Section 1: Data Privacy and Security

• The ethical concern surrounding data collection in AI is that users may not be aware their data is being used or how it’s being processed.
  
• GDPR (General Data Protection Regulation) is a regulation that aims to protect users' personal data in AI systems.
• Informed Consent, in regards to AI data collection, is users actively giving permission after understanding how their data will be used.
• AI systems have a responsibility to encrypt and anonymize its results when handling sensitive personal data.
• Data minimization refers to limiting the amount of data collected to what is strictly necessary.

Section 2: Protecting Artists, Authors, and Creators

• It is critical to protect artists, authors, and other creatives in AI development because unauthorized use of their work can lead to loss of income and intellectual property violations.
• There are many ways AI developers can ethically use copyrighted content, the foremost being obtaining proper licensing and permissions from creators.
• Fair Use is the limited use of copyrighted material for purposes like education, commentary, or research without seeking permission
• A popular suggestion to make AI-generated content more ethical is to acknowledge and compensate original creators for work used in the generation.
• Digital watermarking is a visible or invisible marker embedded in digital content to track usage and prevent unauthorized copying, meant to protect creative works.

Section 3: Accountability and Regulation in AI

• The role in human oversight in ethical AI is to ensure AI systems function in a responsible and fair manner
• AI decisions can have real-world consequences that affect people’s lives, which is why many advocate that companies be held accountable for their systems.
• It is recommended businesses conduct regular audits and impact assessments to ensure AI accountability.
• Regulations are important for businesses using AI tools, as they ensure AI is used responsibly and does not cause harm.
• Most forms of bias in AI training/results can be evaluated and measured by conducting third-party audits and fairness testing.

Section 4: The Future of Ethical AI

• Explainable AI (XAI) is AI that provides clear reasoning for its decisions
• Ethical AI development critical for the future because it ensures AI benefits society without causing harm
• AI developers can promote ethical AI adoption by designing AI with transparency, fairness, and accountability in mind.
• Consumers have the power to demand ethical practices and transparency from companies.
• Businesses benefit from adopting ethical AI practices by building trust with consumers and reducing legal risks.