The reviewed record of science sign in
Pith

arxiv: 2206.09511 · v2 · pith:6DP63UER · submitted 2022-06-20 · cs.LG

The Fallacy of AI Functionality

Reviewed by Pithpith:6DP63UERopen to challenge →

classification cs.LG
keywords functionalityoftenpolicydeployedaffectedalgorithmicanalyzeargue
0
0 comments X
read the original abstract

Deployed AI systems often do not work. They can be constructed haphazardly, deployed indiscriminately, and promoted deceptively. However, despite this reality, scholars, the press, and policymakers pay too little attention to functionality. This leads to technical and policy solutions focused on "ethical" or value-aligned deployments, often skipping over the prior question of whether a given system functions, or provides any benefits at all. To describe the harms of various types of functionality failures, we analyze a set of case studies to create a taxonomy of known AI functionality issues. We then point to policy and organizational responses that are often overlooked and become more readily available once functionality is drawn into focus. We argue that functionality is a meaningful AI policy challenge, operating as a necessary first step towards protecting affected communities from algorithmic harm.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. GraphFlow: An Architecture for Formally Verifiable Visual Workflows Enabling Reliable Agentic AI Automation

    cs.AI 2026-05 unverdicted novelty 4.0

    GraphFlow is an architecture for formally verifiable visual workflows that treats diagrams as executable specs with proof-checkable contracts, backed by a pilot of 8728 runs at 97.08% completion on an early prototype ...