LAWS2075: AI Regulation Module 1

Module 1: The Complexities of AI Regulation

• AI regulation module for LAWS2075, Southern Cross University
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Module Overview

• Introduces students to the complexities of AI technologies
• Covers technical intricacies, security challenges, and potential biases
• Aims to equip students with foundational understanding for regulatory landscape study

Module Purpose

• Students will be able to analyze technological complexities of AI systems (machine learning, neural networks)
• Assess security risks associated with AI (adversarial attacks/data poisoning) and discuss mitigation approaches
• Evaluate potential biases in AI systems (algorithmic, data, human biases) and develop strategies for fairness
• Evaluate AI governance concerns (job displacement, hyper-surveillance)

Introduction to AI Technologies

• Explains that AI is a transformative force
• Outlines fundamental concepts (machine learning, narrow/general AI) and components of AI systems
• Highlights that lawyers require technical understanding of AI for effective client service
• Emphasizes the importance of advocating for responsible AI development and regulation

Legal Implications of AI

• AI raises legal issues like data privacy, intellectual property, liability, anti-discrimination, etc.
• Highlights the need for lawyers to understand underlying AI technology for informed advice
• Discusses changing legal practice due to automation and increased client expectations of modern services

Client Expectations and Risk Management

• Clients expect legal advice regarding relevant tech issues (cybersecurity, data protection, ethical deployment)
• Highlights lawyer's role in anticipation and management of risks associated with AI
• Reinforces competence expectations for legal professionals

Ethical and Professional Responsibility

• Emphasizes the duty of lawyers to maintain competence in technological areas (including AI)
• Highlights the importance of legal ethics rules in relation to technology competence
• Discusses risks associated with inadequate technical understanding and potential malpractice claims

Al in Litigation and Dispute Resolution

• Al tools increasingly used in litigation (e.g., predictive coding)
• Raises questions about evidence admissibility, algorithmic transparency, and bias relevance
• Covers the critical role of Al in litigation and dispute resolution for lawyers

Al and Fundamental Rights

• Discusses the intersection of Al and fundamental rights (privacy, freedom of expression, non-discrimination)
• Notes concern about biased algorithms and potential for unfair treatment and need for redress

Cybersecurity & Data Protection

• Emphasizes risks of cyberattacks and data breaches associated with AI systems
• Highlights the necessity of Al security best practices and compliance for legal counsel

Timeline of Key Developments

• Provides a detailed timeline of key developments in AI history, including key figures and events (e.g., Dartmouth Conference, development of expert systems, rise of big data)

Types of Artificial Intelligence

• Explains that AI is not a single technology, but rather a collection of subfields (generative, ML, NLP, Computer Vision)
• Explains Generative Al (creation of new content) and Machine Learning (patterns from data)
• Explains Natural Language Processing (understand, interpret, and generate language) and Computer Vision (interpret and make decisions from visual data)
• Differentiates narrow Al (specific tasks) and general Al/Strong Al (human-level intelligence)

Al Functionalities / How It Operates

• Describes simple Reactive Machines and more complex advanced systems (Limited Memory, Theory of Mind)
• Highlighting components of Al including algorithms and data, and computational power
• Explains how algorithms and data quality are crucial for effective operation

Computational Power

• Explains the importance of parallel processing and cloud computing for training deep learning models
• Highlights that GPU (Graphics Processing Units) and TPU (Tensor Processing Units) are key components
• Explains that cloud computing has democratized access to high-performance computing resources

Defining AI

• Defines AI according to different organizations/bodies, including OECD, ISO
• Highlights definitions from different organizations
• Explains variations in definitions based on the goals and use cases of the definitions

Key Differences Between Definitions

• Summarizes and compares definitions from ISO and OECD, highlighting differences based on focus, context use, and learning aspects.

Why Lawyers Should Know the Definitions of AI

• Explains the various ways AI technologies and their definitions affect legal practices and implications
• Expands on the significance of AI regulation and how it impacts and influence legal interpretation
• Discusses client expectations and liability that influence how lawyers advise and handle AI deployment risk

AI in Different Sectors (Healthcare, Finance, Social Services & Transportation)

• Briefly addresses areas of AI application in healthcare, finance, social services, and transportation
• Identifies relevant challenges and potential regulatory concerns for each application
• Highlights implications for regulatory intervention

Emerging Challenges Requiring Al Regulation

• Discusses current and emerging challenges of Al regulation related to Al content generation, intellectual property, trust issues and warfare

This module introduces students to the intricate world of AI technologies and their regulation. It covers crucial aspects like security challenges, potential biases, and the ethical implications of AI systems. By the end of the module, students will be equipped to analyze and assess the complexities involved in AI governance.