# AI Incident Database Tracker (`jungle_synthesizer/ai-incident-database-model-vuln-tracker-scraper`) Actor Structured feed of AI Incident Database (incidentdatabase.ai) incident records — title, description, alleged deployer/developer/harmed parties, linked reports, and CSET/GMF/MIT taxonomy tags — scraped from the public SSR pages since the GraphQL API is origin-gated. - **URL**: https://apify.com/jungle\_synthesizer/ai-incident-database-model-vuln-tracker-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** AI, Developer tools, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## AI Incident Database Incident Scraper Scrape structured incident records from the [AI Incident Database](https://incidentdatabase.ai). Returns titles, dates, descriptions, alleged deployers/developers, harmed parties, linked source reports, and CSET/GMF/MIT taxonomy tags for every catalogued AI-harm incident — over 1,500 and growing. --- ### AI Incident Database Scraper Features - Extracts full incident records — title, date, description, and every organization named as a deployer, developer, or harmed party - Resolves entity IDs to display names, so you get "YouTube," not `youtube` - Pulls every linked source report per incident, with title, source domain, publish date, and URL - Returns CSET, GMF, and MIT taxonomy classifications — intent, severity, harm type, AI technique, risk domain — flattened into readable tags - Walks the incident id space directly, one request per incident. No browser required - Pick up mid-range with `startId` — useful for incremental delta runs as new incidents get published --- ### Who Uses AI Incident Database Data? - **AI governance and policy teams** — build an evidence base for regulation proposals instead of screenshotting individual incidents - **Insurers underwriting AI liability** — track real-world harm patterns by sector, deployer, and technology - **Researchers** — study incident frequency, severity trends, and recurring failure modes across CSET's own taxonomy - **Safety and red-team teams** — cross-reference known failure modes against systems they're evaluating, or at least the public record of what's already gone wrong - **Journalists** — source specific, citable incidents with linked reporting instead of relying on memory --- ### How AI Incident Database Scraper Works 1. Set a starting incident id (default `1`) and a `maxItems` cap. 2. The scraper walks the id space sequentially, fetching each incident's structured record directly — not the rendered HTML page, and not the site's GraphQL API (which only answers browser-origin requests). 3. Each valid incident returns its full record: description, deployer/developer/harmed-party lists resolved to names, every linked report, and its taxonomy classifications. 4. The run stops once it hits `maxItems`, or after enough consecutive ids come back invalid to signal the end of the known range. --- ### Input ```json { "startId": 1, "maxItems": 50 } ```` | Field | Type | Default | Description | |-------|------|---------|-------------| | `startId` | Integer | `1` | Incident id to begin enumeration from. Leave at `1` for a full crawl, or set it to pick up where a previous run left off. | | `maxItems` | Integer | `10` | Maximum number of incident records to return. | *** ### AI Incident Database Scraper Output Fields ```json { "incident_id": 1, "title": "Google’s YouTube Kids App Presents Inappropriate Content", "date": "2015-05-19", "description": "YouTube’s content filtering and recommendation algorithms exposed children to disturbing and inappropriate videos.", "alleged_deployer": ["YouTube"], "alleged_developer": ["YouTube"], "alleged_harmed_parties": ["Minors"], "report_count": 14, "reports": [ "YouTube Kids, Criticized for Content, Introduces New Parental Controls (nytimes.com, 2018-04-26) [https://www.nytimes.com/2018/04/25/business/media/youtube-kids-parental-controls.html]" ], "editor_notes": "", "taxonomy_tags": [ "CSETv0:Intent=Accident", "CSETv0:Severity=Moderate", "CSETv0:Harm Type=Psychological harm", "MIT:Risk Domain=1. Discrimination and Toxicity" ], "similar_incidents": [], "source_database": "aiid", "incident_url": "https://incidentdatabase.ai/cite/1/", "first_seen_at": "2026-07-13T06:34:38.570Z", "scraped_at": "2026-07-13T06:34:38.570Z" } ``` | Field | Type | Description | |-------|------|-------------| | `incident_id` | Integer | AIID sequential incident identifier | | `title` | String | Incident title | | `date` | String | Incident date (editorially assigned) | | `description` | String | One-paragraph incident summary | | `alleged_deployer` | Array\ | Organizations/entities alleged to have deployed the AI system | | `alleged_developer` | Array\ | Organizations/entities alleged to have developed the AI system | | `alleged_harmed_parties` | Array\ | Parties alleged to have been harmed or nearly harmed | | `report_count` | Integer | Number of linked source reports | | `reports` | Array\ | Linked reports, formatted as `title (source_domain, date_published) [url]` | | `editor_notes` | String | AIID editor notes, where present | | `taxonomy_tags` | Array\ | CSET/GMF/MIT classification tags, formatted as `Namespace:Field=Value` | | `similar_incidents` | Array\ | Similar incident ids, where surfaced (best-effort — the source site computes this client-side via a gated endpoint, so it's often empty) | | `source_database` | String | Always `aiid` in this version | | `incident_url` | String | Canonical incidentdatabase.ai URL | | `first_seen_at` | String | ISO-8601 timestamp this id was first observed by this actor | | `scraped_at` | String | ISO-8601 timestamp this record was scraped | *** ### FAQ #### How do I scrape the AI Incident Database? Run this actor with a `startId` and `maxItems`. It returns full incident records — no need to click through hundreds of `/cite//` pages by hand. #### What data can I get from AI Incident Database? Every catalogued incident's title, date, description, alleged deployer/developer, harmed parties, linked source reports, and CSET/GMF/MIT taxonomy classifications. That's the same data editors see on the site, structured for export. #### Does AI Incident Database Scraper need a browser or proxies? No. It fetches the site's own JSON data endpoint directly. No Playwright, no proxy configuration, no CAPTCHA handling — the source is a plain public dataset once you know where to ask. #### Why isn't the AIID GraphQL API used directly? Because it's origin-gated — the AI Incident Database's `/api/graphql` endpoint rejects any request that isn't a browser tab on their own site. This actor reads the same structured data via the public page-data endpoint the site itself serves, which isn't gated. #### Can I resume a large crawl where I left off? Set `startId` to the incident id right after the last one you received, and run again with the same `maxItems`. The scraper walks ids sequentially, so this picks up cleanly. *** ### Need More Features? Need OECD AI Incidents Monitor coverage, a different taxonomy filter, or a different output shape? [File an issue](https://console.apify.com/actors/issues) or get in touch. ### Why Use AI Incident Database Scraper? - **No browser required** — pure JSON fetches against the site's own data endpoint, so runs are fast and cheap - **Structured, not scraped-looking** — entity ids resolve to real names, reports are deduplicated against the incident's actual citation list, and taxonomy tags are flattened into readable strings instead of a nested classification blob - **Fills a real gap** — the AI Incident Database's API only answers browser-origin requests, so this is the only programmatic route to its data # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `startId` (type: `integer`): AIID incident id to begin enumeration from (sequential). Leave at 1 for a full crawl. ## `maxItems` (type: `integer`): Maximum number of incident records to scrape. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "startId": 1, "maxItems": 10 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "startId": 1, "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/ai-incident-database-model-vuln-tracker-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "startId": 1, "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/ai-incident-database-model-vuln-tracker-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "startId": 1, "maxItems": 10 }' | apify call jungle_synthesizer/ai-incident-database-model-vuln-tracker-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/ai-incident-database-model-vuln-tracker-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Incident Database Tracker", "description": "Structured feed of AI Incident Database (incidentdatabase.ai) incident records — title, description, alleged deployer/developer/harmed parties, linked reports, and CSET/GMF/MIT taxonomy tags — scraped from the public SSR pages since the GraphQL API is origin-gated.", "version": "0.1", "x-build-id": "AmEZuoNc89mOBSqxL" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~ai-incident-database-model-vuln-tracker-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-ai-incident-database-model-vuln-tracker-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~ai-incident-database-model-vuln-tracker-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-ai-incident-database-model-vuln-tracker-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~ai-incident-database-model-vuln-tracker-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-ai-incident-database-model-vuln-tracker-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "startId": { "title": "Start Incident ID", "type": "integer", "description": "AIID incident id to begin enumeration from (sequential). Leave at 1 for a full crawl.", "default": 1 }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of incident records to scrape.", "default": 10 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```