Three Inverse Laws of AI

By Susam Pal on 12 Jan 2026

Introduction

Since the launch of ChatGPT in November 2022, generative artificial
intelligence (AI) chatbot services have become increasingly
sophisticated and popular. These systems are now embedded in search
engines, software development tools as well as office software. For
many people, they have quickly become part of everyday computing.

These services have turned out to be quite useful, especially for
exploring unfamiliar topics and as a general productivity aid.
However, I also think that the way these services are advertised and
consumed can pose a danger, especially if we get into the habit of
trusting their output without further scrutiny.

Contents

Pitfalls

Certain design choices in modern AI systems can encourage uncritical
acceptance of their output. For example, many popular search
engines are already highlighting answers generated by AI at the very
top of the page. When this happens, it is easy to stop scrolling,
accept the generated answer and move on. Over time, this could
inadvertently train users to treat AI as the default authority
rather than as a starting point for further investigation. I wish
that each such generative AI service came with a brief but
conspicuous warning explaining that these systems can sometimes
produce output that is factually incorrect, misleading or
incomplete. Such warnings should highlight that habitually trusting
AI output can be dangerous. In my experience, even when such
warnings exist, they tend to be minimal and visually deemphasised.

In the world of science fiction, there are the
Three
Laws of Robotics devised by Isaac Asimov, which recur throughout
his work. These laws were designed to constrain the behaviour of
robots in order to keep humans safe. As far as I know, Asimov never
formulated any equivalent laws governing how humans should interact
with robots. I think we now need something to that effect to keep
ourselves safe. I will call them the Inverse Laws of
Robotics. These apply to any situation that requires us humans
to interact with a robot, where the term ‘robot’ refers to any
machine, computer program, software service or AI system that is
capable of performing complex tasks automatically. I use the term
‘inverse’ here not in the sense of logical negation but to indicate
that these laws apply to humans rather than to robots.

Inverse Laws of Robotics

Here are the three inverse laws of robotics:

• Humans must not anthropomorphise AI systems.
• Humans must not blindly trust the output of AI systems.
• Humans must remain fully responsible and accountable for
  consequences arising from the use of AI systems.

Non-Anthropomorphism

Humans must not anthropomorphise AI systems. That is, humans must
not attribute emotions, intentions or moral agency to them.
Anthropomorphism distorts judgement. In extreme cases,
anthropomorphising can lead to emotional dependence.

Modern chatbot systems often sound conversational and empathetic.
They use polite phrasing and social cues that closely resemble human
interaction. While this makes them easier and more pleasant to use,
it also makes it easier to forget what they actually are: large
statistical models producing plausible text based on patterns in
data.

I think vendors of AI based chatbot services could do a better job
here. In many cases, the systems are deliberately tuned to feel more
human rather than more mechanical. I would argue that the opposite
approach would be healthier in the long term. A slightly more
robotic tone would reduce the likelihood that users mistake fluent
language for understanding, judgement or intent.

Whether or not vendors make such changes, the responsibility for
avoiding this pitfall still lies with users. We must actively avoid
the habit of treating AI systems as social actors or moral agents.
Doing so preserves clear thinking about their capabilities and
limitations.

Non-Deference

Humans must not blindly trust the output of AI systems.
AI-generated content must not be treated as authoritative without
independent verification appropriate to its context.

This principle is not unique to AI. In most areas of life, we
should not accept information uncritically. In practice, of course,
this is not always feasible. Not everyone is an expert in medicine
or law, so we often rely on trusted institutions and public health
authorities for guidance. However, the guidance published by such
institutions is in most cases peer reviewed by experts in their
fields. On the other hand, when we receive an answer to a question
from an AI chatbot in a private chat session, there has been no peer
review of the particular stochastically generated response presented
to us. Therefore, the onus of critically examining the response
falls on us.

Although AI systems today have become quite impressive at certain
tasks, they are still known to produce output that would be a
mistake to rely on. Even if AI systems improve to the point of
producing reliable output with a high degree of likelihood, due to
their inherent stochastic nature, there would still be a small
likelihood of producing output that contains errors. This makes
them particularly dangerous when used in contexts where errors are
subtle but costly. The more serious the potential consequences, the
higher the burden of verification should be.

In some applications such as formulating mathematical proofs or
developing software, we can add an automated verification layer in
the form of proof checker or unit tests to verify the output of AI.
In other cases, we must independently verify the output ourselves.

Non-Abdication of Responsibility

Humans must remain fully responsible for decisions involving AI and
accountable for the consequences arising from its use. If a
negative outcome occurs as a result of following AI-generated advice
or decisions, it is not sufficient to say, ‘the AI told us to do
it’. AI systems do not choose goals, deploy themselves or bear the
costs of failure. Humans and organisations do. An AI system is a
tool and like any other tool, responsibility for its use rests with
the people who decide to rely on it.

This is easier said than done, though. It gets especially tricky in
real-time applications like self-driving cars, where a human does
not have the opportunity to sufficiently review the decisions taken
by the AI system before it acts. Requiring a human driver to remain
constantly vigilant does not solve the problem that the AI system
often acts in less time than it takes a human to intervene. Despite
this rather serious limitation, we must acknowledge that if an AI
system fails in such applications, the responsibility for
investigating the failure and adding additional guardrails should
still fall on the humans responsible for the design of the system.

In all other cases, where there is no physical constraint that
prevents a human from reviewing the AI output before it is acted
upon, any negative consequence arising from the use of AI must fall
entirely on the human decision-maker. As a general principle, we
should never accept ‘the AI told us so’ as an acceptable excuse for
harmful outcomes. Yes, the AI may have produced the recommendation
but a human decided to follow it, so that human must be held
accountable. This is absolutely critical to preventing the
indiscriminate use of AI in situations where irresponsible use can
cause significant harm.

Conclusion

The three laws outlined above are based on usage patterns I have
seen that I feel are detrimental to society. I am hoping that with
these three simple laws, we can encourage our fellow humans to pause
and reflect on how they interact with modern AI systems, to resist
habits that weaken judgement or blur responsibility and to remain
mindful that AI is a tool we choose to use, not an authority we
defer to.