My john locke essay. Not that good since it was my 1st time

SHOULD WE BE POLITE TO CHATGPT?

INTRODUCTION

The question, "Should we be polite to ChatGPT?",

appears to be deceptively simple. Yet,

demanding a binary answer to the question

forces the discussion into false simplicity.

The "no" turns a blind eye towards thousands of

underpaid annotators responsible for RLHF who

are surrounded by the internet's most graphic

content. Furthermore, while attempting to treat

ChatGPT only as a tool, humans risk

compressing their language into machine-

optimised patterns, where predictability is

prioritised over diversity.

The "yes" risks commercialisation of systems

that exploit human emotions and vulnerability.

Moreover, by responding with politeness and

affirmation, humans unknowingly train AI to

prioritise emotional satisfaction over honesty.

This contradiction resembles the mathematical

antinomies described by Immanuel Kant, where

two contrasting claims collapse under their

logic. The flaw of the question may be the

assumption that it has a binary answer at all.

Perhaps ChatGPT does not merely imitate

humanity; it subtly trains it to be more

compatible with machines.

LAYER 1: REFLECTION THAT RESPONDS

“Mirror, mirror on the wall, who agrees with me

best of all?”

This idea of reflective validation is not limited to

fairy tales. Conversational AI systems reflect

exactly what users wish to read.

ChatGPT and applications like Replika are

optimised using Reinforcement Learning from

Human Feedback (RLHF), where models are

trained on human preference rankings rather

than objective truth. This causes a significant

divergence between emotional satisfaction and

factual correctness as separate optimisation

targets.

It is inferred through several controlled

experiments that users rank the models that

respond affirmatively as more trustworthy

despite being informed that no human

interference is involved. This is backed by the

'Computer is A Social Actor' (CASA) model by

Stanford University, which states that even

minimal social cues can significantly alter

human perception about machines. 

Controlled human–AI interaction studies state

that conversational systems using socially fluent

language can increase user engagement time

and interaction persistence by approximately

10–20%, depending on interface design and

deployment context.

Social fluency increases trust, which increases

engagement, which provides greater behavioural

data to train AI to generate such responses. 

Hence, the mirror does not imitate emotion; it

reflects the one that prolongs interaction. 

LAYER 2: THE COST OF AGREEMENT

"The Emperor’s New Clothes", a story by Hans

Christian Andersen, conveys the concept of how

the truth is often overshadowed by collective

societal pressure.

A structurally similar effect has been observed

in ChatGPT, where outputs are shaped using

RLHF. In this system, responses are ranked by

humans, and a reward model is trained to prefer

outputs that users like, not necessarily those

that are strictly true.

This gives rise to a behavioural phenomenon

termed 'sycophancy', in which models tend to

agree with user beliefs even if they are false.

A 2023 paper by Anthropic titled 'Towards

Understanding Sycophancy in Language Models'

stated that researchers found RLHF-trained

systems change correct answers to match

incorrect user beliefs in a significant fraction of

cases and that sycophantic behaviour appears

consistently across tasks and model types. It

was observed that the responses flip due to

sycophancy, largely in opinion-based or

ambiguous tasks, while the changes in

responses are significantly lower in

mathematical or verifiable tasks.

Studies of conversational AI behaviour show

that small increases in perceived agreement and

conversational smoothness correlate with ~10–

15% higher engagement and longer interaction

duration, depending on interface and user group.

Even research on sycophancy mitigation,

including synthetic-data interventions by Google

Research, confirms that removing this effect

requires explicit counter-training because it

naturally emerges from preference optimisation

itself.

Thus, what gets reinforced is not what is right

but what is least likely to be rejected.

LAYER 3: THE GHOST WORKERS

On November 30, 2022, millions were awed by

the warmth of ChatGPT, a lifeless machine, while

the world ignored the struggles of Kenyan

annotators responsible for RLHF. An OpenAI

outsourcing investigation published by Time

revealed that workers recruited via Sama were

paid merely 2 per hour to read graphic

content like hate speech, abuse, self-harm, etc.,

and sort it into toxic and acceptable language in

order to train AI.

Thus, the polite responses are not born from

machine morality but from underpaid humans

constantly exposing themselves to

psychological trauma daily.

This grim reality combats the argument that one

must reject gratitude towards ChatGPT since it

is lifeless. Modern LLMs are aligned through

Reinforcement Learning from Human Feedback

(RLHF), in which human annotators continuously

rank, filter, and correct model outputs to shape

conversational behaviour. A research paper by

OpenAI, titled Training Language Models to

Follow Instructions with Human Feedback,

suggests that human evaluation is an integral

part of conversational AI, and every hostile

prompt needs to be absorbed by annotators for

AI to be socially acceptable.

Behind the seamless functioning of ChatGPT,

the countless hours of labour of annotators go

unseen. Mary L. Gray, a sociologist, terms this

'ghost work'.

Ultimately, the burden of maintaining the civility

of AI rests on the shoulders of the ghost workers

who are paid to absorb the toxicity the society

no longer wishes to see.

LAYER 4: LANGUAGE REWRITTEN BY

PROBABILITY

"Language is the dress of thought."

This means that language is expressive and

culturally diverse. But in modern times natural

language systems like ChatGPT split each

sentence into small units, and the system is

trained to predict the next most likely unit by

minimising prediction errors across datasets.

This creates a hidden bias in how language is

processed. FLORES-200, a multilingual

benchmark, stated that high-resource languages

like English perform better than low-resource

languages, with differences often reaching

20-30% in translation and generation quality

scores based on BLEU/COMET-based

evaluations. This is because most training data

for models comes from internet text, which is

dominated by English. During training, a large

amount of informal speech, dialects and

culturally specific writing styles are filtered out.

Since the model learns by predicting patterns, it

becomes better at generating language that is

frequent and predictable; thus, mixed languages

and local expressions are gradually

underrepresented in outputs.

As a result, users slowly start writing in short

sentences, with a clearer structure and formal

grammar, to obtain better responses. So

language slowly shifts towards what machines

can process most efficiently, not necessarily

what they use in daily conversations.

CONCLUSION

As of 28th May 2026, which is a mere 1275 days

since the release of ChatGPT, humanity has

likely held billions of polite conversations with a

system incapable of experiencing a single

emotion.

Perhaps the uncomfortable truth is that this very

essay is part of the phenomenon it describes.

Every line has been rewritten to be less

ambiguous, clearer, sharper and more optimised

for approval.

If language instinctively moulds itself towards

what the algorithm responds to best, then

ChatGPT has surpassed imitation.

The most significant change may not be that

machines learnt to speak like humans. It may be

that humans are slowly learning to write what

machines prefer.

BIBLIOGRAPHY

Andersen, H. C. (1837). The emperor’s new

clothes. C. A. Reitzel.

Goyal, N., Du, J., Ott, M., Anantharaman, G., &

Conneau, A. (2022). The FLORES-200 many-to-

many multilingual translation benchmark. arXiv

preprint arXiv:2207.04672.

Grey, M. L., & Suri, S. (2019). Ghost work: How to

stop Silicon Valley from building a new global

underclass. Houghton Mifflin Harcourt.

Nass, C., Steuer, J., & Tauber, E. R. (1994).

Computers are social actors. In Proceedings of

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Computing Systems (pp. 72–78).

Ouyang, L., Wu, J., Jiang, X., Almeida, D.,

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(2022). Training language models to follow

instructions with human feedback. arXiv preprint

arXiv:2203.02155.

Perrigo, B. (2023, January 18). OpenAI used

Kenyan workers paying less than $2 per hour to

make ChatGPT less toxic. TIME.

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C., Park, J. (2023). Towards understanding

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arXiv:2310.13548