The Ethics of AI Writing Assistants: Is Grammarly Changing the Way We Write?

By the end of this article you’ll be able to weigh the ethical trade-offs of using AI writing assistants, recognize the ways these tools change what we write, and apply practical steps to keep your voice, autonomy, and integrity intact.

Why this matters - fast

AI writing assistants sit between your brain and your text. They tidy sentences, flag tone, and sometimes rewrite whole passages for you. Helpful. Time-saving. Empowering. But they also nudge decisions you might otherwise make on your own. They flatten some kinds of creativity. They introduce privacy and fairness questions. And they change who gets credit for a piece of writing.

This is not a debate about whether the technology works. It does. The question is: how does it change us - our styles, habits, and responsibilities - and how should we respond?

How AI writing assistants like Grammarly actually work (short primer)

They run machine-learned models that analyze text for grammar, clarity, tone, and style.
Suggestions are ranked and surfaced based on patterns the system learned from large corpora and from product heuristics (e.g., conservative vs. confident language).
Some services collect data to improve models and features; others run analyses locally. Check the provider’s policies for details.

For company policies, see Grammarly’s privacy statement and data-handling documentation: https://www.grammarly.com/privacy

The ethical fault lines

Below are the main ethics concerns that come up repeatedly when people talk about AI writing assistants.

1) Originality and authorship - who owns the voice?

AI assistants can rewrite your sentence in a more formal register or invent phrasing you might not have used. That raises two hard questions:

When suggestions heavily change content or argument, is the final text still your intellectual product?
Should you disclose the use of AI assistance when publishing, submitting schoolwork, or producing client deliverables?

The ethical line depends on context. A grammar fix is different from a paragraph the model invents. Transparency norms should follow that difference.

2) Style homogenization - do we start to sound the same?

The more writers accept the same ranked suggestions, the more writing risks converging toward the platform’s preferred tone and register.

This is not merely aesthetic. Style carries identity, culture, and rhetorical choices. When those are flattened by default suggestions, certain voices - regional, non-native, historically marginalized - can be suppressed.

3) Cognitive offloading and skill erosion

We offload tasks to tools all the time. But there’s a cost.

Relying on AI for revisions can reduce opportunities to learn grammar, rhetorical moves, and the habit of self-editing. Over years, a writer can lose confidence in making stylistic choices independently. This phenomenon - cognitive offloading - is well described in cognitive science discussions: https://en.wikipedia.org/wiki/Cognitive_offloading

4) Bias and fairness

Models are trained on historical language data. That data can encode biases about gender, race, formality, and persuasion strategies.

When suggestions systematically favor one dialect or social group, they reproduce societal bias, not neutral “better writing.” The risk matters for hiring communications, academic feedback, and AI-mediated publishing.

5) Privacy and surveillance

Many writing assistants collect data to refine models and to offer personalized feedback. That means drafts, private emails, and sensitive wording could be stored, processed, or used to train future models. Users should read provider policies and seek options for local processing where necessary.

For international and institutional guidance on responsible AI design and use, see UNESCO’s work on AI ethics: https://en.unesco.org/artificial-intelligence/ethics

6) Power, business incentives, and the attention economy

Commercial incentives shape product design. Suggestions that increase engagement (e.g., prompting the user to upgrade for stylistic rewrites) can bias product behavior. Feature decisions may favor retention and monetization over user autonomy.

Real-world scenarios (concrete and practical)

A non-native English speaker uses a writing assistant and accepts suggestions that erase a syntactic quirk tied to identity - the writer’s original voice becomes less visible.
An undergraduate submits an essay heavily revised by an AI assistant and claims full authorship. The professor suspects misuse. Is this plagiarism? It depends on institutional rules about AI assistance and disclosure.
A hiring manager uses assistant-produced job descriptions that all sound identical across roles and organizations. Candidate diversity of interpretation is reduced.
A journalist lets an assistant suggest framing language. Subtle bias in model training nudges headlines toward sensational phrasing over nuance.

Each scenario reveals a different ethical boundary: identity, fairness, academic integrity, and public trust.

What good practice looks like (for four stakeholder groups)

For individual writers

Use assistants as draft helpers and readability checkers - not as sole authors.
Keep your profile and style settings explicit. If you prefer a particular voice, configure the tool to respect it.
Retain final editorial control. Always review suggestions with the rhetorical goal in mind.
When a suggestion changes content, cite or disclose if required by context (academic work, client rules, or editorial standards).

For educators and institutions

Set clear policies - what counts as acceptable AI assistance in assignments, and how must students disclose it?
Teach critical editing skills alongside tool use. Explain what suggestions are doing and why they might be misleading.
Use assessment designs that value process and reflection, not just final text, to reduce gaming.

For product designers and companies

Default to transparency - show why a suggestion is made and give provenance when available.
Provide a “voice-preservation” mode that prioritizes preserving idiosyncratic phrasing and dialectical choices.
Offer local processing or enterprise controls for sensitive workflows and privacy-conscious users.
Audit suggestions for bias across demographics and iteratively fix skewed behaviors.

For policymakers and regulators

Require disclosure standards for substantial AI authorship in public-facing documents.
Promote interoperability and portability of user-level settings (e.g., voice profiles, opt-outs for data collection).
Fund independent audits of commercial writing models for privacy, bias, and safety.

A short checklist you can use today

Did the tool materially change meaning or argument? If yes, disclose.
Is the suggestion altering voice or dialect? If yes, review against your identity goals.
Does your organization require disclosure? Follow the policy.
Have you reviewed the provider’s privacy settings and data retention policy? Adjust them if needed: https://www.grammarly.com/privacy
Are you using the tool to learn? Use it as feedback, not a replacement for practice.

Trade-offs worth accepting (and those to resist)

Accept:

Efficiency gains for editing and clarity.
Accessibility benefits for writers with dyslexia or language barriers.

Resist:

Default suggestions that erase cultural-linguistic identity.
Using assistants as a cover for authorship without disclosure in contexts that require original work.

Final design principle for tools and users

Tools will always nudge. Good tools nudge transparently, preserve user voice, and make it easy to opt out. Good users keep control, declare assistance where it matters, and treat suggestions as companions - not supervisors.

Grammarly and its peers are changing how we write, but they do not have to decide the future of style or authorship by themselves. Those decisions are ours to make through policy, pedagogy, product design, and personal habits. In the end, the ethics of these assistants are not only a question of what the models can do - they’re a question of what we will allow them to do to our words and to each other.