# Algorithmic Systems **Domain:** Computing / Institutions / Governance **Doc Type:** Concept Node **Maturity:** Developed ## Definition **Algorithmic systems** are sociotechnical arrangements in which formal procedures, software, data, models, interfaces, institutional rules, and human operators jointly produce classifications, rankings, recommendations, permissions, or actions. The system is larger than the algorithm. Training data, measurement choices, deployment context, incentives, review procedures, and downstream users determine what a computational output can do in the world. ## Governance Significance When algorithmic systems allocate visibility, credit, opportunity, scrutiny, or access, description becomes intervention. Their legitimacy therefore depends on inspectability, contestability, uncertainty handling, meaningful appeal, and limits on automated authority. ## See Also [[wiki/Model-Based Governance|Model-Based Governance]] · [[wiki/Social Control|Social Control]] · [[wiki/Feedback Loops|Feedback Loops]] · [[wiki/Agency|Agency]] ## Simple Reminders, Quotations, and Thoughts > "More likely, advancing computers and algorithms will stand for nothing, and will be the amplifiers and implementers of consciously-directed human choices." > **— D.A. Wallach**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/AI Control/Algorithms Will Amplify Human Choices by D.A. Wallach|Algorithms Will Amplify Human Choices by D.A. Wallach]] > "One gloomy possibility is that we become zombie consumers of a machine-run world straight out of an apocalyptic futuristic film noir." > **— Athena Vouloumanos**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/Humans Could Become Consumers of a Machine-Run World by Athena Vouloumanos|Humans Could Become Consumers of a Machine-Run World by Athena Vouloumanos]] > "Looking ahead, I could live with a partnership with machine learning in order to make complex modern life more resource-efficient in a way that human brains cannot." > **— Laurence C. Smith**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/Machine Learning Could Make Modern Life More Efficient by Laurence C. Smith|Machine Learning Could Make Modern Life More Efficient by Laurence C. Smith]] > "The patterns involved can easily exceed what the human mind can grasp." > **— Bart Kosko**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/Machine Patterns Can Exceed Human Understanding by Bart Kosko|Machine Patterns Can Exceed Human Understanding by Bart Kosko]] > "In fact, natural cognition is likely much more complex and detailed than our current incarnations of artificial intelligence or cognitive computing." > **— Maximilian Schich**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/Natural Cognition Is More Complex Than Today’s AI by Maximilian Schich|Natural Cognition Is More Complex Than Today’s AI by Maximilian Schich]]