Artificial Intelligence (AI) requires careful safeguards to mitigate potential risks. If released prematurely without proper precautions, AI systems can pose significant dangers, including disinformation, cyberattacks, fraud, privacy violations, bias, hate speech, and hallucinations. To address these risks, several safeguards should be implemented. These include ensuring transparency about the data used for training, testing the AI in controlled environments before deployment, and clearly indicating when content is AI-generated. Additionally, making AI systems explainable and providing mechanisms to disable AI remotely if it behaves undesirably are critical steps. However, this disabling mechanism must be well-planned, as users who depend on AI systems will need access to backup systems and information in case of failure.

While these safeguards focus on the technical and informational risks of AI, they also present challenges for responsible AI designers. Safeguards should extend beyond just the technology to address broader social, cultural, environmental, and ethical risks. By considering these wider impacts, AI can be developed and deployed more responsibly, ensuring accountability and minimizing harm to individuals and communities. This holistic approach is essential for building AI systems that serve the greater good.

Sources

Full Applied AI Lectures (MFML)

Safeguarding AI:
Addressing the Risks of Generative Artificial Intelligence
Paul M. barrett and Justin Hendrix J

https://bhr.stern.nyu.edu/wp-content/uploads/2024/01/NYUCBHRGenerativeAI_June20ONLINEFINAL.pdf