Generative AI: Impact, Considerations, and Ethical Issues PDF

12/28/2024 Generative AI: Impact, Considerations, and Ethical Issues 1 Today Introduction Limitation and Ethical Issues of Generative AI Social Impact Responsible Generative AI Economic Impact Responsible Generative AI...

12/28/2024 Generative AI: Impact, Considerations, and Ethical Issues 1 Today Introduction Limitation and Ethical Issues of Generative AI Social Impact Responsible Generative AI Economic Impact Responsible Generative AI 2 1  12/28/2024 What is Generative AI Generative AI refers to a subset of artificial intelligence that uses algorithms to generate new content based on training data. 3 Applications of Generative AI: o Content Creation o Healthcare o Gaming and Entertainment o Education o Marketing 4 2  12/28/2024 Ethics in Generative AI Deployment Ethical : o Transparency: Users should know when content is AI-generated. o Accountability: Ensuring organizations take responsibility for AI-generated errors. o Privacy: Safeguarding sensitive data used in AI systems. o Fairness: Avoiding bias in outputs to ensure equitable representation. 5 Ethics in Generative AI Deployment Potential Risks: o Biased content generation leading to discrimination. o Misuse of deepfakes for misinformation campaigns. o Plagiarism and intellectual property concerns. 6 3  12/28/2024 Activity ChatGPT for text-based help. Alexa and Google Assistant for voice commands. Customer service chatbots on websites. Did you discover any unexpected ethical issues? How can users and developers address the challenges? What role do regulations and policies play in ensuring ethical AI development? 7 Ethical Issues Data Privacy: Challenge: Generative AI often uses large datasets that may include sensitive personal information, leading to risks of data breaches or unauthorized sharing. Example: An AI-powered chatbot revealing customer information due to poor data handling practices. 8 4  12/28/2024 Ethical Issues Copyright : o Challenge: AI models trained on copyrighted material may generate content that resembles original works without proper attribution. o Example: An AI system generating artwork that closely resembles the style of a famous artist. 9 Ethical Issues Accountability: o Challenge: When AI generates biased or harmful outputs, it’s unclear who should be held responsible—the developer, the user, or the organization. o Example: A biased hiring AI rejecting qualified candidates based on gender or race. 10 5  12/28/2024 What are Hallucinations? o Hallucinations refer to AI generating incorrect, nonsensical, or factually untrue outputs. o Example: A chatbot fabricating a scientific fact that doesn't exist. Implications: o Misinformation: Spreading false information can damage public trust and lead to real-world consequences. o Safety Risks: Incorrect medical advice generated by AI can endanger lives. 11 Why is it important to validate AI-generated outputs before relying on them? Ensuring Accuracy and Avoiding Errors 12 6  12/28/2024 Why is it important to validate AI-generated outputs before relying on them? Ensuring Accuracy and Avoiding Errors Preventing the Spread of Misinformation 13 Why is it important to validate AI-generated outputs before relying on them? Ensuring Accuracy and Avoiding Errors Preventing the Spread of Misinformation Addressing Ethical Implications 14 7  12/28/2024 Why is it important to validate AI-generated outputs before relying on them? Ensuring Accuracy and Avoiding Errors Preventing the Spread of Misinformation Addressing Ethical Implications Mitigating Legal and Professional Risks 15 Why is it important to validate AI-generated outputs before relying on them? Ensuring Accuracy and Avoiding Errors Preventing the Spread of Misinformation Addressing Ethical Implications Mitigating Legal and Professional Risks Maintaining User Trust 16 8  12/28/2024 Why is it important to validate AI-generated outputs before relying on them? Ensuring Accuracy and Avoiding Errors Preventing the Spread of Misinformation Addressing Ethical Implications Mitigating Legal and Professional Risks Maintaining User Trust Avoiding Over-Reliance on AI Managing Safety Risks 17 Limitations of Generative AI 1. Dependence on Training Data: o AI models are only as good as the data they’re trained on. o Biased or incomplete datasets lead to limited or skewed outputs. o Example: An AI trained only on Western data may fail to understand non-Western contexts. 2. Lack of Explainability: o Complex AI models often operate as "black boxes," making it difficult to explain how a decision or output was generated. o Implication: Reduced trust and adoption in critical fields like healthcare and law. 18 9  12/28/2024 Exploring Methods to Mitigate Risks Transparency 19 Exploring Methods to Mitigate Risks Transparency Explainability 20 10  12/28/2024 Exploring Methods to Mitigate Risks Transparency Explainability Diverse Data Collection 21 Exploring Methods to Mitigate Risks Transparency Explainability Diverse Data Collection Algorithm Audits 22 11  12/28/2024 Exploring Methods to Mitigate Risks Transparency Explainability Diverse Data Collection Algorithm Audits Quality Metrics 23 Exploring Methods to Mitigate Risks Transparency Explainability Diverse Data Collection Algorithm Audits Quality Metrics Fairness Metrics 24 12  12/28/2024 Social and economic impacts of Generative AI Access to Knowledge Bias Amplification Job Automation and Creation 25 Strategies to Counteract Bias: Diverse Training Data Bias Detection Tools Human Oversight 26 13  12/28/2024 How Generative AI Enhances CSR? Sustainability Community Engagement Employee Empowerment 27 14