Ethical-Challenges-of-Generative-AI.pptx

Ethics

1. Ethical Challenges of Generative AI Generative AI, with its ability to create realistic content, has revolutionized digital creation but also raised significant ethical concerns. This presentation explores these challenges, examining their impact on academic integrity, intellectual property, privacy, and more. We will delve into the implications for various sectors, including academia, healthcare, and media. Join us as we navigate the ethical landscape of this powerful technology. Erick Wasonga

2. Generative AI: A Powerful Tool Deep Learning Models Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are at the forefront of generative AI research. They create content that closely mimics human-like creativity and understanding. Applications Across Domains Generative AI has applications in art, music, healthcare, and technology, pushing the boundaries of what is possible and fostering innovations once deemed the realm of science fiction.

3. Ethical Concerns of Generative AI Authorship and Academic Integrity The potential for plagiarism and misuse of AI-generated content in academic settings raises concerns about the decline of academic integrity. IPR, Copyright Issues The blurred lines of authorship and originality when AI generates content pose challenges for copyright protection and intellectual property rights. Privacy, Trust, and Bias The use of generative AI in healthcare raises concerns about patient data privacy, the perpetuation of biases, and the need for transparency in AI systems. Misinformation and Deepfakes The ability to create convincing deepfakes and generate misinformation poses threats to public discourse, trust, and the integrity of media.

4. Authorship and Academic Integrity 1 Verification Challenges AI’s ability to mimic human writing makes it difficult to distinguish between human-generated and AI-generated texts. 2 Predatory Journals These journals exploit AI to generate large volumes of low-quality scholarly articles, jeopardizing the credibility of academic publishing. 3 Solutions Developing AI output detectors, improving academic processes, and promoting ethical practices can help safeguard academic integrity.

5. IPR and Copyright Issues Authorship Attribution Can an AI be considered the author of a work, and how can we apply traditional copyright concepts to AI-generated content? Legal Frameworks Developing new legal frameworks that balance human creators' rights with broader public interests is essential for addressing the ethical implications of AI-generated works. Economic Concerns Granting copyright protection to AI-generated content could restrict knowledge sharing, curb innovation, and foster monopolistic practices.

6. Privacy, Trust, and Bias 1 Data Anonymization Removing personally identifiable information (PII) from patient data is crucial for preserving confidentiality in healthcare settings. 2 AI Training Data The training data used for AI models must be carefully curated to prevent the perpetuation of biases and ensure the integrity of healthcare outcomes. 3 Transparency and Explainability Promoting the development of transparent and interpretable AI systems is essential for maintaining public trust and safeguarding ethical standards.

7. Misinformation and Deepfakes Convincing Fabrications AI-generated content, especially deepfakes, can be almost indistinguishable from authentic information, leading to widespread deception and manipulation. Dissemination and Attribution The ease with which AI-generated misinformation can spread through social media makes containment efforts challenging. Combating Misinformation A holistic approach, including public education, cross-sector collaboration, and robust legal frameworks, is needed to address the challenge of AI-driven misinformation.

8. Social and Economic Impact 1 Employment Transformation Generative AI can create new jobs but also displace existing ones, requiring targeted reskilling and upskilling initiatives. 2 Public Discourse The potential for AI-generated misinformation necessitates policies to preserve the integrity of information and public trust. 3 Transparency and Accountability Promoting transparency and explainability in AI systems will ensure users understand the reasoning behind AI-driven decisions.

9. Toward Responsible AI Development The ethical challenges posed by generative AI demand a proactive approach. We must work towards creating a responsible AI that prioritizes human rights, fairness, and transparency. This requires collaborative efforts from policymakers, technologists, and researchers. By embracing ethical guidelines, implementing robust safeguards, and promoting open dialogue, we can harness the power of generative AI for the benefit of all.