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what is awful-ai fr?

daviddao/awful-ai — explained in plain English

Analysis updated 2026-06-24

7,427Audience · researcherComplexity · 1/5Setup · easy

tl;dr

A curated catalog of real-world cases where AI caused harm, encoded bias, or raised ethical concerns, organized by category and citable via Zenodo for academic use.

vibe map

mindmap
  root((awful-ai))
  What it is
    Harm case catalog
    Bias documentation
    Academic citable
  Categories
    Discrimination
    Surveillance
    Disinformation
    Autonomous weapons
  Contestational AI
    Auditing tools
    Counter-AI projects
  Contribution
    Pull requests
    Zenodo citation

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

what do people make with this?

VIBE 1

Reference specific AI bias cases by category for a research paper or ethics report

VIBE 2

Find documented examples of AI discrimination in facial recognition, hiring, or education for a talk or article

VIBE 3

Cite harmful AI deployments using the Zenodo DOI in academic work

VIBE 4

Discover contestational AI projects that audit or counter harmful AI systems

how it stacks up fr

daviddao/awful-aifex-emu/fexopen-mmlab/mmagic
Stars7,4277,4267,426
LanguageC++Jupyter Notebook
Setup difficultyeasyhardmoderate
Complexity1/54/54/5
Audienceresearcherdeveloperresearcher

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · easy time til it works · 5min

in plain english

Awful AI is a curated list of real-world cases where artificial intelligence has been used in ways that cause harm, reflect bias, or raise serious ethical concerns. The project was created to publicly track these uses and raise awareness about the risks of AI deployment in society. It is a reference document and catalog, not a piece of software. The list is organized into categories. The discrimination section covers AI systems shown to encode racial, gender, or socioeconomic bias, such as facial recognition tools that perform poorly for people with darker skin, an image classification program that labeled Black faces incorrectly, and a UK grade-prediction algorithm that disadvantaged students from lower-income backgrounds. The disinformation and fakes category covers AI-generated deepfakes, synthetic media used for propaganda, and automated influence campaigns. The surveillance section documents cases where AI has been used to track, score, or monitor people without meaningful consent. Other categories include social credit systems, misleading or fraudulent AI-powered products, the contribution of large AI model training to climate change, and autonomous weapon systems. The README notes that AI systems often amplify existing social biases even when trained on balanced data, and that the field is susceptible to attacks and difficult to control. A second section of the list covers contestational AI: research and technical projects aimed at exposing, auditing, or countering harmful AI applications. The repository accepts contributions via pull request and has been formally published on Zenodo so it can be cited in academic work. The full README is longer than what was shown.

prompts (copy fr)

prompt 1
Using the Awful AI list as context, write a section for my ethics paper on documented cases of AI racial bias in facial recognition systems
prompt 2
Give me a summary of surveillance AI cases from the Awful AI catalog and explain the key risks for individuals
prompt 3
I am building an AI product, use the Awful AI list to generate a checklist of bias risks I should test before launching
prompt 4
List examples from Awful AI of AI systems used in education that caused harm and explain what went wrong in each case

Frequently asked questions

what is awful-ai fr?

A curated catalog of real-world cases where AI caused harm, encoded bias, or raised ethical concerns, organized by category and citable via Zenodo for academic use.

How hard is awful-ai to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is awful-ai for?

Mainly researcher.

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This repo across BitVibe Labs

double-check against the repo, no cap.