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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Automated decision-making | 2/3 | https://en.wikipedia.org/wiki/Automated_decision-making | reference | science, encyclopedia | 2026-05-05T07:11:00.240751+00:00 | kb-cron |
=== Law === In legal systems around the world, algorithmic tools such as risk assessment instruments (RAI), are being used to supplement or replace the human judgment of judges, civil servants and police officers in many contexts. In the United States RAI are being used to generate scores to predict the risk of recidivism in pre-trial detention and sentencing decisions, evaluate parole for prisoners and to predict "hot spots" for future crime. These scores may result in automatic effects or may be used to inform decisions made by officials within the justice system. In Canada ADM has been used since 2014 to automate certain activities conducted by immigration officials and to support the evaluation of some immigrant and visitor applications.
=== Economics === Automated decision-making systems are used in certain computer programs to create buy and sell orders related to specific financial transactions and automatically submit the orders in the international markets. Computer programs can automatically generate orders based on predefined set of rules using trading strategies which are based on technical analyses, advanced statistical and mathematical computations, or inputs from other electronic sources.
=== Business ===
==== Continuous auditing ==== Continuous auditing uses advanced analytical tools to automate auditing processes. It can be utilized in the private sector by business enterprises and in the public sector by governmental organizations and municipalities. As artificial intelligence and machine learning continue to advance, accountants and auditors may make use of increasingly sophisticated algorithms which make decisions such as those involving determining what is anomalous, whether to notify personnel, and how to prioritize those tasks assigned to personnel.
=== Media and entertainment === Digital media, entertainment platforms, and information services increasingly provide content to audiences via automated recommender systems based on demographic information, previous selections, collaborative filtering or content-based filtering. This includes music and video platforms, publishing, health information, product databases and search engines. Many recommender systems also provide some agency to users in accepting recommendations and incorporate data-driven algorithmic feedback loops based on the actions of the system user. Large-scale machine learning language models and image creation programs being developed by companies such as OpenAI and Google in the 2020s have restricted access however they are likely to have widespread application in fields such as advertising, copywriting, stock imagery and graphic design as well as other fields such as journalism and law.
=== Advertising === Online advertising is closely integrated with many digital media platforms, websites and search engines and often involves automated delivery of display advertisements in diverse formats. 'Programmatic' online advertising involves automating the sale and delivery of digital advertising on websites and platforms via software rather than direct human decision-making. This is sometimes known as the waterfall model which involves a sequence of steps across various systems and players: publishers and data management platforms, user data, ad servers and their delivery data, inventory management systems, ad traders and ad exchanges. There are various issues with this system including lack of transparency for advertisers, unverifiable metrics, lack of control over ad venues, audience tracking and privacy concerns. Internet users who dislike ads have adopted counter measures such as ad blocking technologies which allow users to automatically filter unwanted advertising from websites and some internet applications. In 2017, 24% of Australian internet users had ad blockers.
=== Health === Deep learning AI image models are being used for reviewing x-rays and detecting the eye condition macular degeneration.
=== Social services === Governments have been implementing digital technologies to provide more efficient administration and social services since the early 2000s, often referred to as e-government. Many governments around the world are now using automated, algorithmic systems for profiling and targeting policies and services including algorithmic policing based on risks, surveillance sorting of people such as airport screening, providing services based on risk profiles in child protection, providing employment services and governing the unemployed. A significant application of ADM in social services relates to the use of predictive analytics – eg predictions of risks to children from abuse/neglect in child protection, predictions of recidivism or crime in policing and criminal justice, predictions of welfare/tax fraud in compliance systems, predictions of long term unemployment in employment services. Historically these systems were based on standard statistical analyses, however from the early 2000s machine learning has increasingly been developed and deployed. Key issues with the use of ADM in social services include bias, fairness, accountability and explainability which refers to transparency around the reasons for a decision and the ability to explain the basis on which a machine made a decision. For example Australia's federal social security delivery agency, Centrelink, developed and implemented an automated processes for detecting and collecting debt which led to many cases of wrongful debt collection in what became known as the RoboDebt scheme.