Skip to Content
logologo
AI Incident Database
Open TwitterOpen RSS FeedOpen FacebookOpen LinkedInOpen GitHub
Open Menu
Discover
Submit
  • Welcome to the AIID
  • Discover Incidents
  • Spatial View
  • Table View
  • List view
  • Entities
  • Taxonomies
  • Submit Incident Reports
  • Submission Leaderboard
  • Blog
  • AI News Digest
  • Risk Checklists
  • Random Incident
  • Sign Up
Collapse
Discover
Submit
  • Welcome to the AIID
  • Discover Incidents
  • Spatial View
  • Table View
  • List view
  • Entities
  • Taxonomies
  • Submit Incident Reports
  • Submission Leaderboard
  • Blog
  • AI News Digest
  • Risk Checklists
  • Random Incident
  • Sign Up
Collapse

Incident 86: Coding Errors in Leaving Certificate Grading Algorithm Caused Inaccurate Scores in Ireland

Description: Errors in Irish Department of Education's algorithm to calculate students’ Leaving Certificate exam grades resulted in thousands of inaccurate scores.

Tools

New ReportNew ReportNew ResponseNew ResponseDiscoverDiscoverView HistoryView History

Entities

View all entities
Alleged: Irish Department of Education and Skills developed and deployed an AI system, which harmed Irish Department of Education and Skills and Leaving Certificate exam takers.

Incident Stats

Incident ID
86
Report Count
2
Incident Date
2020-10-08
Editors
Sean McGregor, Khoa Lam
Applied Taxonomies
CSETv0, CSETv1, GMF, MIT

CSETv1 Taxonomy Classifications

Taxonomy Details

Incident Number

The number of the incident in the AI Incident Database.
 

86

CSETv0 Taxonomy Classifications

Taxonomy Details

Problem Nature

Indicates which, if any, of the following types of AI failure describe the incident: "Specification," i.e. the system's behavior did not align with the true intentions of its designer, operator, etc; "Robustness," i.e. the system operated unsafely because of features or changes in its environment, or in the inputs the system received; "Assurance," i.e. the system could not be adequately monitored or controlled during operation.
 

Specification

Physical System

Where relevant, indicates whether the AI system(s) was embedded into or tightly associated with specific types of hardware.
 

Software only

Level of Autonomy

The degree to which the AI system(s) functions independently from human intervention. "High" means there is no human involved in the system action execution; "Medium" means the system generates a decision and a human oversees the resulting action; "low" means the system generates decision-support output and a human makes a decision and executes an action.
 

Medium

Nature of End User

"Expert" if users with special training or technical expertise were the ones meant to benefit from the AI system(s)’ operation; "Amateur" if the AI systems were primarily meant to benefit the general public or untrained users.
 

Amateur

Public Sector Deployment

"Yes" if the AI system(s) involved in the accident were being used by the public sector or for the administration of public goods (for example, public transportation). "No" if the system(s) were being used in the private sector or for commercial purposes (for example, a ride-sharing company), on the other.
 

Yes

Data Inputs

A brief description of the data that the AI system(s) used or were trained on.
 

student's class and exam grades

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

7.3. Lack of capability or robustness

Risk Domain

The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
 
  1. AI system safety, failures, and limitations

Entity

Which, if any, entity is presented as the main cause of the risk
 

AI

Timing

The stage in the AI lifecycle at which the risk is presented as occurring
 

Post-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Unintentional

Incident Reports

Reports Timeline

Explainer: why has one line of computer code caused such disruption to the Leaving Cert grades?+1
Leaving Cert: Why the Government deserves an F for algorithms
Explainer: why has one line of computer code caused such disruption to the Leaving Cert grades?

Explainer: why has one line of computer code caused such disruption to the Leaving Cert grades?

independent.ie

Leaving Cert: Why the Government deserves an F for algorithms

Leaving Cert: Why the Government deserves an F for algorithms

irishtimes.com

Explainer: why has one line of computer code caused such disruption to the Leaving Cert grades?
independent.ie · 2020

This week it emerged that a problem was discovered with the Leaving Certificate calculated grades system which means thousands of students will have their results upgraded. But what happened?

What is an algorithm?

It’s code that makes decis…

Leaving Cert: Why the Government deserves an F for algorithms
irishtimes.com · 2020

August, following the grading algorithm debacle in the UK, I wrote a column wondering if perhaps this unfortunate event might prove a critical tipping point for public trust in the almighty algorithm.

This new generation in the UK, about to…

Variants

A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.

Similar Incidents

By textual similarity

Did our AI mess up? Flag the unrelated incidents

Northpointe Risk Models

Northpointe Risk Models

May 2016 · 15 reports
Racist AI behaviour is not a new problem

Racist AI behaviour is not a new problem

Mar 1998 · 4 reports
Employee Automatically Terminated by Computer Program

Employee Automatically Terminated by Computer Program

Oct 2014 · 20 reports
Previous IncidentNext Incident

Similar Incidents

By textual similarity

Did our AI mess up? Flag the unrelated incidents

Northpointe Risk Models

Northpointe Risk Models

May 2016 · 15 reports
Racist AI behaviour is not a new problem

Racist AI behaviour is not a new problem

Mar 1998 · 4 reports
Employee Automatically Terminated by Computer Program

Employee Automatically Terminated by Computer Program

Oct 2014 · 20 reports

Research

  • Defining an “AI Incident”
  • Defining an “AI Incident Response”
  • Database Roadmap
  • Related Work
  • Download Complete Database

Project and Community

  • About
  • Contact and Follow
  • Apps and Summaries
  • Editor’s Guide

Incidents

  • All Incidents in List Form
  • Flagged Incidents
  • Submission Queue
  • Classifications View
  • Taxonomies

2023 - AI Incident Database

  • Terms of use
  • Privacy Policy
  • Open twitterOpen githubOpen rssOpen facebookOpen linkedin
  • 8b8f151