Transparency
EVIDENCE & SOURCES
Know what is verified—and what isn't.
Data Provenance Tiers
Every impact figure on DoctorKnow.ai carries one of four provenance badges so you can see at a glance how strong the underlying evidence is.
Verified Model
Measured, model-specific data from independent primary sources — peer-reviewed research, regulator records, or audited measurements that can be traced directly to the model in question.
Corporate Disclosed
Figures reported by the company itself — ESG reports, privacy policies, or technical disclosures. Self-reported, so we label the exact reporting scope (group, cloud division, facility) and never attribute it to a specific model beyond what the disclosure supports.
Estimated Data
Modeled or scenario-based figures — commissioned studies, efficiency ratios, or infrastructure calculations. Estimates describe systems, not models, and are never converted into per-model consumption or impact.
Data Gap
No credible evidence exists at the required scope. Rather than filling the space with a guess, we say so plainly: the score is unavailable and the absence itself is reported.
Evidence Tier by Model
Every model in the Top 10 carries four impact indicators, and each indicator is graded separately. The badges below are exactly the ones shown on each model's page, so you can compare how well documented each impact area is before you compare the scores themselves.
- Job Displacement
- Estimated Data
- Privacy & Surveillance
- Corporate Disclosed
- Water Usage
- Corporate Disclosed
- Energy & Carbon
- Estimated Data
- Job Displacement
- Estimated Data
- Privacy & Surveillance
- Verified Model
- Water Usage
- Estimated Data
- Energy & Carbon
- Estimated Data
- Job Displacement
- Estimated Data
- Privacy & Surveillance
- Corporate Disclosed
- Water Usage
- Estimated Data
- Energy & Carbon
- Estimated Data
- Job Displacement
- Verified Model
- Privacy & Surveillance
- Verified Model
- Water Usage
- Corporate Disclosed
- Energy & Carbon
- Corporate Disclosed
- Job Displacement
- Estimated Data
- Privacy & Surveillance
- Verified Model
- Water Usage
- Estimated Data
- Energy & Carbon
- Verified Model
- Job Displacement
- Estimated Data
- Privacy & Surveillance
- Verified Model
- Water Usage
- Estimated Data
- Energy & Carbon
- Estimated Data
- Job Displacement
- Estimated Data
- Privacy & Surveillance
- Verified Model
- Water Usage
- Estimated Data
- Energy & Carbon
- Estimated Data
- Job Displacement
- Estimated Data
- Privacy & Surveillance
- Verified Model
- Water Usage
- Corporate Disclosed
- Energy & Carbon
- Corporate Disclosed
- Job Displacement
- Estimated Data
- Privacy & Surveillance
- Verified Model
- Water Usage
- Estimated Data
- Energy & Carbon
- Estimated Data
- Job Displacement
- Estimated Data
- Privacy & Surveillance
- Verified Model
- Water Usage
- Corporate Disclosed
- Energy & Carbon
- Corporate Disclosed
Privacy indicators are the best documented across the field, because regulator actions, disclosed vulnerabilities, and independent testing produce model-specific records. Job displacement is the weakest, since no study measures losses caused by a single product. Water and energy sit in between: some companies disclose totals at the group or facility level, and we report them at that scope rather than dividing them down to a model.
Why AI Impact Data May Be Unavailable
AI companies are not required to publish model-level measurements of the human and environmental impacts of their systems. There is no standardized disclosure for how much water a data centre uses to serve a specific model, how many kilowatt-hours a single query consumes, or how many jobs a given product displaces. Most environmental figures that do exist are reported at the level of an entire company, a cloud division, or a whole facility — not per model.
Converting company-wide numbers into per-model figures requires assumptions we refuse to make. Dividing a data centre's water bill across its hosted models, or attributing industry-wide job losses to one product, produces numbers that look precise but are invented. When the evidence does not support a figure, we show a data gap instead of a number.
The result is that some impact cards carry less data than you might expect. That is intentional. A blank with a documented reason is more honest than a confident estimate, and as disclosure standards mature we will upgrade figures — and their badges — as better evidence becomes available.
Political Neutrality & Editorial Independence
DoctorKnow.ai is an independent, nonpartisan information platform. We do not promote a political agenda, endorse political candidates or parties, advocate for or against legislation, organize political activity, lobby public officials, or attempt to direct public opinion toward a predetermined political conclusion.
Our purpose is to document, analyze, and present verifiable information concerning artificial intelligence, its developers, its societal and environmental impacts, and the laws, regulations, policies, and institutions that govern its use.
When political, governmental, or regulatory matters are relevant to AI, DoctorKnow.ai may report and compare those policies, laws, proposals, institutions, and public statements. Their inclusion does not constitute endorsement or opposition.
We seek to distinguish verified facts, company disclosures, government information, independent research, estimates, disputed claims, and information that is not publicly verifiable.
DoctorKnow.ai does not ask users to adopt a particular political position. We provide information and source material so individuals — regardless of nationality, political affiliation, ideology, or jurisdiction — can evaluate the evidence and reach their own conclusions.
DoctorKnow.ai informs, documents, verifies, and links to primary sources. It does not organize, mobilize, lobby, endorse, or direct political action.
Official U.S. Government AI Risk Resources
The frameworks below inform how DoctorKnow.ai structures its impact diagnostics and evidence standards.
- NIST AI Risk Management Framework (AI RMF 1.0)
The U.S. National Institute of Standards and Technology's voluntary framework for managing risks to individuals, organizations, and society from AI — organized around governance, mapping, measurement, and management.
- NIST AI 600-1 — Generative AI Profile
NIST's companion resource extending the AI RMF to generative AI, covering risks unique to generative systems including confabulation, information integrity, and environmental impacts.
- NIST AI Resource Center
NIST's official hub for AI framework publications, profiles, and implementation resources.