# A Framework for Understanding AI-Induced Field Change: How AI Technologies are Legitimized and Institutionalized

arXiv: https://arxiv.org/abs/2108.07804 · HTML: https://mlsanity.com/paper/2108.07804/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 112 references that are in the map.
- First posted on arXiv 18 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Regulatory Markets for AI Safety](https://mlsanity.com/paper/2001.00078/) (2019)
- [A Nutritional Label for Rankings](https://mlsanity.com/paper/1804.07890/) (2018)

## Built on by (most cited first, 1 of 1)

- [A short review of the main concerns in A.I. development and application within the public sector supported by NLP and TM](https://mlsanity.com/paper/2308.02042/) (2023)

## Abstract (arXiv)

Artificial intelligence (AI) systems operate in increasingly diverse areas, from healthcare to facial recognition, the stock market, autonomous vehicles, and so on. While the underlying digital infrastructure of AI systems is developing rapidly, each area of implementation is subject to different degrees and processes of legitimization. By combining elements from institutional theory and information systems-theory, this paper presents a conceptual framework to analyze and understand AI-induced field-change. The introduction of novel AI-agents into new or existing fields creates a dynamic in which algorithms (re)shape organizations and institutions while existing institutional infrastructures determine the scope and speed at which organizational change is allowed to occur. Where institutional infrastructure and governance arrangements, such as standards, rules, and regulations, still are unelaborate, the field can move fast but is also more likely to be contested. The institutional infrastructure surrounding AI-induced fields is generally little elaborated, which could be an obstacle to the broader institutionalization of AI-systems going forward.