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Artificial Intelligence and the Value Alignment Problem: A Philosophical Introduction de Travis LaCroix

Artificial Intelligence and the Value Alignment Problem: A Philosophical Introduction

Autor Travis LaCroix
en Limba Engleză Paperback – 9 mai 2025

Găsim în lucrarea Artificial Intelligence and the Value Alignment Problem o abordare riguroasă a intersecției dintre filozofia morală, informatică și științele sociale. Travis LaCroix propune o perspectivă interdisciplinară esențială, argumentând că alinierea sistemelor IA cu valorile umane nu este doar o provocare tehnică, ci una structurală. Reținem modul în care autorul reușește să unifice teme aparent disparate ale eticii digitale — de la opacitatea algoritmilor la echitate — sub umbrela centrală a problemei alinierii valorilor.

Volumul este organizat metodic pentru a sprijini progresia învățării în mediul academic. Prima parte stabilește contextul prin reevaluarea istoriei IA și a arhitecturilor actuale de rețele neuronale, în timp ce partea a doua analizează axele specifice ale alinierii, precum obiectivele și algoritmii de învățare. Această ediție publicată de Broadview Press se distinge prin claritatea cu care explică „scaling laws” și metodele de evaluare, transformând concepte tehnice abstracte în fundament pentru dezbaterea etică.

Considerăm această lucrare o alternativă structurată la AI Ethics de Paula Boddington pentru cursurile de filozofia tehnologiei sau etică aplicată, cu avantajul că Travis LaCroix oferă o definiție structurală nouă care permite cititorului să clasifice problemele de „bias” algoritmic ca instanțe specifice de eșec al alinierii. Spre deosebire de abordările mai teoretice, volumul de față integrează numeroase studii de caz care ancorează discuția în realitatea implementării IA, făcând-o indispensabilă pentru înțelegerea modului în care funcționează de fapt sistemele autonome astăzi.

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Specificații

ISBN-13: 9781554816293
ISBN-10: 1554816297
Pagini: 354
Dimensiuni: 152 x 229 x 17 mm
Greutate: 0.51 kg
Editura: BROADVIEW PR
Colecția Broadview Press
Locul publicării:Peterborough, Canada

De ce să citești această carte

Această introducere filozofică este recomandată studenților și cercetătorilor care doresc să înțeleagă fundamentul tehnic al dilemelor etice din IA. Cititorul câștigă o viziune clară asupra modului în care obiectivele programate pot devia de la intențiile umane. Este o resursă valoroasă pentru cursuri universitare, oferind un echilibru între rigoarea analitică și exemple practice, facilitând navigarea prin concepte complexe precum transparența și echitatea algoritmică.

Recenzii

Written for an interdisciplinary audience, this book provides strikingly clear explanations of the many difficult technical and moral concepts central to discussions of ethics and AI. In particular, it serves as an introduction to the value alignment problem: that of ensuring that AI systems are aligned with the values of humanity. LaCroix redefines the problem as a structural one, showing the reader how various topics in AI ethics, from bias and fairness to transparency and opacity, can be understood as instances of the key problem of value alignment. Numerous case studies are presented throughout the book to highlight the significance of the issues at stake and to clarify the central role of the value alignment problem in the many ethical challenges facing the development and implementation of AI.

“Travis LaCroix’s book on value alignment is, without a doubt, the best I have read on AI ethics. I highly recommend it to anyone interested in the ethics of artificial intelligence. The text is intellectually rigorous, and many of its ideas are genuinely novel. I found his discussion of measuring value alignment particularly insightful, along with the appendix on superintelligence and the control problem, which provides valuable depth to the topic.” — Martin Peterson, Texas A&M University
“LaCroix’s Artificial Intelligence and the Value Alignment Problem offers an insightful overview and evaluation of the predicament we find ourselves in with respect to machine learning. The book doesn't shy away from engaging with the mathematical background of these challenges, but it does so in a way that’s intelligible to readers with limited mathematical experience. The structural characterization of the alignment problem(s) provides a great conceptual tool for exploring the ways that values are (or fail to be) incorporated in machine learning systems. The discussions of values are also inclusive, incorporating views from Western, Eastern, and Indigenous philosophy. This book offers an up-to-date introduction to the topic at a level suitable for undergraduates while also providing a novel analytic tool for anyone already working in the area of AI ethics.” — Gillman Payette, University of Calgary

Descriere

Written for an interdisciplinary audience, this book provides strikingly clear explanations of the many difficult technical and moral concepts central to discussions of ethics and AI. In particular, it serves as an introduction to the value alignment problem: that of ensuring that AI systems are aligned with the values of humanity. LaCroix redefines the problem as a structural one, showing the reader how various topics in AI ethics, from bias and fairness to transparency and opacity, can be understood as instances of the key problem of value alignment. Numerous case studies are presented throughout the book to highlight the significance of the issues at stake and to clarify the central role of the value alignment problem in the many ethical challenges facing the development and implementation of AI.

Cuprins

List of Figures
List of Tables
List of Cases
Preface
Acknowledgements
Introduction

I Basic Concepts

  • 1 A Brief History of Artificial Intelligence
    • 1.1 The Idea of AI
    • 1.2 The Invention of AI
    • 1.3 First-Wave AI: False Promises
    • 1.4 Second-Wave AI: Empty Threats
    • 1.5 Third-Wave AI: Deep Hype
    • 1.6 Summary
  • 2 Artificial Intelligence Today
    • 2.1 Neural Network Architectures
    • 2.2 Data and Datasets
    • 2.3 Machine Learning Methods
    • 2.4 Objectives, Goals, and Values
    • 2.5 Learning Algorithms
    • 2.6 Evaluation
    • 2.7 Scaling Laws
    • 2.8 Summary
  • 3 The Value Alignment Problem
    • 3.1 The Standard Definition of Value Alignment
    • 3.2 Adding Sophistication to the Standard Definition
    • 3.3 The Principal-Agent Framework
    • 3.4 The Value Alignment Problem for Artificial Intelligence
    • 3.5 Benefits of the Structural Definition

II Axes of Value Alignment

Introduction to Part II
  • 4 Objectives
    • 4.1 Proxies and Abstractions
    • 4.2 Insights from the Structural Definition
    • 4.3 Bias and Fairness
    • 4.4 Algorithmic Bias
    • 4.5 The Social Character of Objectives
    • 4.6 Summary
  • 5 Information
    • 5.1 Informational Asymmetries, Economic and Artificial
    • 5.2 Transparency and Opacity
    • 5.3 Explainability, Interpretability, and Understanding
    • 5.4 Data and Datasets
    • 5.5 Interaction Effects
    • 5.6 Summary
  • 6 Principals
    • 6.1 Principals and Their Goals
    • 6.2 The Values of Humanity
    • 6.3 The Values Encoded in AI Research
    • 6.4 The Human Costs of Artificial Intelligence
    • 6.5 Interaction Effects
    • 6.6 Summary

III Approaches to Value Alignment

Introduction to Part III
  • 7 AI Safety
    • 7.1 Adversarial Examples
    • 7.2 Concrete Problems in AI Safety
    • 7.3 Mitigating Risk
    • 7.4 AI Safety and the Value Alignment Problem
    • 7.5 Summary
  • 8 Machine Ethics
    • 8.1 Artificial Moral Agency
    • 8.2 Our Best Normative Theories
    • 8.3 Technical Approaches to Artificial Moral Agency
    • 8.4 Critiques of Artificial Moral Agency
    • 8.5 Related Concepts
    • 8.6 Summary

IV Mitigating Misalignment

Introduction to Part IV
  • 9 Measuring Degrees of Alignment
    • 9.1 Benchmarking
    • 9.2 Benchmarking Ethics
    • 9.3 Aligning Values
    • 9.4 Degrees of Alignment
    • 9.5 The Scaling Hypothesis for Value-Aligned AI
    • 9.6 Summary
  • 10 Normativity and Language
    • 10.1 Linguistic Communication
    • 10.2 Language in Human Value Alignment
    • 10.3 Language, Value Alignment, and Information Transfer
    • 10.4 Objective Functions and Value Proxies
    • 10.5 Implications
    • 10.6 Summary
  • 11 Values and Value-Ladenness
    • 11.1 The Value-Free Ideal of Science
    • 11.2 Against the Value-Free Ideal
    • 11.3 Values and Value Alignment
    • 11.4 Optimism
    • 11.5 Regulation
    • 11.6 Summary
  • 12 Conclusion

V Appendix

  • A Superintelligence and Control
    • A.1 Superintelligence
    • A.2 Paths to Superintelligence
    • A.3 Forms of Superintelligence
    • A.4 Intelligence Explosion and the Singularity
    • A.5 Existential Risk
    • A.6 Intelligence, Motivation, and Goals
    • A.7 The Control Problem
    • A.8 Criticism
    • A.9 Summary
  • References
    Index