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b. Deepfakes: The term "deepfake" combines "deep learning" and "fake," referring to AI techniques that
create realistic but fake videos and audios. These AI-generated videos can mislead people by making
it seem like someone said or did something which they didn't, undermining trust in public figures and
institutions.
2. Lead to Job Displacement
a. Automation: Generative AI can perform tasks traditionally done by humans, such as writing, graphic
design, and customer service, potentially leading to significant job displacement.
b. Economic Disruption: Workers who lose their jobs may struggle financially, face higher unemployment,
and find it harder to get new jobs without the required skills.
3. Privacy and Data Security Risks
a. Sensitive Information: AI can inadvertently or maliciously generate sensitive personal information, like
social security numbers or medical records, which can be exploited for identity theft or fraud.
b. Data Breaches: The misuse of AI to access or generate personal data poses significant risks to an
individual's privacy and security.
4. Ethical and Moral Concerns
a. Bias and Discrimination: AI models trained on biased data can produce biased outputs, reinforcing
stereotypes and discrimination.
b. Dehumanisation: Relying too much on AI for tasks that need human understanding and compassion
can make services and interactions feel less personal and caring.
5. Security Threats
a. Cyber Attacks: AI can be used to develop sophisticated cyber-attacks, including generating malicious
code or automated phishing schemes.
b. Weaponisation: Generative AI could be used to create harmful technologies or weapons, posing
significant national security risks.
6. Environmental Impact
a. Training and running large AI models require significant computational resources, contributing to high
energy consumption and environmental impact.
b. Many devices are being exchanged due to outdated hardware leading to increase in e-waste.
Responsible Use of Generative AI
The responsible use of Generative AI involves:
• Ensuring that the training data used are diverse and representative. Use datasets that reflect a wide range
of demographics, cultures, and contexts to avoid biases in AI outputs. Regularly audit and adjust datasets to
address and reduce biases.
• The outputs are scrutinised for bias and misinformation. Implement tools and processes to detect biases in
AI-generated content.
• Prioritising user privacy and informed consent. Apply strong encryption and data anonymisation techniques to
protect user data. Ensure users are aware of and consent to how their data is being used by AI systems.
• Educating stakeholders on ethical use and risks. Provide ongoing education for developers on ethical AI practices
and the potential risks of AI misuse. Educate users about the capabilities, limitations, and ethical considerations
of AI technology.
• Establishing clear guidelines on ownership and attribution. Define and enforce guidelines regarding the
ownership of AI-generated content. Clearly attribute AI-generated content to its sources, distinguishing between
human and AI contributions.
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