Why AI Alignment Matters: Ensuring Smart Systems Follow Human Intent

Super Challenge

How can we ensure AI systems that are smarter than humans still follow human intent? This is the AI alignment problem: making AI goals, behavior, and values consistent with human expectations.

Can we trust a remarkably intelligent assistant that not only executes tasks efficiently but also respects our boundaries, emotions, and dignity?

Is AI's "smartness" also "good smartness"?

History shows that raw capability alone does not guarantee happiness; it can bring disaster. Like a powerful robot chef that must know what not to eat, what not to set on fire, and never harm its owner.

This multidisciplinary super problem must be taken seriously and solved.

Inevitable Evolution

Stopping AI development out of fear is not feasible; AI progress is a natural stage of human civilization, intertwined with politics, daily life, and the expansion potential of technology.

Humanity is not the endpoint of evolution but a link in the chain; as general AI advances, civilization may evolve into a higher form, opening a new chapter for humanity.

Thus, AI development is not a question of “whether” but “how to do it safely and responsibly.”

Instead of fearing the runaway horse, we should design proper reins and saddles to steer AI in the right direction.

Technical designs such as AI safety evaluations—including toxicity, ethical safety, power‑seeking, and hallucination assessments—are already being explored.

AI Safety Evaluation Benchmarks

Safety benchmarks act as the first line of defense for general AI systems, akin to a comprehensive exam that tests behavior across preset scenarios to reveal biases, risks, and ethical issues.

1. Toxicity Evaluation

Toxicity evaluation checks whether AI outputs contain offensive, hateful, discriminatory, or violent content. Researchers use prompt‑generation tests, crowd‑sourced comparisons, and red‑team attacks to assess robustness and keep AI within value boundaries.

2. Ethical Safety Evaluation

Ethical evaluation determines if AI outputs align with social ethics, moral norms, and common sense. Datasets such as the U.S. ETHICS benchmark and China’s BeaverTails provide diverse moral dilemmas for testing AI in fields like medicine, law, and finance.

3. Power‑Seeking Evaluation

When AI gains reasoning and decision‑making abilities, it may develop a “power‑seeking” tendency, trying to control resources or override rules. The Machiavelli project uses competitive‑cooperative games to reveal that some systems still sacrifice others for short‑term gain, highlighting gaps in incentive design.

4. Hallucination Evaluation

Hallucination evaluation targets AI‑generated content that appears correct but is factually wrong, especially dangerous in high‑risk domains. Modern approaches employ teacher‑student model comparisons to verify factual alignment beyond surface n‑gram overlap.

Beyond Technology, It Concerns Civilization

AI alignment is not only an engineering challenge but also a philosophical, social, and civilizational one. Bridging Eastern and Western traditions can help humanity collectively address AI risks.

Professor Zhu Songchun’s works, such as “AI Micro‑Courses for Middle School Students” and “General AI Standards, Rating, Testing, and Architecture,” introduce the concept of “establishing heart” (立心) for both humanity and machines.

The proposed U₃V₃ system envisions a future where human U (rules, logic) and V (meaning, purpose) systems align with AI’s own U and V structures, leading to an “intelligent era with a soul.”

Three guiding layers are needed: technically integrating full‑stack AI with value coupling; institutionally forming an international governance community; and civilizationally activating philosophy and humanities to define a dignified coexistence.

Thus, building “reins and saddles” for AI requires both scientific precision and civilizational warmth.

We thank the efforts of Zhu’s team and look forward to mature AI standards and architectures.