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Do No Harm: Is it Time for a Hippocratic Oath in AI?

As deep learning systems move from code completion to automated judicial roles, the ethical stakes have never been higher. This MIT Deep Learning session explores the moral and technical responsibilities of AI researchers in an era of complex autonomous systems.

Core themes:

The Risk-Reward Matrix: Evaluating project boundaries before launch.
The explainability gap: Why deep learning cannot always "justify" its decisions to humans.
Real-world failure modes: From legal hallucinations to financial risks in enterprise bots.
Regulatory friction: Analyzing the impact of the EU AI Act.
Ultimate Accountability: Why the "oath" remains a human burden, not a machine one.

In a self-organizing system, technical excellence must be balanced with rigorous ethical evaluation.

All my links: https://linktr.ee/learnbydoingwithsteven #learnbydoingwithsteven #AIEthics #DeepLearning #MIT #AI #TechEthics #MachineLearning #AIGovernance #SafeAI #DataScience

Do No Harm: Is it Time for a Hippocratic Oath in AI?

Steven AI Talk

Do No Harm: Is it Time for a Hippocratic Oath in AI?