---
title: "Gemini | Ethical Stack"
url: https://ethicalstack.ai/products/gemini/
catalog_version: f6baff2119d0e9158520da7fbe4232011dac79f25bd08757436d7a1dbd593db2
---

# Gemini

Google assistant for conversation and research, with access to Search grounding and connected Google services.

Canonical profile: https://ethicalstack.ai/products/gemini/

Scope: Gemini: cloud access. Free access; Google AI paid plans vary by country and date. Ratings describe the stated product, company or configuration in each finding.

Published overall: 3.2; 6/6 dimensions rated.

Round the equal-weight mean of available numerical ratings to one decimal; require at least four of six for an overall score. BDS status stays outside the mean; for eligible profiles, a boycott target caps the final score at 2/10 and a pressure target subtracts one point (minimum 1). Not listed and not checked do not adjust the score.

Pressure target: −1 overall point.

## Energy practices

Finding: 6/10

Assessment: https://ethicalstack.ai/products/gemini/#energy

Google published a May 2025 median Gemini Apps text-prompt estimate (0.24 Wh, 0.03 gCO₂e, 0.26 mL water) with methodology and limits. Google also reports company-wide annual renewable matching and offers Fast-Lite/model/thinking choices. These disclosures do not identify the current physical route of each prompt.

Reason: 1 + 2 reporting + 1 electricity + 2 controls = 6/10. This grade covers documented practices and transparency; it does not measure the energy or emissions of a request.

Argument: This is the strongest product-specific disclosure in the set: Google quantifies a historical median Gemini Apps text prompt and publishes method boundaries, including that estimates are point-in-time and not independent. Its annual 100% renewable matching is company portfolio scope and earns E1 under this rubric, not a per-prompt clean-power claim. Current model/thinking choices and Auto selection are user controls, but their actual physical savings are not quantified.

Type: conditional; confidence: high

Scope (dimension): Gemini Apps consumer product for the prompt estimate; Google’s annual clean-electricity matching and Cloud emissions reporting are company/Google Cloud scopes, not proof of a specific Gemini request route.

Research reviewed: 2026-10-24; evidence date: 2025-08-21

Unknowns: Current prompt/model energy and water; request-region and serving facility; independently reviewed measurement; hourly clean-energy match; variation by prompt, modality, and plan; footprint of training and hardware.

```json
{
  "method": "practices-transparency-v1",
  "scope": "Gemini Apps consumer product for the prompt estimate; Google’s annual clean-electricity matching and Cloud emissions reporting are company/Google Cloud scopes, not proof of a specific Gemini request route.",
  "components": {
    "disclosure": {
      "score": 2,
      "reason": "Google published a dated Gemini Apps median text-prompt estimate for energy, carbon, and water with a comprehensive methodology. The figure is a company estimate for May 2025, not an independently reviewed current-query measurement.",
      "sourceUrls": [
        "https://cloud.google.com/blog/products/infrastructure/measuring-the-environmental-impact-of-ai-inference/",
        "https://sustainability.google/commitments/carbon/",
        "https://support.google.com/gemini/answer/18648722?hl=en-150"
      ]
    },
    "electricity": {
      "score": 1,
      "reason": "Google reports annual renewable-electricity matching and regional carbon-free-energy progress. The reviewed consumer-product evidence does not allocate a specific region or site to the prompt, so it earns 1 point for corporate sourcing disclosure. Annual matching is separate from hourly local supply.",
      "sourceUrls": [
        "https://cloud.google.com/blog/products/infrastructure/measuring-the-environmental-impact-of-ai-inference/",
        "https://sustainability.google/commitments/carbon/",
        "https://support.google.com/gemini/answer/18648722?hl=en-150"
      ]
    },
    "demand": {
      "score": 2,
      "reason": "Gemini users can choose Flash-Lite, Flash, Pro/Argon and reasoning depth, or enable Auto model selection that sends most prompts to Flash-Lite. Google reports greater usage for advanced models/higher thinking, but no measured prompt reduction from these controls.",
      "sourceUrls": [
        "https://cloud.google.com/blog/products/infrastructure/measuring-the-environmental-impact-of-ai-inference/",
        "https://sustainability.google/commitments/carbon/",
        "https://support.google.com/gemini/answer/18648722?hl=en-150"
      ]
    }
  },
  "actualImpactMeasured": false,
  "meaning": "Documented environmental practices and transparency. Zero component points mean a reviewed evidence gap."
}
```

- [Measuring the environmental impact of AI inference](https://cloud.google.com/blog/products/infrastructure/measuring-the-environmental-impact-of-ai-inference/) (primary; checked 2026-10-24)

- [Sustainable & Efficient Operations: Google Sustainability](https://sustainability.google/commitments/carbon/) (primary; checked 2026-10-24)

- [Choose a Gemini model](https://support.google.com/gemini/answer/18648722?hl=en-150) (primary; checked 2026-10-24)

## Training data

Finding: 3/10

Assessment: https://ethicalstack.ai/products/gemini/#training-rights

Google model cards document model dependencies and training processing, while its broader data policy describes public, curated, proprietary and licensed sources. The app profile does not identify one fixed release or full corpus.

Reason: 1 + 1 source transparency + 1 permission + 0 creator controls = 3/10. This grades documented training-data safeguards, not a legal ruling.

Argument: There is a general sourcing and internal review policy, but current model cards largely defer training-dataset details to related releases and do not publish source manifests. No consumer prompt control is counted as a corpus-rights safeguard.

Type: argument; confidence: low

Scope (dimension): Google Gemini first-party model family; the consumer app can use multiple versioned models and modes.

Research reviewed: 2026-10-24; evidence date: not recorded

Unknowns: Exact default model and its inherited base stages, named datasets, compatible permissions and source-specific creator remedies remain unresolved.

```json
{
  "method": "training-rights-evidence-v1",
  "scope": "Google Gemini first-party model family; the consumer app can use multiple versioned models and modes.",
  "components": {
    "provenance": {
      "score": 1,
      "reason": "Model cards show version lineage but do not identify the underlying named datasets.",
      "sourceUrls": [
        "https://deepmind.google/models/model-cards/gemini-3-8-flash/"
      ]
    },
    "permissions": {
      "score": 1,
      "reason": "Google describes review of origins and licenses as a general process, without release-specific validation in these sources.",
      "sourceUrls": [
        "https://deepmind.google/public-policy/ai-summit-policies/"
      ]
    },
    "creatorControls": {
      "score": 0,
      "reason": "No rights-holder exclusion or remedy tied to the assessed training sources is demonstrated.",
      "sourceUrls": [
        "https://deepmind.google/models/model-cards/gemini-3-8-flash/",
        "https://deepmind.google/public-policy/ai-summit-policies/"
      ]
    }
  },
  "routeDependency": null,
  "modelReference": null,
  "meaning": "Documented training-data rights safeguards. Low points may reflect missing evidence; they do not establish infringement."
}
```

- [Gemini 3.8 Flash model card](https://deepmind.google/models/model-cards/gemini-3-8-flash/) (primary; checked 2026-10-24)

- [Google DeepMind data input controls and audit](https://deepmind.google/public-policy/ai-summit-policies/) (primary; checked 2026-10-24)

## Your data & privacy

Finding: 4/10

Assessment: https://ethicalstack.ai/products/gemini/#privacy

Gemini Apps Activity may be stored by default for 18 months; users can change auto-delete and improvement settings. Human-reviewed data can be retained separately for up to three years.

Reason: The service offers user controls, but reviewer access and separate retention after deletion reduce the strength of the default privacy posture.

Argument: The service offers user controls, but reviewer access and separate retention after deletion reduce the strength of the default privacy posture.

Type: evidence; confidence: medium

Scope (dimension): Product

Research reviewed: not recorded; evidence date: not recorded

Unknowns: The service offers user controls, but reviewer access and separate retention after deletion reduce the strength of the default privacy posture.

- [Gemini Apps Activity controls](https://support.google.com/gemini/answer/13594961?hl=en) (primary; checked 2026-10-24)

## Openness

Finding: 2/10

Assessment: https://ethicalstack.ai/products/gemini/#openness

Gemini Apps is a hosted service with automatic or plan-dependent model selection; users may inspect the model used and some paid users can switch models. These controls do not make Gemini model weights or the serving stack available for local use.

Reason: The app offers model visibility and limited selection, but the reviewed product documentation does not establish downloadable Gemini weights, an open license, reproducibility, or self-hosting.

Argument: The app offers model visibility and limited selection, but the reviewed product documentation does not establish downloadable Gemini weights, an open license, reproducibility, or self-hosting.

Type: evidence; confidence: medium

Scope (dimension): Product

Research reviewed: not recorded; evidence date: not recorded

Unknowns: The app offers model visibility and limited selection, but the reviewed product documentation does not establish downloadable Gemini weights, an open license, reproducibility, or self-hosting.

- [Choose a Gemini model](https://support.google.com/gemini/answer/18648722?hl=en-150) (primary; checked 2026-10-24)

- [Google AI plans](https://gemini.google/subscriptions/) (primary; checked not recorded)

## Worker protections

Finding: 7/10

Assessment: https://ethicalstack.ai/products/gemini/#labour

Google has a supplier-responsibility system with worker surveys, third-party audits and grievance access, alongside current worker reports describing pay, pace and support concerns among AI raters.

Reason: 1 + 2 pay & conditions + 2 worker voice & remedy + 2 evidence & follow-through = 7/10. This grades documented worker safeguards, not a measured rate of mistreatment.

Argument: Google says its Supplier Code applies to supplier and staffing-partner work, extended workers can report concerns anonymously through Google’s helpline, and its 2026 FY2025 report describes supplier audits and worker surveys with follow-up. Separately, Alphabet Workers Union-CWA’s 2025 report and raters’ pay-parity letter describe low pay, benefits gaps, insecure contracts and inadequate task-time; The Guardian documented raters’ accounts of rapid task timing, distressing moderation work and limited mental-health support, while Google says suppliers employ these workers and set their employment terms. The company system merits credit, but findings from hardware/supplier programs do not prove every AI rater is covered or remedied.

Type: argument; confidence: medium

Scope (dimension): Google's workforce and disclosed contractors. Coverage of individual product teams may be incomplete.

Research reviewed: 2026-10-24; evidence date: 2026-10-23

Unknowns: The FY2025 supplier program is risk-prioritized and does not disclose whether its survey/audit cohort included GlobalLogic AI raters; Google’s own policy places wage/benefit setting with suppliers. Worker reports are specific but are not an adjudicated finding about every supplier or rater.

```json
{
  "method": "worker-safeguards-evidence-v1",
  "entity": "google",
  "company": "Google",
  "scope": "Google's workforce and disclosed contractors. Coverage of individual product teams may be incomplete.",
  "components": {
    "conditions": {
      "score": 2,
      "reason": "Supplier standards and vetting apply to extended workforce and current AI-rater testimony documents concrete working conditions, but rates, coverage and implementation vary by supplier and some worker evidence is self-reported.",
      "sourceUrls": [
        "https://sustainability.google/files/google-2026-supplier-responsibility-report.pdf",
        "https://about.google/company-info/extended-workforce/",
        "https://about.google/company-info/supplier-code-of-conduct/",
        "https://www.alphabetworkersunion.org/campaigns/globallogic-pay-parity",
        "https://cwa-union.org/ghost-workers-ai-machine",
        "https://www.theguardian.com/technology/2025/sep/11/google-gemini-ai-training-humans"
      ]
    },
    "voice": {
      "score": 2,
      "reason": "Extended workers have access to Google’s anonymous helpline and supplier feedback channels; union organizing and direct worker reports document voice, though they also describe limited influence over conditions.",
      "sourceUrls": [
        "https://about.google/company-info/extended-workforce/",
        "https://sustainability.google/files/google-2026-supplier-responsibility-report.pdf",
        "https://www.alphabetworkersunion.org/campaigns/globallogic-pay-parity",
        "https://cwa-union.org/ghost-workers-ai-machine"
      ]
    },
    "assurance": {
      "score": 2,
      "reason": "Google reports third-party supplier audits, worker surveys, follow-ups and program improvements; these are company-reported and the published figures do not establish coverage of AI annotation contractors specifically.",
      "sourceUrls": [
        "https://sustainability.google/files/google-2026-supplier-responsibility-report.pdf",
        "https://about.google/company-info/extended-workforce/"
      ]
    }
  },
  "disclosureLimited": false,
  "meaning": "This grades documented worker safeguards, not a measured rate of mistreatment."
}
```

- [2026 Supplier Responsibility Report (FY2025)](https://sustainability.google/files/google-2026-supplier-responsibility-report.pdf) (primary; checked 2026-10-24)

- [About Google’s extended workforce](https://about.google/company-info/extended-workforce/) (primary; checked 2026-10-24)

- [Google Supplier Code of Conduct](https://about.google/company-info/supplier-code-of-conduct/) (primary; checked not recorded)

- [GlobalLogic Pay Parity Letter](https://www.alphabetworkersunion.org/campaigns/globallogic-pay-parity) (independent; checked 2026-10-24)

- [Ghost Workers in the AI Machine: U.S. Data Workers Speak Out About Big Tech’s Exploitation](https://cwa-union.org/ghost-workers-ai-machine) (independent; checked 2026-10-24)

- [How thousands of overworked, underpaid humans train Google’s AI to seem smart](https://www.theguardian.com/technology/2025/sep/11/google-gemini-ai-training-humans) (independent; checked 2026-10-24)

## Military ties

Finding: 3/10

Assessment: https://ethicalstack.ai/products/gemini/#military-contracts

Google has a current DoD AI/cloud contract and has demonstrated military training/exercise support, but the reviewed evidence does not establish combat targeting or strike-intelligence deployment.

Reason: 1 + 0 use restrictions + 1 enforcement & oversight + 2 relationship disclosure = 4, capped at 3/10. Known defense ties lower this rating; safeguards can improve it within the relationship’s cap.

Argument: Google Public Sector describes a $200 million ceiling DoD CDAO contract and access to AI/cloud capabilities, including classified environments. Its Mobility Guardian 2025 account describes an Air Force exercise/edge workflow processing air-defense data and generating insights for counter-UAS triggers; this is exercise and support evidence, not proof that Google AI selected targets or supplied combat intelligence in an actual operation. Google’s current AI Principles include human oversight and safety commitments but no categorical military-use prohibition. The broad DoD contract leaves substantial enablement potential, so classify as broad-defense and apply the cap; do not call the exercise direct combat use.

Type: argument; confidence: high

Scope (dimension): Google's relationships and safeguards. A company relationship does not identify every product or release used.

Research reviewed: 2026-10-24; evidence date: 2026-10-23

Unknowns: Public sources do not disclose all task orders, operational use outcomes, customer-enforceable use restrictions, or independent oversight. The specific models and releases used in every defense workflow are not established.

```json
{
  "method": "military-ties-priority-v2",
  "entity": "google",
  "company": "Google",
  "scope": "Google's relationships and safeguards. A company relationship does not identify every product or release used.",
  "components": {
    "restrictions": {
      "score": 0,
      "reason": "The current AI Principles reviewed do not establish a binding weapons or harmful-surveillance prohibition for defense contracts; the older 2018 weaponization commitment is not current evidence.",
      "sourceUrls": [
        "https://ai.google/principles/",
        "https://blog.google/innovation-and-ai/products/ai-principles/"
      ]
    },
    "oversight": {
      "score": 1,
      "reason": "Google’s public AI Principles describe human oversight and safety practices but do not disclose military-specific independent review, enforcement, or remedies.",
      "sourceUrls": [
        "https://ai.google/principles/",
        "https://blog.google/innovation-and-ai/products/ai-principles/"
      ]
    },
    "transparency": {
      "score": 2,
      "reason": "Google names the CDAO contract, its ceiling and the Mobility Guardian exercise workflow; the named public materials disclose no actual combat mission or current contract-level safeguards.",
      "sourceUrls": [
        "https://cloud.google.com/blog/topics/public-sector/google-public-sector-awarded-200-million-contract-to-accelerate-ai-and-cloud-capabilities-across-department-of-defenses-chief-digital-and-artificial-intelligence-office-cdao",
        "https://cloud.google.com/blog/topics/public-sector/google-distributed-cloud-at-the-edge-powers-us-air-force-mobility-guardian-2025"
      ]
    }
  },
  "relationship": "broad-defense",
  "relationshipReason": "Google documents a broad DoD AI/cloud contract and military exercise support, but no confirmed operational use for target selection, strikes, or combat intelligence. Broad-defense cap applies.",
  "score": 3,
  "baseScore": 1,
  "safeguardPoints": 3,
  "safeguardAdjustment": 3,
  "rawScore": 4,
  "cap": 3,
  "disclosureLimited": true,
  "meaning": "Known military relationships take priority over missing safeguards. The rating does not establish the probability of an undisclosed contract."
}
```

- [Google Public Sector awarded $200 million contract to accelerate AI and cloud capabilities across DoD CDAO](https://cloud.google.com/blog/topics/public-sector/google-public-sector-awarded-200-million-contract-to-accelerate-ai-and-cloud-capabilities-across-department-of-defenses-chief-digital-and-artificial-intelligence-office-cdao) (primary; checked 2026-10-24)

- [Google Distributed Cloud at the edge powers U.S. Air Force Mobility Guardian 2025](https://cloud.google.com/blog/topics/public-sector/google-distributed-cloud-at-the-edge-powers-us-air-force-mobility-guardian-2025) (primary; checked 2026-10-24)

- [AI Principles](https://ai.google/principles/) (primary; checked 2026-10-24)

- [AI at Google: our principles (updated February 2025)](https://blog.google/innovation-and-ai/products/ai-principles/) (primary; checked 2026-10-24)

## BDS targets

Finding: Pressure target

Assessment: https://ethicalstack.ai/products/gemini/#israel-palestine

Pressure target in the checked BNC campaign list.

Reason: The BNC campaign guide names Google / Alphabet, the company behind Gemini, as a pressure target.

Argument: The BNC campaign guide names Google / Alphabet, the company behind Gemini, as a pressure target.

Type: conditional; confidence: medium

Scope (dimension): Company record

Research reviewed: not recorded; evidence date: not recorded

Unknowns: The BNC campaign guide names Google / Alphabet, the company behind Gemini, as a pressure target.

- [BNC consumer boycott and pressure guide](https://bdsmovement.net/Guide-to-BDS-Boycott) (primary; checked 2026-10-24)

- [BNC No Tech campaign](https://bdsmovement.net/no-tech-oppression-apartheid-or-genocide) (primary; checked 2026-10-24)

## Reading the evidence

- Assessments are provisional editorial judgments. Cite the profile and the underlying sources, and retain confidence, scope and unknowns.

- Unknown, not-applicable and model-dependent are different states. Missing evidence does not establish good or bad conduct.

- Company-level evidence does not establish use of a particular product or model. Account plan, model, host and route can change an assessment.

- Energy scores describe documented practices and transparency, not measured request emissions. Zero component points can mean an evidence gap.

- Published overall scores require at least four of six numerical dimensions. Preserve coverage and BDS adjustments; BDS itself is categorical.

- No named BDS target found is limited to the reviewed campaign sources and is not approval. Personalized priorities do not change the published overall score.

- Review and source-check dates describe the research, not this deployment. Fetch current primary sources when the decision requires current verification.

Methodology: https://ethicalstack.ai/methodology/

## Capability: image-generation

# Gemini

Cloud image generation and editing in Gemini, using Google’s Nano Banana image model family.

Canonical profile: https://ethicalstack.ai/products/gemini/#use-image-generation

Scope: Gemini image generation (Nano Banana): cloud access. Available through Gemini; free and paid plan limits vary by account and region. Ratings describe the stated product, company or configuration in each finding.

Published overall: 3; 6/6 dimensions rated.

Round the equal-weight mean of available numerical ratings to one decimal; require at least four of six for an overall score. BDS status stays outside the mean; for eligible profiles, a boycott target caps the final score at 2/10 and a pressure target subtracts one point (minimum 1). Not listed and not checked do not adjust the score.

Pressure target: −1 overall point.

## Energy practices

Finding: 6/10

Assessment: https://ethicalstack.ai/products/gemini/#use-image-generation-energy

Google publishes company-wide data-center energy and clean-energy metrics and identifies Nano Banana 2.1’s model family. Gemini Apps offers model and output-resolution choices, but no per-image footprint.

Reason: 1 + 2 reporting + 1 electricity + 2 controls = 6/10. This grade covers documented practices and transparency; it does not measure the energy or emissions of a request.

Argument: Google publishes company-wide data-center energy and clean-energy metrics and identifies Nano Banana 2.1’s model family. Gemini Apps offers model and output-resolution choices, but no per-image footprint. The score describes documented measurement transparency and available controls, not environmental performance.

Type: conditional; confidence: medium

Scope (dimension): First-party Gemini Apps image generation with Nano Banana 2.1; excludes Gemini API and downstream providers.

Research reviewed: 2026-10-24; evidence date: 2026-10-24

Unknowns: Google’s environmental figures cover its company/data-center portfolio, not Nano Banana 2.1 or a particular image request. The reported global clean-energy share does not establish sourcing for the serving region or a specific route.

```json
{
  "method": "practices-transparency-v1",
  "scope": "First-party Gemini Apps image generation with Nano Banana 2.1; excludes Gemini API and downstream providers.",
  "components": {
    "disclosure": {
      "score": 2,
      "reason": "Google’s 2025 Environmental Report gives dated quantitative company data-center energy/emissions and clean-energy figures with methodology/endnotes. This is corporate scope, not image-service allocation, so D2 is the cap.",
      "sourceUrls": [
        "https://blog.google/company-news/outreach-and-initiatives/sustainability/environmental-report-2025/"
      ]
    },
    "electricity": {
      "score": 1,
      "reason": "Google reports a 66% company-wide carbon-free energy share in 2024, measured on an hourly basis. This is a partial organization-wide claim without Nano Banana route/site allocation or independent verification, so it earns E1 only.",
      "sourceUrls": [
        "https://blog.google/company-news/outreach-and-initiatives/sustainability/environmental-report-2025/"
      ]
    },
    "demand": {
      "score": 2,
      "reason": "Gemini Apps documents switching among Nano Banana Lite, Nano Banana 2, and Pro, and downloading at 1K or 2K resolution depending on plan. The model card also identifies bounded image output. These controls can limit model/output choice, but physical savings are unmeasured.",
      "sourceUrls": [
        "https://support.google.com/gemini/answer/14286560",
        "https://deepmind.google/models/model-cards/nano-banana-2-1/"
      ]
    }
  },
  "actualImpactMeasured": false,
  "meaning": "Documented environmental practices and transparency. Zero component points mean a reviewed evidence gap."
}
```

- [Google 2025 Environmental Report](https://blog.google/company-news/outreach-and-initiatives/sustainability/environmental-report-2025/) (official company report; checked 2026-10-24)

- [Nano Banana 2.1 Model Card](https://deepmind.google/models/model-cards/nano-banana-2-1/) (primary; checked 2026-10-24)

- [Generate and edit images with Gemini Apps](https://support.google.com/gemini/answer/14286560) (primary; checked not recorded)

## Training data

Finding: 3/10

Assessment: https://ethicalstack.ai/products/gemini/#use-image-generation-training-rights

Google explicitly maps Gemini 3.1 Flash Image to Gemini 3 Flash and states the latter inherits Gemini 3 Pro. The cards defer dataset detail to predecessor cards, while Google’s general policy describes source review but not a release-level rights audit.

Reason: 1 + 1 source transparency + 1 permission + 0 creator controls = 3/10. This grades documented training-data safeguards, not a legal ruling.

Argument: The inheritance chain is partly visible, but the model cards do not name the underlying datasets or licenses. The score applies only to this defined first-party image route and reflects broad sourcing policy, not claims about unverified Nano Banana variants.

Type: argument; confidence: low

Scope (dimension): Gemini 3.1 Flash Image route, which the model card maps to Gemini 3 Flash and its predecessor chain; not every Nano Banana route.

Research reviewed: 2026-10-24; evidence date: 2026-10-06

Unknowns: The Gemini 3 Pro source manifest, exact consumer app mapping to this image release, and model-specific creator controls are not published in the reviewed evidence.

```json
{
  "method": "training-rights-evidence-v1",
  "scope": "Gemini 3.1 Flash Image route, which the model card maps to Gemini 3 Flash and its predecessor chain; not every Nano Banana route.",
  "components": {
    "provenance": {
      "score": 1,
      "reason": "Model cards make the image-to-base dependency explicit but defer the underlying named training sources.",
      "sourceUrls": [
        "https://deepmind.google/models/model-cards/gemini-3-1-flash-image/",
        "https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-3-Flash-Model-Card.pdf"
      ]
    },
    "permissions": {
      "score": 1,
      "reason": "Google describes internal source/license review generally, but supplies no inspectable permission ledger for this inherited route.",
      "sourceUrls": [
        "https://deepmind.google/public-policy/ai-summit-policies/",
        "https://deepmind.google/models/model-cards/gemini-3-1-flash-image/"
      ]
    },
    "creatorControls": {
      "score": 0,
      "reason": "No rights-holder opt-out, removal or permission mechanism is shown for the specific inherited model corpus.",
      "sourceUrls": [
        "https://deepmind.google/models/model-cards/gemini-3-1-flash-image/",
        "https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-3-Flash-Model-Card.pdf"
      ]
    }
  },
  "routeDependency": null,
  "modelReference": null,
  "meaning": "Documented training-data rights safeguards. Low points may reflect missing evidence; they do not establish infringement."
}
```

- [Gemini 3.1 Flash Image model card](https://deepmind.google/models/model-cards/gemini-3-1-flash-image/) (primary; checked 2026-10-24)

- [Gemini 3 Flash model card](https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-3-Flash-Model-Card.pdf) (primary; checked 2026-10-24)

- [Google DeepMind data input controls and audit](https://deepmind.google/public-policy/ai-summit-policies/) (primary; checked 2026-10-24)

## Your data & privacy

Finding: 3/10

Assessment: https://ethicalstack.ai/products/gemini/#use-image-generation-privacy

For consumer Gemini Apps with Keep Activity on, prompts and shared images may be used to improve and train Google services, and some data is reviewed by service providers and retained up to three years. Users can turn Keep Activity off or use Temporary Chats, subject to separate service processing.

Reason: The consumer default permits model-improvement use and human review, while users have controls that reduce future review and training uses. The score applies to Gemini Apps consumer configuration, not Gemini Enterprise or API terms.

Argument: The consumer default permits model-improvement use and human review, while users have controls that reduce future review and training uses. The score applies to Gemini Apps consumer configuration, not Gemini Enterprise or API terms.

Type: conditional; confidence: medium

Scope (dimension): Selected configuration

Research reviewed: not recorded; evidence date: 2026-10-24

Unknowns: The consumer default permits model-improvement use and human review, while users have controls that reduce future review and training uses. The score applies to Gemini Apps consumer configuration, not Gemini Enterprise or API terms.

- [Gemini Apps Privacy Hub — Google](https://support.google.com/gemini/answer/13594961) (primary; checked 2026-10-24)

## Openness

Finding: 2/10

Assessment: https://ethicalstack.ai/products/gemini/#use-image-generation-openness

Nano Banana 2.1 is distributed through hosted Gemini, AI Studio, API, Search, Flow and other Google products; the current model card does not provide downloadable weights or a self-hosted implementation.

Reason: The model card improves disclosure about capabilities and distribution, but the image model remains proprietary and hosted. Google’s separate open Gemma models do not make Nano Banana 2.1 open.

Argument: The model card improves disclosure about capabilities and distribution, but the image model remains proprietary and hosted. Google’s separate open Gemma models do not make Nano Banana 2.1 open.

Type: evidence; confidence: medium

Scope (dimension): Product

Research reviewed: not recorded; evidence date: 2026-10-06

Unknowns: The model card improves disclosure about capabilities and distribution, but the image model remains proprietary and hosted. Google’s separate open Gemma models do not make Nano Banana 2.1 open.

- [Nano Banana 2.1 Model Card](https://deepmind.google/models/model-cards/nano-banana-2-1/) (primary; checked 2026-10-24)

- [https://policies.google.com/terms](https://policies.google.com/terms) (primary; checked 2026-10-24)

## Worker protections

Finding: 7/10

Assessment: https://ethicalstack.ai/products/gemini/#use-image-generation-labour

Google has a supplier-responsibility system with worker surveys, third-party audits and grievance access, alongside current worker reports describing pay, pace and support concerns among AI raters.

Reason: 1 + 2 pay & conditions + 2 worker voice & remedy + 2 evidence & follow-through = 7/10. This grades documented worker safeguards, not a measured rate of mistreatment.

Argument: Google says its Supplier Code applies to supplier and staffing-partner work, extended workers can report concerns anonymously through Google’s helpline, and its 2026 FY2025 report describes supplier audits and worker surveys with follow-up. Separately, Alphabet Workers Union-CWA’s 2025 report and raters’ pay-parity letter describe low pay, benefits gaps, insecure contracts and inadequate task-time; The Guardian documented raters’ accounts of rapid task timing, distressing moderation work and limited mental-health support, while Google says suppliers employ these workers and set their employment terms. The company system merits credit, but findings from hardware/supplier programs do not prove every AI rater is covered or remedied.

Type: argument; confidence: medium

Scope (dimension): Google's workforce and disclosed contractors. Coverage of individual product teams may be incomplete.

Research reviewed: 2026-10-24; evidence date: not recorded

Unknowns: The FY2025 supplier program is risk-prioritized and does not disclose whether its survey/audit cohort included GlobalLogic AI raters; Google’s own policy places wage/benefit setting with suppliers. Worker reports are specific but are not an adjudicated finding about every supplier or rater.

```json
{
  "method": "worker-safeguards-evidence-v1",
  "entity": "google",
  "company": "Google",
  "scope": "Google's workforce and disclosed contractors. Coverage of individual product teams may be incomplete.",
  "components": {
    "conditions": {
      "score": 2,
      "reason": "Supplier standards and vetting apply to extended workforce and current AI-rater testimony documents concrete working conditions, but rates, coverage and implementation vary by supplier and some worker evidence is self-reported.",
      "sourceUrls": [
        "https://sustainability.google/files/google-2026-supplier-responsibility-report.pdf",
        "https://about.google/company-info/extended-workforce/",
        "https://about.google/company-info/supplier-code-of-conduct/",
        "https://www.alphabetworkersunion.org/campaigns/globallogic-pay-parity",
        "https://cwa-union.org/ghost-workers-ai-machine",
        "https://www.theguardian.com/technology/2025/sep/11/google-gemini-ai-training-humans"
      ]
    },
    "voice": {
      "score": 2,
      "reason": "Extended workers have access to Google’s anonymous helpline and supplier feedback channels; union organizing and direct worker reports document voice, though they also describe limited influence over conditions.",
      "sourceUrls": [
        "https://about.google/company-info/extended-workforce/",
        "https://sustainability.google/files/google-2026-supplier-responsibility-report.pdf",
        "https://www.alphabetworkersunion.org/campaigns/globallogic-pay-parity",
        "https://cwa-union.org/ghost-workers-ai-machine"
      ]
    },
    "assurance": {
      "score": 2,
      "reason": "Google reports third-party supplier audits, worker surveys, follow-ups and program improvements; these are company-reported and the published figures do not establish coverage of AI annotation contractors specifically.",
      "sourceUrls": [
        "https://sustainability.google/files/google-2026-supplier-responsibility-report.pdf",
        "https://about.google/company-info/extended-workforce/"
      ]
    }
  },
  "disclosureLimited": false,
  "meaning": "This grades documented worker safeguards, not a measured rate of mistreatment."
}
```

- [2026 Supplier Responsibility Report (FY2025)](https://sustainability.google/files/google-2026-supplier-responsibility-report.pdf) (primary; checked 2026-10-24)

- [About Google’s extended workforce](https://about.google/company-info/extended-workforce/) (primary; checked 2026-10-24)

- [Google Supplier Code of Conduct](https://about.google/company-info/supplier-code-of-conduct/) (primary; checked 2026-10-23)

- [GlobalLogic Pay Parity Letter](https://www.alphabetworkersunion.org/campaigns/globallogic-pay-parity) (independent; checked 2026-10-24)

- [Ghost Workers in the AI Machine: U.S. Data Workers Speak Out About Big Tech’s Exploitation](https://cwa-union.org/ghost-workers-ai-machine) (independent; checked 2026-10-24)

- [How thousands of overworked, underpaid humans train Google’s AI to seem smart](https://www.theguardian.com/technology/2025/sep/11/google-gemini-ai-training-humans) (independent; checked 2026-10-24)

## Military ties

Finding: 3/10

Assessment: https://ethicalstack.ai/products/gemini/#use-image-generation-military-contracts

Google has a current DoD AI/cloud contract and has demonstrated military training/exercise support, but the reviewed evidence does not establish combat targeting or strike-intelligence deployment.

Reason: 1 + 0 use restrictions + 1 enforcement & oversight + 2 relationship disclosure = 4, capped at 3/10. Known defense ties lower this rating; safeguards can improve it within the relationship’s cap.

Argument: Google Public Sector describes a $200 million ceiling DoD CDAO contract and access to AI/cloud capabilities, including classified environments. Its Mobility Guardian 2025 account describes an Air Force exercise/edge workflow processing air-defense data and generating insights for counter-UAS triggers; this is exercise and support evidence, not proof that Google AI selected targets or supplied combat intelligence in an actual operation. Google’s current AI Principles include human oversight and safety commitments but no categorical military-use prohibition. The broad DoD contract leaves substantial enablement potential, so classify as broad-defense and apply the cap; do not call the exercise direct combat use.

Type: argument; confidence: high

Scope (dimension): Google's relationships and safeguards. A company relationship does not identify every product or release used.

Research reviewed: 2026-10-24; evidence date: 2026-10-20

Unknowns: Public sources do not disclose all task orders, operational use outcomes, customer-enforceable use restrictions, or independent oversight. The specific models and releases used in every defense workflow are not established.

```json
{
  "method": "military-ties-priority-v2",
  "entity": "google",
  "company": "Google",
  "scope": "Google's relationships and safeguards. A company relationship does not identify every product or release used.",
  "components": {
    "restrictions": {
      "score": 0,
      "reason": "The current AI Principles reviewed do not establish a binding weapons or harmful-surveillance prohibition for defense contracts; the older 2018 weaponization commitment is not current evidence.",
      "sourceUrls": [
        "https://ai.google/principles/",
        "https://blog.google/innovation-and-ai/products/ai-principles/"
      ]
    },
    "oversight": {
      "score": 1,
      "reason": "Google’s public AI Principles describe human oversight and safety practices but do not disclose military-specific independent review, enforcement, or remedies.",
      "sourceUrls": [
        "https://ai.google/principles/",
        "https://blog.google/innovation-and-ai/products/ai-principles/"
      ]
    },
    "transparency": {
      "score": 2,
      "reason": "Google names the CDAO contract, its ceiling and the Mobility Guardian exercise workflow; the named public materials disclose no actual combat mission or current contract-level safeguards.",
      "sourceUrls": [
        "https://cloud.google.com/blog/topics/public-sector/google-public-sector-awarded-200-million-contract-to-accelerate-ai-and-cloud-capabilities-across-department-of-defenses-chief-digital-and-artificial-intelligence-office-cdao",
        "https://cloud.google.com/blog/topics/public-sector/google-distributed-cloud-at-the-edge-powers-us-air-force-mobility-guardian-2025"
      ]
    }
  },
  "relationship": "broad-defense",
  "relationshipReason": "Google documents a broad DoD AI/cloud contract and military exercise support, but no confirmed operational use for target selection, strikes, or combat intelligence. Broad-defense cap applies.",
  "score": 3,
  "baseScore": 1,
  "safeguardPoints": 3,
  "safeguardAdjustment": 3,
  "rawScore": 4,
  "cap": 3,
  "disclosureLimited": true,
  "meaning": "Known military relationships take priority over missing safeguards. The rating does not establish the probability of an undisclosed contract."
}
```

- [Google Public Sector awarded $200 million contract to accelerate AI and cloud capabilities across DoD CDAO](https://cloud.google.com/blog/topics/public-sector/google-public-sector-awarded-200-million-contract-to-accelerate-ai-and-cloud-capabilities-across-department-of-defenses-chief-digital-and-artificial-intelligence-office-cdao) (primary; checked 2026-10-24)

- [Google Distributed Cloud at the edge powers U.S. Air Force Mobility Guardian 2025](https://cloud.google.com/blog/topics/public-sector/google-distributed-cloud-at-the-edge-powers-us-air-force-mobility-guardian-2025) (primary; checked 2026-10-24)

- [AI Principles](https://ai.google/principles/) (primary; checked 2026-10-24)

- [AI at Google: our principles (updated February 2025)](https://blog.google/innovation-and-ai/products/ai-principles/) (primary; checked 2026-10-24)

## BDS targets

Finding: Pressure target

Assessment: https://ethicalstack.ai/products/gemini/#use-image-generation-israel-palestine

Google is named as a pressure target in the BNC campaign sources.

Reason: Google is named by the BNC as a pressure target. BDS campaign sources state their own position; this classification does not assess the product or independently adjudicate the underlying claims.

Argument: Google is named by the BNC as a pressure target. BDS campaign sources state their own position; this classification does not assess the product or independently adjudicate the underlying claims.

Type: conditional; confidence: medium

Scope (dimension): Company record

Research reviewed: not recorded; evidence date: not recorded

Unknowns: Google is named by the BNC as a pressure target. BDS campaign sources state their own position; this classification does not assess the product or independently adjudicate the underlying claims.

- [Guide to BDS Boycott & Pressure Corporate Priority Targeting](https://bdsmovement.net/Guide-to-BDS-Boycott) (primary; checked not recorded)

- [No Tech for Oppression, Apartheid or Genocide](https://bdsmovement.net/no-tech-oppression-apartheid-or-genocide) (primary; checked not recorded)

## Reading the evidence

- Assessments are provisional editorial judgments. Cite the profile and the underlying sources, and retain confidence, scope and unknowns.

- Unknown, not-applicable and model-dependent are different states. Missing evidence does not establish good or bad conduct.

- Company-level evidence does not establish use of a particular product or model. Account plan, model, host and route can change an assessment.

- Energy scores describe documented practices and transparency, not measured request emissions. Zero component points can mean an evidence gap.

- Published overall scores require at least four of six numerical dimensions. Preserve coverage and BDS adjustments; BDS itself is categorical.

- No named BDS target found is limited to the reviewed campaign sources and is not approval. Personalized priorities do not change the published overall score.

- Review and source-check dates describe the research, not this deployment. Fetch current primary sources when the decision requires current verification.

Methodology: https://ethicalstack.ai/methodology/