Introduction

Data science is at the heart of modern decision-making, powering innovations in AI, machine learning, big data analytics, and automation. However, the increasing reliance on data introduces legal and ethical risks, including privacy concerns, regulatory compliance, intellectual property (IP) issues, and AI accountability.

This course provides a comprehensive understanding of global legal frameworks affecting data science projects, covering GDPR, CCPA, AI regulations, data governance, cybersecurity, and algorithmic fairness. Participants will explore real-world case studies, legal risk management, and compliance strategies to future-proof their data science initiatives.

Course Objectives

By the end of this training, participants will be able to:

• Navigate data protection laws (GDPR, CCPA, HIPAA, etc.) and ensure regulatory compliance.
• Understand intellectual property (IP) rights, data ownership, and licensing in data-driven projects.
• Identify and mitigate legal risks in AI and automated decision-making.
• Apply ethical AI principles to ensure fairness, transparency, and accountability.
• Implement cybersecurity best practices and data governance frameworks.
• Develop contracts, policies, and strategies to manage legal risks in data science projects.

Who Should Attend?

This course is ideal for professionals working at the intersection of data science, law, compliance, and technology, including:

• Data scientists and AI developers.
• Legal professionals and compliance officers.
• Data protection officers (DPOs) and cybersecurity experts.
• Business leaders and technology strategists.
• Policymakers and regulatory officials overseeing data governance.

5-Day Course Outline

Day 1: Legal Foundations of Data Science & AI Regulation

• The Role of Law in Data Science & AI Development.
• Overview of Data Protection & Privacy Laws: GDPR, CCPA, HIPAA, etc.
• Regulatory Frameworks for AI & Automated Decision-Making.
• Case Study: Legal consequences of data misuse (e.g., Cambridge Analytica, Clearview AI).

Day 2: Data Ownership, Intellectual Property (IP) & Contracts

• Who Owns the Data? Understanding Data Rights & Responsibilities.
• IP Protection for AI Models, Algorithms & Databases.
• Contracts & Licensing for Data Use: Open-source vs. proprietary data.
• Workshop: Drafting a data-sharing agreement with legal safeguards.

Day 3: AI Ethics, Algorithmic Bias & Accountability

• Legal & Ethical Implications of Algorithmic Bias & Discrimination.
• AI Transparency & Explainability: Regulatory expectations & best practices.
• Ensuring Fairness & Non-Discrimination in Data Models.
• Interactive Exercise: Analyzing a biased AI system & proposing legal fixes.

Day 4: Cybersecurity, Data Breaches & Compliance Risks

• Cybersecurity Laws & Regulations: Data breach reporting & liability.
• Cross-Border Data Transfers & Legal Challenges.
• Data Governance & Risk Management Frameworks.
• Case Study: Managing a real-world data breach & legal response strategies.

Day 5: Future-Proofing Data Science Projects – Legal & Compliance Strategies

• AI & Emerging Technologies: Legal challenges in autonomous systems & generative AI.
• The Role of Governments & Policy in AI & Data Regulation.
• Building a Legal Compliance & Ethics Framework for Data Science Teams.
• Final Capstone Project: Developing a legally compliant & ethical AI strategy for an organization.