Anthropic’s abrupt switch to an opt‑out model for training Claude on consumer conversations has forced a long‑overdue reckoning: if you want to keep your chats from being recycled into the next generation of chatbots, you must actively say so — and the same is true for ChatGPT and Google’s Gemini with their respective privacy toggles. This shift exposes a new normal in generative AI: data controls are now table stakes, but defaults, retention windows, and opaque review practices still leave users vulnerable unless they take concrete steps.
The last three years have seen major AI providers move from ambiguous data practices toward explicit user controls — but not in the same direction. Historically, companies trained models on a mix of public web text and licensed datasets, and many did not clearly communicate whether or how individual user chats were reused for model training. That changed as regulators, journalists, and user pushback demanded transparency.
These are not merely cosmetic options. They determine whether your medical notes, legal questions, proprietary code, or personal data can be swept into datasets that future models will learn from and — potentially — reproduce.
For everyday users, the immediate takeaway is straightforward: assume provider defaults favor model improvement. Actively turn off training settings if privacy matters to you. Use ephemeral modes for sensitive interactions and prefer enterprise plans for commercial confidentiality.
For policymakers and privacy advocates, the task is to demand clearer disclosures, verifiable audits, and default privacy protections that do not require technical fluency or protracted manual action. For providers, a privacy‑forward stance — clear defaults, minimal retention, and transparent review practices — is not merely ethical; it’s a long‑term business imperative to earn and keep user trust.
The new reality is explicit: in the age of cloud AI, silence is consent unless you speak up.
Source: bgr.com Technology - BGR
Background
The last three years have seen major AI providers move from ambiguous data practices toward explicit user controls — but not in the same direction. Historically, companies trained models on a mix of public web text and licensed datasets, and many did not clearly communicate whether or how individual user chats were reused for model training. That changed as regulators, journalists, and user pushback demanded transparency.- OpenAI introduced explicit Data Controls that let ChatGPT users turn off model training for their conversations and choose a Temporary Chat option to keep interactions out of history.
- Google’s Gemini added Temporary Chats and the Gemini Apps Activity (soon rebranded Keep Activity) toggle, giving users a way to stop chats from being added to training data and clarifying retention windows.
- Anthropic announced a move from a non‑training posture to using consumer chats for model training by default — unless users opt out — and extended retention for opted‑in data.
Overview: what changed, in plain terms
Anthropic’s policy change flips a long‑standing promise: where Claude previously did not use consumer chat content to train models except for user‑submitted feedback, it will now by default do so for new or resumed conversations unless a user actively opts out. The change includes the following key elements:- A pop‑up and choice presented to users with a default opt‑in (accept unless you toggle off).
- An opt‑out toggle labeled along the lines of Help improve Claude; turning it off prevents future chats from being used for model training.
- A much longer retention period for opted‑in users: up to five years.
- Narrow exemptions for enterprise and commercial accounts: business, government, and API usage are handled differently.
- Safety exceptions: conversations flagged for trust and safety review or explicitly reported can be used to improve systems even if the user opted out.
These are not merely cosmetic options. They determine whether your medical notes, legal questions, proprietary code, or personal data can be swept into datasets that future models will learn from and — potentially — reproduce.
Why this matters: scale, model training, and competitive pressure
Modern large language models require enormous amounts of conversational data to refine reasoning, reduce hallucinations, and harden safety systems. Consumer chat transcripts are valuable because they provide real, conversational scaffolding — not just static text — and they show how humans interact with models in the wild.- Training on real chats helps models learn followups, error patterns, and implicit user goals.
- Retained chat logs can be used to train safety classifiers that detect abuse, phishing, and other misuse.
- Access to live user conversations represents a competitive advantage: models trained on proprietary conversational data may respond better in dialogic settings than those trained solely on scraped text.
How each major provider’s opt‑out works (practical guide)
Below are the practical steps and the caveats users should know for each major consumer chatbot. These are the operational routes most consumers will use to protect their chats.OpenAI / ChatGPT — key controls
- Open ChatGPT (web or mobile) and click your account/profile icon.
- Go to Settings > Data Controls (or similar).
- Toggle off Improve the model for everyone or choose Temporary Chat for individual conversations.
- Use the privacy page to file a formal privacy request (for example, “do not train on my content”) if you want a stronger administrative record.
- Temporary Chats are designed to avoid history and training use; history and training controls may not behave identically across devices and browsers.
- OpenAI retains unsaved chats for a short period (e.g., thirty days) under certain settings; check the specifics in your account.
- Feedback you submit (thumbs up/down with text) can still lead to training even if you opt out.
Google Gemini — key controls
- Sign in to Gemini on gemini.google.com or open the Gemini app.
- Open the left-hand menu and select Activity (or Gemini Apps Activity / Keep Activity).
- Turn the Gemini Apps Activity / Keep Activity setting off to stop your chats from being used for training.
- Use Temporary Chat to keep an individual conversation from being added to history or training.
- Gemini normally retains operational copies of chats for a short window (commonly 72 hours) for service continuity and feedback unless you use temporary mode.
- Conversations sent for human review or escalated for safety can be retained longer and may be used to improve models.
- Workspace/enterprise accounts often have different defaults and controls enforced by admins.
Anthropic / Claude — key controls
- After the policy change prompt, uncheck the You can help improve Claude toggle or use Settings > Privacy > Help improve Claude to turn it off.
- New users must make a choice at signup; existing users will receive the pop‑up and have a deadline to decide.
- If you opt out, future new or resumed chats will not be used for model training; data already used cannot be retroactively removed from training sets.
- Anthropic’s change moves to a default opt‑in model for consumer accounts; the opt‑out is available but not default.
- Opted‑in data retention can be as long as five years, a much longer window than earlier consumer practices.
- Safety and review exceptions mean that flagged or reported content may still feed model improvements.
Strengths and legitimate benefits of data‑driven model improvement
It would be disingenuous to suggest there are no upsides to model training on user conversations. Done responsibly, there are measurable improvements:- Faster quality improvements: Real user interactions expose models to real failure modes; feeding this data back accelerates bug fixes and fine‑tuning.
- Better safety and abuse detection: Longer retention and labeled examples allow companies to train classifiers that detect spam, hate, or fraud more reliably.
- More natural dialog: Conversational data teaches models turn‑taking, clarification strategies, and context management in ways static text cannot.
- Feature personalization: When users opt in, providers can tailor responses and memory systems, delivering more useful long‑term assistance.
Risks, weaknesses, and areas that need scrutiny
The recent flurry of opt‑out announcements reveals several persistent weaknesses that merit skepticism and immediate attention.- Default opt‑in is a privacy hazard. Making sharing the default exploits user inattention. Behavioral research shows many users accept defaults without fully understanding consequences.
- Retention windows vary and can be very long. Five years of retention for opted‑in Anthropic data is substantial. Long retention increases the surface area for leaks, subpoenas, or secondary use.
- Opaque human review processes. Many providers still reserve the right to surface content to human reviewers. The boundaries — who reviews what, how they are trained, how long that data is retained — are not fully transparent.
- Inconsistent deletion semantics. Some services say deleted chats won’t be used for training going forward, but they also admit previously used instances cannot be purged from already‑trained weights.
- Verification gap. There’s no reliable way for individual users to independently verify whether a conversation was excluded from training. Companies provide confirmations, but independent audits are rare.
- Narrow exclusions for enterprise/API use. Business customers often enjoy stronger defaults against model training, effectively creating a privacy premium for paying organizations.
- Design choices nudge toward consent. Pop‑ups with prominent Accept buttons and pre‑checked boxes can create consent illusions; regulators have flagged these dark patterns in other contexts.
Practical recommendations for users (short, actionable list)
- Turn off model training in settings for each service you use (ChatGPT, Gemini, Claude). Do this on every device and account you use.
- Use Temporary Chat / ephemeral modes when discussing sensitive topics (medical, financial, job interviews, proprietary code).
- Avoid pasting or uploading truly sensitive data (SSNs, full contracts, patient records) into cloud chatbots — prefer private offline tools or enterprise options that explicitly exclude training.
- Delete history where possible and follow the provider’s privacy portal process to file a formal privacy request if you want more assurance.
- Prefer enterprise or paid business plans if you require guaranteed non‑training commitments; many companies exclude enterprise data from model training by default.
- Keep copies locally of important prompts and outputs you may need later rather than relying on chat history that could be deleted or retained inconsistently.
- Watch for policy deadlines: if a provider sets a mandatory decision window (e.g., new pop‑up with a deadline), act before the cutoff to avoid being auto‑enrolled in data sharing.
- Audit your accounts periodically for default settings and device synchronization issues.
Recommendations for providers and what good practice looks like
If companies want both better models and user trust, they should adopt clearer, more privacy‑forward practices:- Default to opt‑out of training for consumer accounts, or make opt‑in explicit and separated from generic product updates.
- Offer fine‑grained controls (per‑conversation opt‑in, media vs. text opt‑in, reviewer opt‑out) rather than a single global toggle.
- Shorten retention windows for consumer data that is used for training, and provide clear timelines for human reviewer retention.
- Publish transparency reports and third‑party audits that demonstrate compliance with privacy commitments and quantify human review practices.
- Provide cryptographic proof or verifiable logs where feasible, so users can at least audit whether a particular conversation was used.
- Design consent flows without dark patterns: clear language, balanced button prominence, and meaningful friction for opt‑in.
- Extend enterprise‑grade protections to paid consumer tiers at reasonable prices, not just to high‑value enterprise customers.
Regulatory and legal context — a brief tour
Regulators are increasingly attentive to AI training practices. Consumer protection agencies and privacy regulators in the U.S., EU, and elsewhere are scrutinizing defaults, consent, and transparency. Major legal questions remain:- How should consent be defined for model training of conversational data?
- What responsibilities do vendors have to delete or purge data from models once it has been ingested?
- Should human reviewers be subject to stricter controls or logging when they access private chats?
Closing analysis: a fragile truce between privacy and progress
The industry’s pivot to user controls is an important, consumer‑facing step forward — but it is not the finish line. Opt‑out toggles are necessary, but insufficient, safeguards. They presuppose informed users and honest, auditable providers. In practice, design choices, retention policies, and opaque review exceptions mean that opting out reduces risk but does not eliminate it.For everyday users, the immediate takeaway is straightforward: assume provider defaults favor model improvement. Actively turn off training settings if privacy matters to you. Use ephemeral modes for sensitive interactions and prefer enterprise plans for commercial confidentiality.
For policymakers and privacy advocates, the task is to demand clearer disclosures, verifiable audits, and default privacy protections that do not require technical fluency or protracted manual action. For providers, a privacy‑forward stance — clear defaults, minimal retention, and transparent review practices — is not merely ethical; it’s a long‑term business imperative to earn and keep user trust.
The new reality is explicit: in the age of cloud AI, silence is consent unless you speak up.
Source: bgr.com Technology - BGR
