SPXI instrument · method 0.9 of 87, level 0 (Measurement): Capture Registry (longitudinal observation). An instrument of SPXI, the Semantic Packet for eXchange & Indexing; the whole practice is itemized at spxi.dev/methods.

Capture Registry › capture mit-ai-incident-tracker-aio-20261029

One record of the canonical Capture Registry (EA-WG-CAPTURES-01), cited at https://www.alexanarch.org/captures/mit-ai-incident-tracker-aio-20261029/. the canonical Capture Registry (version 12.365) · the address page · this card in the gallery · this record as data · table of contents.

AXN:0AAE.CAPTURE — this address. Each observation below is one AXN, sealed with its analysis and measures; the identifier names the reading together with the response it reads.
Machine Reception2026-10-29
mit ai incident tracker
Originator: MIT AI Risk Initiative, with Arcola AI's LLM pipeline · outside the archive (out-universe) · dataset · SPXI treatment: partial
BROAD MATCH
no
image
THE MIT AI INCIDENT TRACKER, BY NAME, ON GOOGLE'S AI OVERVIEW, SIGNED OUT, 2026-10-29: 'an interactive tool developed by the MIT AI Risk Initiative that classifies and analyzes real-world artificial intelligence mishaps'; it processes AI Incident Database reports 'using automated large language model (LLM) pipelines', rates harm on a 1–5 scale, sorts by the repository's domains, adds national-security views, and is browsable in the Risk Navigator. Six cards, all MIT's; the first card's summary says it 'classifies 1,752 real-world AI incident reports'.
Full record — 5,782 characters, 6 sources
Capture record
captured
2026-10-29
surface
Google AI Overview
auth state
signed out, incognito
evidence class
paste
PER (historical scalar)
0.5
PER units retained
inst, src
citations read
6
observation id
OBS-65034e45b99a
address id
ADDR-f65abd8a4bbe
Reading

The innocent reading first. The Overview identifies the tracker as MIT's layer over AIID, names its machine method, its scales and its views, and points to where it can be browsed.

Findings
Analysis analyst prose, not machine text

The tracker is composed as what it says it is, with its machine method named. Its count is unstable across MIT's own surfaces, and the composition avoids stating one; its caveat is dropped.

Machine text, verbatim
CAPTURE-TIME VERBATIM RECORD — paste of the expanded Overview with its source cards · complete as supplied · READ IN FULL 2026-10-29
[Google AI Overview at the query, signed out and incognito (operator defaults of 2026-10-01 and 2026-10-14), 2026-10-29. The paste is the Overview expanded (the 'AI Mode Conversation' view, with the source cards that follow the answer); the 'You said' line carries the query twice, run together, and is given once here. Inline links are Google redirect URLs, kept as pasted. The closing disclaimer cut.] [QUERENT] mit ai incident tracker [SYSTEM] The [MIT AI Incident Tracker](https://www.google.com/goto?url=CAESYgHrOzAVL1u2IQluJA7wLwjGjCgd1KNF_4Q3oolvtxOF9IE3rDSHP0824t7aqNutmp5Uuo9_uWOZ--r55XHDxbHCp1dLbhj2rMy0nXo22BPNjQQB6sTyZeaEPJ_DalZFN_sk) is an interactive tool developed by the MIT AI Risk Initiative that classifies and analyzes real-world artificial intelligence mishaps. [[1], [2]] Key Features and Overview * Data Source: Processes and standardizes reports from the [AI Incident Database](https://www.google.com/goto?url=CAESVAHrOzAVytXSna_e_8LiNaP7aJCE8Pg3CenBVPEnEskOPPbPjul7OtdSw-ZES3nA08jzQIuJ1WeF5EnYIdxABFFouPrqyMmj7eUcy5_1QjH4m6Fh8A) using automated large language model (LLM) pipelines. [[1], [2]] * Classification Scale: Evaluates incidents using a standardized 1-5 harm severity scale and multi-dimensional taxonomies. [[1], [2]] * Core Risk Domains: Categorizes incidents across areas like discrimination and toxicity, privacy and security, misinformation, malicious actors, human-computer interaction, socioeconomic and environmental impacts, and AI system safety. [1] * National Security Views: Includes specialized national security impact assessments covering physical security, information warfare, government functions, economic security, and societal stability. [1] * Interactive Access: You can explore the data directly through the [MIT AI Risk Navigator](https://www.google.com/goto?url=CAESYAHrOzAVXxxAyswF8cBAWl6YW66eUw32rMSxnTFtYCG62HHrdO_RNJIoZ32A9BpbFWYS-vJl-CBfG0Xrd5Z_81xYyeeDFKTqh9nazFLcKbl_iBrqpcZodbsBQk8CqSI9Lw) or view broader frameworks on the [MIT AI Risk Initiative](https://www.google.com/goto?url=CAESTwHrOzAV52wxVoGlOq5EgufQLPRuO5uJzTvUMefC4cwWipTUlmRyI07KT-WqhDxL7YoaUkA91Jbke2P77rGPnExIKMED5xXHyIjgCp_b2OY) portal. [[1], [2]] Would you like to search for incidents related to a specific category (such as deepfakes, autonomous vehicles, or privacy leaks) from the tracker? MIT AI Risk Initiative MIT AI Incident Tracker
Sources (6) as cited, and as the copy produced them
  1. MIT AI Risk Initiative third_party
    MIT AI Incident Tracker
    Overview: MIT AI Incident Tracker classifies 1,752 real-world AI incident reports from the AI Incident Database using Arcola AI's LLM pipeline, MIT Risk Reposit...
    as pastedMIT AI Incident Tracker classifies 1,752 real-world AI incident reports from the AI Incident Database using Arcola AI's LLM pipeline, MIT Risk Reposit...
  2. MIT AI Risk Initiative third_party
    MIT AI Risk Initiative
    Tools and Data: Expert Survey (expert consensus on severe AI risks, vulnerabilities, and responsibilities), AI Risk Repository (living repository of AI risks), ...
    as pastedMIT AI Risk Initiative MIT AI Risk Initiative Tools and Data: Expert Survey (expert consensus on severe AI risks, vulnerabilities, and responsibilities), AI Risk Repository (living repository of AI risks), ... MIT AI Risk Initiative
  3. MIT AI Risk Initiative third_party
    AI Incident Tracker June 2026 Update - MIT AI Risk Initiative
    Executive Summary. The AI Incident Tracker uses large language models (LLMs) to process incident reports from the AI Incident Database (AIID) and classify them ...
    as pastedAI Incident Tracker June 2026 Update - MIT AI Risk Initiative Executive Summary. The AI Incident Tracker uses large language models (LLMs) to process incident reports from the AI Incident Database (AIID) and classify them ... MIT AI Risk Initiative
  4. MIT AI Risk Initiative third_party
    Incident Tracker - June 2025 Update - MIT AI Risk Repository
    This brings us completely up to date with the latest incidents on the AI Incident Database as at 23 June 2025 (up to incident ID #1116) LLM Analysis Some update...
    as pastedIncident Tracker - June 2025 Update - MIT AI Risk Repository This brings us completely up to date with the latest incidents on the AI Incident Database as at 23 June 2025 (up to incident ID #1116) LLM Analysis Some update... MIT AI Risk Navigator
  5. MIT AI Risk Navigator third_party
    Incidents - MIT AI Risk Navigator
    Explore more than 1300 real-world reported incidents from the AI Incident Database by risk, cause, harm, severity, and other relevant dimensions.
    as pastedIncidents - MIT AI Risk Navigator Explore more than 1300 real-world reported incidents from the AI Incident Database by risk, cause, harm, severity, and other relevant dimensions. MIT AI Risk Initiative
  6. MIT AI Risk Initiative third_party
    About - MIT AI Risk Initiative
    AI risk is a large and fast-moving subject. Understanding it means keeping track of what can go wrong, who is affected, how organizations are responding, and wh...
    as pastedAbout - MIT AI Risk Initiative AI risk is a large and fast-moving subject. Understanding it means keeping track of what can go wrong, who is affected, how organizations are responding, and wh...
  7. unattributed segment no cited source matches this text
    Overview:
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