The AI-Control-Problem must now be the Top Priority
The AI-Control-Problem must now be the Top Priority - Murat Durmus

The AI-Control-Problem must now be the Top Priority

The AI control problem refers to ensuring that artificial intelligence (AI) systems act in ways that are consistent with human values, goals, and interests. This complex problem involves understanding and mitigating the potential risks associated with AI while ensuring that these systems are trustworthy, safe, and ethical; To address the AI control problem, researchers and practitioners in the field of AI ethics and governance are working to develop the best practices, ethical principles, and technical solutions that can help ensure that AI systems behave in ways that are socially responsible and consistent with human values; This includes areas such as transparency, accountability, fairness, and explainability in AI systems and the development of AI systems that can learn from human feedback and preferences.

The following points should be considered:

  1. Safety:?The AI system should not cause harm to humans or the environment.
  2. Algorithm transparency:?Understanding how the AI system makes decisions should be possible.
  3. Bias:?The AI system should be trained on unbiased data to prevent discrimination and prejudice.
  4. Privacy:?The AI system should not compromise the privacy of individuals or organizations.
  5. Ethics:?The AI system should align with ethical and moral principles.
  6. Responsibility:?There should be clear accountability for the decisions made by the AI system.
  7. Sustainability:?The AI system should be designed to have a minimal environmental impact and to be energy efficient.
  8. Security:?The AI system should be protected from malicious attacks and breaches.
  9. Human-AI interaction:?The AI system should be designed for effective and safe human interaction.
  10. Interoperability:?The AI system should be compatible with other systems and technologies.


Murat


More thought-provoking thoughts:

Mindful AI: Reflections on Artificial Intelligence


Thought-Provoking Quotes & Reflections on Artificial Intelligence


New Book Release: Beyond the Algorithm: An Attempt to Honor the Human Mind in the Age of Artificial Intelligence (Wittgenstein Reloaded)


Deutsche Ausgabe:


Nikolaj Van Omme

Developing a better AI that optimise complex industrial problems by 20-40% in production!

2 年

It probably can not be solved within ML... but with OR this can be done...

POOJA JAIN

Storyteller | Linkedin Top Voice 2024 | Senior Data Engineer@ Globant | Linkedin Learning Instructor | 2xGCP & AWS Certified | LICAP'2022

2 年

Informative and important pick on #AI

Bill McLellan

HCLS Industry Principal | Senior Solutions Architect

2 年

Murat Durmus I've been wondering, could there be any usefulness to safe AI of combining linear/additive models where errors rebound/revert to mean vs exponential/multiplicative models where errors compound? Should AI that doesn't contain a human in the loop be regulated to use composite models containing both batch linear (safer, more explainable) and online exponential (more accurate, less explainable) models like reinforced learning so alerts go blaring when predictions, recommendations, or actions deviate significantly from the linear model over some period of time? Maybe we could catch the paperclip factory from turning the world into paperclips.

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