Classifications and definitions

Definitions

Harm. AIAAIC defines harm as "Hurt, damage, degradation or loss to people, property or things." 

• Hurt: physical, emotional or mental pain

• Damage: injury to a person, property or reputation

• Degradation: decline in quality, performance, dignity or standing

• Loss: detriment, disadvantage or deprivation from failure to keep, have or get

• People: includes individuals and groups of people

• Property: includes tangible property, such as vehicles and machinery, and intangible property, such as digital assets and intellectual property

• Things: includes tangible things, such as animals and the environment, and intangible things, such as trust in an institution or process

More complex entities (e.g. societies, communities, cultures, organisations, businesses, systems and processes) comprise combinations of people, property and/or things.

Technology. AIAAIC defines the technologies it tracks as follows:

• ArtificiaI intelligence. The capability of a machine to imitate intelligent human behaviour (source).

• Algorithm. A procedure for solving a mathematical problem in a finite number of steps that frequently involves repetition of an operation (source).

• Automation. The automatically controlled operation of an apparatus, process, or system by mechanical or electronic devices that take the place of human labour (source).

Exclusions

AIAAIC does not (currently) collect data for the following technologies or issues: 

• Geo-political issues, such as trade disputes

• Legislation and standards, actual or proposed

• Cryptography

• NFTs, DAOs, DeSci/Fi, etc

• CRISPR

• Genomics/genetic algorithms

• Quantum computing

• AGI/super-intelligence/singularity.

Classifying AIAAIC Repository entries

Entries to the AIAAIC Repository are classified as below:

Type

Each entry is categorised as an Incident, System, or Data. 

• Incident. A sudden known or unknown event that becomes public, takes the form of a disruption, loss, emergency, or crisis, and causes or potentially causes harm. Examples: AI system or robot malfunction; Actual or perceived inappropriate or unethical behaviour by a system operator or developer; Data privacy or info confidentiality leak exposes system vulnerability. It may also take the form of concerns publicly raised about the nature and/or potential impacts of a technology system or set of systems, but without evidence of actual, recognised harms. Examples: A public debate/controversy about an unlaunched system, technology, or patent; Research indicating unwarranted carbon emissions or water consumption by a system

• System. A technology product, project, or programme, and its governance. Examples: Amazon Buy Box; ChatGPT chatbot

• Data. A public or proprietary dataset/database that has been shown to be inaccurate, unreliable, biased, overly intrusive, etc, and/or that results in issues or incident(s) directly or indirectly associated with the AI, algorithmic, or automation system(s) that draw(s) on it. Examples: People in Photo Albums (PIPA) dataset; Stanford University Brainwash cafe facial recognition dataset; Google GoEmotions dataset mis-labelling

Released

The year (and, on the website, month) a system or dataset/database is soft and/or formally launched.

Occurred

The year (and, on the website, month) an incident first occurs or is publicly reported.

Country

The country or jurisdiction impacted by an incident. 

Sector

The industry (including government and non-profit) sector reportedly impacted by an incident: Aerospace; Agriculture; Automotive; Banking/financial services; Beauty/cosmetics; Business/professional services; Chemicals; Construction; Consumer goods; Education; Food/food services; Gambling; Govt - home/interior, welfare, police, justice, security, defence, foreign, etc; Health; Media/entertainment/sports/arts; NGO/non-profit/social enterprise; Manufacturing/engineering; Politics; Personal; Real estate; Retail; Technology; Telecoms; Tourism/leisure; Transport/logistics

Deployer

The individual(s), group(s), or organisation(s) operating or managing the system/data involved in an incident or issue on a day-to-day basis, or the platform(s) on which the system is hosted or being carried.

Developer

The individual(s) or organisation(s) involved in designing, or developing/providing the system/data, and/or that commissions a system to be developed with a view to placing it on the market or putting it into service under its own name or trademark whether for payment or free of charge or that adapts general purpose systems for a specific intended purpose. There may be multiple providers along the system lifecycle.

System name

The system, or set of systems, or dataset/database involved in an incident or controversy.

Technology

The type(s) of technology deployed in the system. These include the following technology types and their application(s): Advertising management system; Advertising authorisation system; Age detection; Anomaly detection; Augmented reality (AR); Automatic identification system (AIS); Automated license plate/number recognition (ALPR, ANPR); Behavioural analysis; Biodiversity assessment algorithm; Blockchain; Border control system; Bot/intelligent agent; Chatbot; Collaborative filtering; Colourisation; Computer vision; Content labelling system; Content ranking system; Content recommendation system; Credit score algorithm; Data matching algorithm; Decision support; Deepfake - video, audio, image, text; Deep learning; Diversity Recognition; Driver assistance system; Drone; Emotion detection; Emotion recognition; Environmental sensing; Expert system; Facial analysis; Facial detection; Facial identification; Facial recognition; Fingerprint biometrics; Gait recognition; Generative AI; Gender detection; Gesture recognition; Gun/weapons detection; Gunshot detection; Heart rate variability algorithm; Image classification; Image segmentation; Image recognition; Learning management system; Link blocking; Location tracking; Location recognition; Machine learning; Mask recognition; Neural network; NLP/text analysis; Object detection; Object recognition; Order matching algorithm; Palm print scanning; Pattern recognition; Pay algorithm; Performance scoring algorithm; Personality analysis; Prediction algorithm; Pricing algorithm; Probabilistic genotyping; Rating system; Recommendation algorithm; Reinforcement learning; Resource assessment algorithm; Risk assessment algorithm; Robotic Process Automation (RPA); Robotics; Routing algorithm; Safety management system; Saliency algorithm; Scheduling algorithm; Search engine algorithm; Self-driving system; Signal processing; Social media monitoring; Sleep sensing; Smile recognition; Speech-to-text; Speech recognition; Suicide prevention algorithm; Text-to-image; Triage algorithm; Virtual currency; Virtual reality (VR); Vital signs detection; Voice recognition; Voice synthesis; Web accessibility overlay; Workforce management system

Purpose

The stated, likely, or alleged purpose(s) for which the system or dataset/database is being used. This may include: Rank content/search results; Recommend users/groups; Recommend content; Moderate content; Minimise mis/disinformation; Identify/verify identity; Increase productivity; Increase speed to market; Improve quality; Engage users/increase revenue; Improve customer service; Increase visibility; Increase revenue/sales; Increase engagement; Moderate content; Improve insights; Cut costs; Defraud; Scare/confuse/destabilise; Damage reputation

News trigger

See AIAAIC's News Trigger Taxonomy

Ethical issue

See AIAAIC's Ethical Issue Taxonomy.

External harm

See AIAAIC's External Harm Taxonomy

Harm consequence

See AIAAIC's Harm Consequence Taxonomy