A Hybrid Moral Codex for Human-AI Cohabitation

"The explosion of artificial intelligence represents more than technological evolution. It's a societal transformation. As AI-powered robots integrate into workplaces, homes, and public spaces, human-machine interaction lines blur. This cohabitation requires a comprehensive, adaptable ethical framework: a hybrid moral codex. For every human, but in particular every business leader, implementing such a codex should be pursued as a priority, because having and following it will be an asset to building trust in a hybrid society."

"For decades, science fiction offered Isaac Asimov's Three Laws of Robotics as an ethical blueprint. These laws required robots to avoid harming humans, to obey human orders (unless conflicting with harm prevention), and to protect their existence (unless conflicting with the first two laws). While foundational to robotic ethics in popular culture, though, these principles prove inadequate for modern AI complexities."

"Ambiguity of "Harm": What constitutes harm in AI contexts? Does it encompass only physical injury, or extend to economic displacement, psychological manipulation, or algorithmic bias perpetuating social inequalities? AI systems predicting job losses due to automation raise complex questions about harm that Asimov's laws don't resolve. Conflicting Directives: Modern AI faces trolley problems for which no outcome avoids harm entirely. Self-driving cars must choose between potentially harming occupants or pedestrians. Asimov's laws offer little guidance for such grey areas."

Artificial intelligence integration is transforming society as AI-powered robots enter workplaces, homes, and public spaces, blurring human-machine interaction lines. A comprehensive, adaptable ethical framework—a hybrid moral codex—is required to guide coexistence and to build trust, especially among business leaders. Asimov's Three Laws are inadequate for modern complexities because 'harm' is ambiguous, directives can conflict in trolley-like dilemmas, collective benefit is value-laden, and self-learning systems produce unintended consequences. Ethical guidance must address physical, economic, psychological, and social harms, resolve conflicting obligations, and adapt to evolving autonomous behaviors.

Read at Psychology Today