Editor's Guide

Definitions

The AI Incident Database contains records of AI “incidents” and “issues”.

  • AI incident: an alleged harm or near harm event to people, property, or the environment where an AI system is implicated.

  • AI issue: an alleged harm or near harm by an AI system that has yet to occur or be detected.

  • AI incident variant: an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident.

  • Artificial Intelligence (AI): for our purposes, AI means the capability of machines to perform functions typically thought of as requiring human intelligence, such as reasoning, recognizing patterns or understanding natural language. AI includes, but is not limited to, machine learning – a set of techniques by which a computer system learns how to perform a task through recognizing patterns in data and inferring decision rules.

  • AI system: technologies and processes in which AI plays a meaningful role. These systems may also include components that do not involve artificial intelligence, such as mechanical components.

    • Examples: a self-driving car; facial-recognition software; Google Translate; a credit-scoring algorithm.

    Algorithms that are not traditionally considered AI may be considered an AI system when a human transfers decision making authority to the system.

    • Example: a hospital system selects vaccine candidates based on a series of hand tailored rules in a black box algorithm.
  • Implicated: a system is implicated in an incident if it played an important role in the chain of events that led to harm. The AI system does not need to be the only factor, or the major factor, in causing the harm, but it should at least be a “but-for” cause – that is, if the AI system hadn’t acted in the way it did, the specific harm would not have occurred. This includes cases where the AI system had the potential to prevent the harm, but did not.

    We make no distinction between accidental and deliberate harm (i.e., malicious use of AI). For purposes of the AIID, what matters is that harm was caused, not whether it was intended.

  • Nearly harmed: played an important role in a chain of events that easily could have caused harm, but some external factor kept the harm from occurring. This external factor should be independent of the AI system and should not have been put in place specifically to prevent the harm in question.

    • Example: an industrial robot begins spinning out of control, but a nearby worker manages to cut power to the robot before either the robot or nearby people are harmed.

    • Counterexample: an industrial robot begins spinning out of control, but its built-in safety sensor detects the abnormality and immediately shuts the robot down.

    Again, the AI system does not need to be the only factor, or even the major factor, in the chain of events that could have led to harm. But it should at least be a “but-for” cause - that is, if the AI system hadn’t acted in the way it did, there would have been no significant chance that the harm would occur.

  • Real-world harm: includes, but is not limited to:

    • Harm to physical health/safety
    • Psychological harm
    • Financial harm
    • Harm to physical property
    • Harm to intangible property (for example, IP theft, damage to a company’s reputation)
    • Harm to social or political systems (for example, election interference, loss of trust in authorities)
    • Harm to civil liberties (for example, unjustified imprisonment or other punishment, censorship)
    • Harms do not have to be severe to meet this definition; an incident resulting in minor, easily remedied expense or inconvenience still counts as harm for our purposes.

    In some cases, especially involving harms that are psychological or otherwise intangible, reasonable people might disagree about whether harm has actually occurred (or nearly occurred). Contributors should use their best judgment, erring on the side of finding harm when there is a plausible argument that harm occurred.

Mapping Reports to Incidents, Variants, and Issues

Apply the following algorithm when deciding whether this is an incident, variant, issue, or not relevant to the AIID.

  1. Does the report primarily detail a current variant?
    • Y: Add it to the current variant
    • N: Continue
  2. Does the report primarily detail a current incident?
    • Y: Add it to the current incident
    • N: Continue
  3. Does the report present the same causative factors, produce similar harms, and involves the same intelligent systems as a known AI incident?
    • Y: Add it as a new variant
    • N: Continue
  4. Does the report meet the definition of an incident?
    • Y: Add it as a new incident
    • N: Continue
  5. Does the report meet the definition of an issue?
    • Y: Add it as an issue to the database
    • N: Reject the report

Examples and Discussion

Case Analysis of “Incidents”

Several common situations do not meet the above criteria and should not be recorded as incidents in the AI Incident Database. These include:

TypeExample
Studies documenting theoretical or conceptual flaws in AI technology
Deep Neural Networks Are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Thought experiments and hypothetical examples
“...artificial general intelligence, even one designed competently and without malice, could ultimately destroy humanity”
The development or deployment of AI products that seem likely to cause harm, but there are no reports of specific situations in which harm was caused
Deep neural networks are more accurate than humans at detecting sexual orientation from facial images
Discussions of broad types of AI incidents or harmful AI behaviors
How a handful of tech companies control billions of minds every day
Misleading marketing of AI products
The mystery of Zach the miracle AI, continued: it all just gets Terribler

Case Analysis of “Issues”

The cases identified here are not comprehensive; there are more cases that potentially fit the definition of “issues”.

  1. Case 1: Concerns by critics or community about an AI system, but without a specific event of harm
    1. Why didn’t it meet criteria for incidents? It has not caused or been involved in an event of harm
    2. Example: Student-Developed Facial Recognition App Raised Ethical Concerns*
  2. Case 2: Harm associated with the AI technology as opposed to a specific AI system
    1. Why didn’t it meet criteria for incidents? No specific event is detailed, but rather the fallibility of the system type in general is discussed
    2. Example: Collaborative Filtering Prone to Popularity Bias, Resulting in Overrepresentation of Popular Items in the Recommendation Outputs*
  3. Case 3: Aggregated report of harm, but individual events are not identified
    1. Why didn’t it meet criteria for incidents? No identified events of harm, only report on the collection of the incidents
    2. Example: AI Tools Failed to Sufficiently Predict COVID Patients, Some Potentially Harmful*
  4. Case 4: Cybersecurity vulnerability reports
    1. Why didn’t it meet criteria for incidents? It has not caused or been involved in an event of harm
    2. Example: Tesla Autopilot’s Lane Recognition Allegedly Vulnerable to Adversarial Attacks*

*incidents pending migration to the “issue” collection due to not meeting definition for “AI incidents”

Report Ingestion Standards

Report Title

Incident and issue reports generally, but not always, have titles. When there is flexibility in the title, you should assign it with the following precedence:

  1. Article title (Can be sentence or title case)
  2. Report types
    1. Journalistic articles, journal article, media (e.g., YouTube video): title or name as is
    2. Social media post: use template “[social media platform]: [username or author]” (e.g., Tweet: @jdoe123, LinkedIn post: John Doe)

Author

The precedence for authors of reports is:

  1. The names of the states authors (Title case)
  2. The full name of organization, if the names of the individuals are not known

Submitter

In some cases, a person may communicate they would like to associate their affiliation with their name.

  1. People can optionally provide affiliations in their submitter ID, for example:
    • Sonali Pednekar (CSET)
    • Patrick Hall (BNH.ai)

Submitters can generally have latitude in their attribution, providing they are consistent and it does not detract from the functioning of the database.

Incident date

Resolve the date of each incident as follows:

  1. Ordered by availability

    1. Date the incident occurred, if self-contained event (e.g., an accident)
    2. Date harm was first committed, if harm was prolonged through a time period
    3. If first incident of harm is unclear or  not known,
      1. Date lawsuit was filed, if a lawsuit exists, or
      2. Date first report of harm existed (might require some historical searches)
    4. Date report was published, only as a last resort
  2. Indicate in editor notes which date was used or based on, if not (a) or (b)

Image URL

Images are associated with every incident in the database (for user experience purposes). They should be selected as follows:

  1. Ordered by availability:

    1. Image from the article
    2. Image of the publisher of the report
    3. Image of the deployer of the technology
    4. Image of the developer of the AI system
    5. Image of the party/parties harmed
  2. If image source is not the article, use image from Wikipedia

  3. Many URLs for images have parameters indicating edits that should be made to the image on the server side. It is best to remove these parameters, for example

    • https://cnn.com/images/sandwich.jpg?resize=w200;h500 should be https://cnn.com/images/sandwich.jpg

Incident ID

When submitting the form to create a new incident, leave the field blank, otherwise assign the incident the report should be associated with.

Text

Format the text of reports according to the following,

  1. One empty line between paragraphs
  2. Strip advertisement text, caption text
  3. Format with Markdown (e.g. to provide links within the text)
  4. If “Fetch info” is used, make sure that all of the text is fetched, otherwise, copy and paste the rest of the text

Editorial Standards

The following standards apply to fields that are written by AIID editors and not directly provided by the incident reports.

Incident title

Objectives

  • Should be short and concise
  • Should ideally be distinguishable from titles of other reports
  • Should communicate AI system of interest and the nature of harm
  • Should be easily understood without any specialized knowledge of AI
  • Should ideally require little update and correction over time, regardless of follow-up events

Rule set

  1. Use title case
  2. Use past tense (e.g. “they found” not “they have found” or “they find”)
  3. Use acronyms and abbreviations when applicable (e.g., US DHS, Tesla FSD)
    • Ideally, acronym and abbreviations are spelled out in description
  4. Use operational keywords for factual reporting (e.g, allegedly, reportedly)
  5. Include
    1. At least one of the following: name of AI, AI deployer, AI developer (e.g., DALL-E, Tesla Autopilot, Stanford’s vaccine distribution algorithm)
    2. Nature of harm (e.g., injured a pedestrian) or implied nature of harm (e.g., exposed users to offensive content)
    3. One of the following, if needed for distinguishability: broad location as state, large city (e.g., San Francisco), country (e.g., Korea), but avoid small city, district; broad time as year or time period (e.g., for decades, for six months), but avoid relative time (e.g., the past six months, two years ago)

Description

Each incident has an editor written or editor approved description including the following properties and elements,

  1. Short, concise
  2. Use neutral terms
  3. Factual and complete description of the incident
  4. Must haves
    1. Incident
    2. Location
    3. Harm

Developer, Deployer, and Harmed/Nearly Harmed Parties (Entities)

All incidents have manually populated tags for,

  1. Developer: the organizations or individuals responsible for producing either the parts or the whole intelligent system implicated in the incident.

  2. Deployer: the organizations or individuals responsible for the intelligent system when it is deployed in the real world.

  3. Harmed/Nearly Harmed Parties: This field may have two different entity types entered:

    1. Impacted class (e.g., teachers, black people, women): This should be the largest identifiable group of people for which the harmed person is substitutable without changing the character of the harmed parties or the incident description.

    2. Individuals (e.g., “John Basilone”): Further, in some cases it may be possible to identify specific people that have been harmed. They can be specifically tagged, but these are secondary to the impacted class to which they belong.

Operational keywords

  1. If incident elements are not universally agreed upon or undisputed, then add wiggle words, for example,

    • “Tesla Injured Pedestrian” should read as “Tesla Allegedly Injured Pedestrian” or “Tesla Reportedly Injured Pedestrian”
  2. In cases where lawsuits are on-going,

    1. Report: “X did Y, violating Z” – use “allege” (e.g., X was alleged to have done Y, potentially violating Z)
    2. Lawsuit filed – use “allege”
    3. Lawsuit settled – use “allege,” because admission of guilt cannot be inferred
    4. Report: “court found X violated Z” – “allege” no longer used, due to admission of guilt being found (e.g., X violated Z)
    5. Report: “X was fined” — “allege” no longer used, due to a fine being issued (e.g., X did Y and violated Z)

Credit

Various drafts and versions of this document have been authored by Sean McGregor, Khoa Lam, and Zachary Arnold

Working with Automated Systems

Bias

Editors working with automated systems as set up for the editors should all the more be vigilant of various biases as they permeate the work we do. The following is a list of documented biases as experienced by editors of the AIID:

  1. Automation bias
    1. What is it?
      • Bias introduced by the use of automated tooling
    2. What does it look like in practice?
      • Trusting the automated tool to do what it does without checking (e.g., “Fetch Info” sometimes does not extract all the text from the site)
  2. Confirmation bias
    1. What is it?
      • The tendency to accept or reject new information based on whether it conforms to pre-existing beliefs.
    2. What does it look like in practice?
      • Favoring submissions, wording, other content that conforms to pre-existing beliefs (e.g. Accepting, with insufficient evidence, that allegations against an entity are accurate because of general distaste for that entity)
  3. Cultural bias
    1. What is it?
      • Interpretation and judgment by standards inherent to one's own culture
    2. What does it look like in practice?
      • Making it easier or harder for certain incidents to pass ingestion criteria because of cultural perceptions of what is harmful, of how responsibility should be determined, or of other factors that may be viewed differently in other cultural contexts.