are safe is not just a problem for researchers in machine learning – it is a societal challenge that cuts across traditional disciplinary boundaries. Integrating insights from safety engineering, economics, and other relevant fields, this book provides readers with fundamental concepts to understand and manage AI risks more effectively.
Duration - 16h 53m.
Author - Dan Hendrycks.
Narrator - Digital Voice Maxwell G.
Published Date - Monday, 20 January 2025.
Copyright - © 2024 Dan Hendrycks ©.
Location:
United States
Description:
This audiobook is narrated by a digital voice. As AI technology is rapidly progressing in capability and being adopted more widely across society, it is more important than ever to understand the potential risks AI may pose and how AI can be developed and deployed safely. Introduction to AI Safety, Ethics, and Society offers a comprehensive and accessible guide to this topic. This book explores a range of ways in which societies could fail to harness AI safely in coming years, such as malicious use, accidental failures, erosion of safety standards due to competition between AI developers or nation-states, and potential loss of control over autonomous systems. Grounded in the latest technical advances, this book offers a timely perspective on the challenges involved in making current AI systems safer. Ensuring that AI systems are safe is not just a problem for researchers in machine learning – it is a societal challenge that cuts across traditional disciplinary boundaries. Integrating insights from safety engineering, economics, and other relevant fields, this book provides readers with fundamental concepts to understand and manage AI risks more effectively. Duration - 16h 53m. Author - Dan Hendrycks. Narrator - Digital Voice Maxwell G. Published Date - Monday, 20 January 2025. Copyright - © 2024 Dan Hendrycks ©.
Language:
English
Introduction
Duration:00:06:30
Chapter 1 ■ Overview of Catastrophic AI Risks
Duration:00:00:05
1.1 INTRODUCTION
Duration:00:06:05
1.2 MALICIOUS USE
Duration:00:02:26
1.2.1 Bioterrorism
Duration:00:04:06
1.2.2 Unleashing AI Agents
Duration:00:03:01
1.2.3 Persuasive AIs
Duration:00:03:32
1.2.4 Concentration of Power
Duration:00:04:55
1.3 AI RACE
Duration:00:01:02
1.3.1 Military AI Arms Race
Duration:00:13:47
1.3.2 Corporate AI Race
Duration:00:06:29
1.3.3 Evolutionary Pressures
Duration:00:12:56
1.4 ORGANIZATIONAL RISKS
Duration:00:04:40
1.4.1 Accidents Are Hard to Avoid
Duration:00:04:25
1.4.2 Organizational Factors can Reduce the Chances of Catastrophe
Duration:00:12:02
1.5 ROGUE AIS
Duration:00:03:34
1.5.1 Proxy Gaming
Duration:00:05:22
1.5.2 Goal Drift
Duration:00:04:51
1.5.3 Power-Seeking
Duration:00:05:09
1.5.4 Deception
Duration:00:05:48
1.6 DISCUSSION OF CONNECTIONS BETWEEN RISKS
Duration:00:03:01
1.7 CONCLUSION
Duration:00:02:52
Chapter 2 ■Artificial Intelligence Fundamentals
Duration:00:00:05
2.1 INTRODUCTION
Duration:00:03:26
2.2 ARTIFICIAL INTELLIGENCE & MACHINE LEARNING
Duration:00:00:28
2.2.1 Artificial Intelligence
Duration:00:13:41
2.2.2 Types of AI
Duration:00:11:50
2.2.3 Machine Learning
Duration:00:18:11
2.2.4 Types of Machine Learning
Duration:00:06:49
2.3 DEEP LEARNING
Duration:00:05:38
2.3.1 Model Building Blocks
Duration:00:16:53
2.3.2 Training and Inference
Duration:00:11:38
2.3.3 History and Timeline of Key Architectures
Duration:00:05:18
2.3.4 Applications
Duration:00:02:03
2.4 SCALING LAWS
Duration:00:02:35
2.4.1 Scaling Laws in Deep Learning
Duration:00:06:15
2.5 SPEED OF AI DEVELOPMENT
Duration:00:06:57
2.6 CONCLUSION
Duration:00:00:28
2.6.1 Summary
Duration:00:05:02
Chapter 3 ■ Single-Agent Safety
Duration:00:00:04
3.1 INTRODUCTION
Duration:00:03:03
3.2 MONITORING
Duration:00:00:19
3.2.1 ML Systems are Opaque
Duration:00:03:42
3.2.2 Motivations for Transparency Research
Duration:00:02:26
3.2.3 Approaches to Transparency
Duration:00:09:42
3.2.4 Emergent Capabilities
Duration:00:03:20
3.2.5 Emergent Goal-Directed Behavior
Duration:00:05:23
3.2.6 Tail Risk: Emergent Goals
Duration:00:05:07
3.2.7 Evaluations and Anomaly Detection
Duration:00:02:25
3.3 ROBUSTNESS
Duration:00:00:51
3.3.1 Proxies in Machine Learning
Duration:00:01:31
3.3.2 Proxy Gaming
Duration:00:12:28
3.3.3 Adversarial Examples
Duration:00:04:11
3.3.4 Trojan Attacks and Other Security Threats
Duration:00:02:33
3.3.5 Tail Risk: AI Evaluator Gaming
Duration:00:04:30
3.4 ALIGNMENT
Duration:00:01:41
3.4.1 Deception
Duration:00:06:48
3.4.2 Deceptive Evaluation Gaming
Duration:00:04:10
3.4.3 Tail Risk: Deceptive Alignment and Treacherous Turns
Duration:00:03:40
3.4.4 Power
Duration:00:04:23
3.4.5 People Could Enlist AIs for Power Seeking
Duration:00:01:06
3.4.6 Power Seeking Can Be Instrumentally Rational
Duration:00:10:37
3.4.7 Structural Pressures Towards Power-Seeking AI
Duration:00:04:23
3.4.8 Tail Risk: Power-Seeking Behavior
Duration:00:01:43
3.4.9 Techniques to Control AI Systems
Duration:00:04:28
3.5 SYSTEMIC SAFETY
Duration:00:04:43
3.6 SAFETY AND GENERAL CAPABILITIES
Duration:00:05:29
3.7 CONCLUSION
Duration:00:05:58
Chapter 4 ■ Safety Engineering
Duration:00:02:39
4.1 RISK DECOMPOSITION
Duration:00:00:22
4.1.1 Failure Modes, Hazards, and Threats
Duration:00:01:29
4.1.2 The Classic Risk Equation
Duration:00:02:24
4.1.3 Framing the Goal as Risk Reduction
Duration:00:01:07
4.1.4 Disaster Risk Equation
Duration:00:00:25
4.1.5 Elements of the Risk Equation
Duration:00:01:48
4.1.6 Applying the Disaster Risk Equation
Duration:00:04:52
4.2 NINES OF RELIABILITY
Duration:00:05:36
4.3 SAFE DESIGN PRINCIPLES
Duration:00:01:45
4.3.1 Redundancy
Duration:00:01:13
4.3.2 Separation of Duties
Duration:00:01:14
4.3.3 Principle of Least Privilege
Duration:00:00:57
4.3.4 Fail-Safes
Duration:00:01:01
4.3.5 Antifragility
Duration:00:02:05
4.3.6 Negative Feedback Mechanisms
Duration:00:01:44
4.3.7 Transparency
Duration:00:00:41
4.3.8 Defense in Depth
Duration:00:02:44
4.3.9 Review of Safe Design Principles
Duration:00:00:23
4.4 COMPONENT FAILURE ACCIDENT MODELS AND METHODS
Duration:00:00:22
4.4.1 Swiss Cheese Model
Duration:00:03:32
4.4.2 Bow Tie Model
Duration:00:01:49
4.4.3 Fault Tree Analysis Method
Duration:00:01:51
4.4.4 Limitations
Duration:00:10:25
4.5 SYSTEMIC FACTORS
Duration:00:04:03
4.5.1 Systemic Accident Models
Duration:00:18:06
4.6 DRIFT INTO FAILURE AND EXISTENTIAL RISKS
Duration:00:03:59
4.7 TAIL EVENTS AND BLACK SWANS
Duration:00:00:39
4.7.1 Introduction to Tail Events
Duration:00:02:22
4.7.2 Tail Events Can Greatly Affect the Average Risk
Duration:00:01:48
4.7.3 Tail Events Can Be Identified From Frequency Distributions
Duration:00:01:50
4.7.4 A Caricature of Tail Events
Duration:00:02:40
4.7.5 Introduction to Black Swans
Duration:00:01:10
4.7.6 Known Unknowns and Unknown Unknowns
Duration:00:06:16
4.7.7 Implications of Tail Events and Black Swans for Risk Analysis
Duration:00:10:34
4.7.8 Identifying the Risk of Tail Events or Black Swans
Duration:00:04:26
4.8.1 Summary
Duration:00:05:29
4.8.2 Key Takeaways
Duration:00:04:03
Chapter 5 ■ Complex Systems
Duration:00:00:04
5.1 OVERVIEW
Duration:00:02:00
5.2 INTRODUCTION TO COMPLEX SYSTEMS
Duration:00:00:04
5.2.1 The Reductionist Paradigm
Duration:00:06:19
5.2.2 The Complex Systems Paradigm
Duration:00:05:07
5.2.3 Deep Learning Systems as Complex Systems
Duration:00:02:22
5.2.4 Complexity is Not a Dichotomy
Duration:00:01:49
5.2.5 The Hallmarks of Complex Systems
Duration:00:24:02
5.2.6 Social Systems as Complex Systems
Duration:00:09:56
5.3 COMPLEX SYSTEMS FOR AI SAFETY
Duration:00:00:05
5.3.1 General Lessons from Complex Systems
Duration:00:09:19
5.3.2 Puzzles, Problems, and Wicked Problems
Duration:00:05:18
5.3.3 Challenges With Interventionism
Duration:00:12:38
5.3.4 Systemic Issues
Duration:00:04:12
5.4 CONCLUSION
Duration:00:02:54
Chapter 6 ■ Beneficial AI and Machine Ethics
Duration:00:00:05
6.1 INTRODUCTION
Duration:00:05:10
6.2 LAW
Duration:00:00:51
6.2.1 The Case For Law
Duration:00:07:18
6.2.2 The Need for Ethics
Duration:00:06:27
6.3 FAIRNESS
Duration:00:01:19
6.3.1 Bias
Duration:00:03:00
6.3.2 Sources of Bias
Duration:00:07:16
6.3.3 AI Fairness Concepts
Duration:00:03:40
6.3.4 Limitations of Fairness
Duration:00:03:12
6.3.5 Approaches to Combating Bias and Improving Fairness
Duration:00:07:09
6.4 THE ECONOMIC ENGINE
Duration:00:01:17
6.4.1 Allocative Efficiency of Free Markets
Duration:00:03:35
6.4.2 Market Failures
Duration:00:09:38
6.4.3 Inequality
Duration:00:04:20
6.4.4 Growth
Duration:00:01:39
6.4.5 Beyond Economic Models
Duration:00:08:42
6.5 WELLBEING
Duration:00:00:39
6.5.1 Wellbeing as the Net Balance of Pleasure over Pain
Duration:00:01:09
6.5.2 Wellbeing as a Collection of Objective Goods
Duration:00:01:10
6.5.3 Wellbeing as Preference Satisfaction
Duration:00:05:05
6.5.4 Applying the Theories of Wellbeing
Duration:00:05:05
6.6 PREFERENCES
Duration:00:01:34
6.6.1 Revealed Preferences
Duration:00:06:15
6.6.2 Stated Preferences
Duration:00:07:00
6.6.3 Idealized Preferences
Duration:00:05:55
6.7 HAPPINESS
Duration:00:01:55
6.7.1 The General Approach to Happiness
Duration:00:05:32
6.7.2 Problems for Happiness-Focused Ethics
Duration:00:07:22
6.8 SOCIAL WELFARE FUNCTIONS
Duration:00:04:24
6.8.1 Measuring Social Welfare
Duration:00:17:34
6.9 MORAL UNCERTAINTY
Duration:00:00:04
6.9.1 Making Decisions Under Moral Uncertainty
Duration:00:12:46
6.9.2 Implementing a Moral Parliament in AI Systems
Duration:00:01:35
6.9.3 Advantages of a Moral Parliament
Duration:00:07:08
6.10 CONCLUSION
Duration:00:02:33
Chapter 7 ■ Collective Action Problems
Duration:00:00:04
7.1 MOTIVATION
Duration:00:10:02
7.2 GAME THEORY
Duration:00:00:04
7.2.1 Overview
Duration:00:03:03
7.2.2 Game Theory Fundamentals
Duration:00:02:56
7.2.3 The Prisoner’s Dilemma
Duration:00:12:20
7.2.4 The Iterated Prisoner’s Dilemma
Duration:00:30:49
7.2.5 Collective Action Problems
Duration:00:17:28
7.2.6 Summary
Duration:00:01:36
7.3 COOPERATION
Duration:00:26:40
7.3.1 Summary
Duration:00:02:24
7.4 CONFLICT
Duration:00:00:04
7.4.1 Overview
Duration:00:05:43
7.4.2 Bargaining Theory
Duration:00:02:50
7.4.3 Commitment Problems
Duration:00:10:50
7.4.4 Information Problems
Duration:00:05:13
7.4.5 Factors Outside of Bargaining Theory
Duration:00:04:19
7.4.6 Summary
Duration:00:01:39
7.5 EVOLUTIONARY PRESSURES
Duration:00:00:04
7.5.1 Overview
Duration:00:01:44
7.5.2 Generalized Darwinism
Duration:00:15:29
7.5.3 Levels of Selection and Selfish Behavior
Duration:00:14:40
7.5.4 Summary
Duration:00:00:33
7.6 CONCLUSION
Duration:00:04:41
Chapter 8 ■ Governance
Duration:00:00:04
8.1 INTRODUCTION
Duration:00:02:29
8.1.1 The Landscape
Duration:00:05:05
8.2 ECONOMIC GROWTH
Duration:00:09:30
8.3 DISTRIBUTION OF AI
Duration:00:06:14
8.3.1 Distribution of Access to AI
Duration:00:08:35
8.3.2 Distribution of Power Among AIs
Duration:00:09:41
8.4 CORPORATE GOVERNANCE
Duration:00:00:24
8.4.1 What Is Corporate Governance?
Duration:00:01:15
8.4.2 Legal Structure
Duration:00:01:57
8.4.3 Ownership Structure
Duration:00:02:20
8.4.4 Organizational Structure
Duration:00:02:09
8.4.5 Assurance
Duration:00:01:56
8.5 NATIONAL GOVERNANCE
Duration:00:00:50
8.5.1 Standards and Regulations
Duration:00:03:35
8.5.2 Liability for AI Harms
Duration:00:02:15
8.5.3 Targeted Taxation
Duration:00:01:34
8.5.4 Public Ownership over AI
Duration:00:00:54
8.5.5 Improving Resilience
Duration:00:02:43