9 Bias and Fairness in Generative AI
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Mani Deepak Choudhry
Abstract
This chapter explores bias and fairness in generative AI, giving a comprehensive view that explores the deep relationship between artificial intelligence and human biases and what they provoke in the progress and implementation of generative prototypes. It is a thorough analysis of the biases in training data, algorithmic decisionmaking, and unintended consequences of AI applications, providing real-world examples. Exploration of the deep complications related to bias within generative AI systems and possible solutions will dominate this chapter. We shall try to relate those discussions to what are essentially ethical considerations and responsibilities embedded in their generation and use. This would provide us with a very clear foundation on which to discuss how one has to balance innovation and ethical issues in the field of AI. That humanist point of view will give way to the need to focus on broad societal implications regarding the development of AI.
Abstract
This chapter explores bias and fairness in generative AI, giving a comprehensive view that explores the deep relationship between artificial intelligence and human biases and what they provoke in the progress and implementation of generative prototypes. It is a thorough analysis of the biases in training data, algorithmic decisionmaking, and unintended consequences of AI applications, providing real-world examples. Exploration of the deep complications related to bias within generative AI systems and possible solutions will dominate this chapter. We shall try to relate those discussions to what are essentially ethical considerations and responsibilities embedded in their generation and use. This would provide us with a very clear foundation on which to discuss how one has to balance innovation and ethical issues in the field of AI. That humanist point of view will give way to the need to focus on broad societal implications regarding the development of AI.
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- About the Editors IX
- List of Contributors XI
- 1 Unveiling the Power of Generative AI: A Journey into Large Language Models 1
- 2 Early Roots of Generative AI Models and LLM: A Diverse Landscape 23
- 3 Generative AI Models and LLM: Training Techniques and Evaluation Metrics 43
- 4 Importance of Prompt Engineering in Generative AI Models 69
- 5 LLM Pretraining Methods 93
- 6 LLM Fine-Tuning: Instruction and Parameter-Efficient Fine-Tuning (PEFT) 117
- 7 Reinforcement Learning from Human Feedback (RLHF) 135
- 8 Exploring the Applications on Generative AI and LLM 155
- 9 Bias and Fairness in Generative AI 177
- 10 Future Directions and Open Problems in Generative AI 193
- 11 Optimizing Sustainable Project Management Life Cycle Using Generative AI Modeling 213
- 12 Generative AI and LLM: Case Study in Finance 231
- 13 Generative AI and LLM: Case Study in E-Commerce 253
- Index 273
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- About the Editors IX
- List of Contributors XI
- 1 Unveiling the Power of Generative AI: A Journey into Large Language Models 1
- 2 Early Roots of Generative AI Models and LLM: A Diverse Landscape 23
- 3 Generative AI Models and LLM: Training Techniques and Evaluation Metrics 43
- 4 Importance of Prompt Engineering in Generative AI Models 69
- 5 LLM Pretraining Methods 93
- 6 LLM Fine-Tuning: Instruction and Parameter-Efficient Fine-Tuning (PEFT) 117
- 7 Reinforcement Learning from Human Feedback (RLHF) 135
- 8 Exploring the Applications on Generative AI and LLM 155
- 9 Bias and Fairness in Generative AI 177
- 10 Future Directions and Open Problems in Generative AI 193
- 11 Optimizing Sustainable Project Management Life Cycle Using Generative AI Modeling 213
- 12 Generative AI and LLM: Case Study in Finance 231
- 13 Generative AI and LLM: Case Study in E-Commerce 253
- Index 273
