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The Irish Department of Education implemented a custom-built algorithm to calculate student grades for the 2020 Leaving Certificate due to pandemic-related disruptions. Coding errors within the system led to thousands of students receiving incorrect grades, both higher and lower than they should have been. The incident highlighted concerns regarding the lack of transparency, testing, and accountability in automated decision-making systems used for high-stakes public services.
Errors in Irish Department of Education's algorithm to calculate students’ Leaving Certificate exam grades resulted in thousands of inaccurate scores.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.
Also classified as
The grading algorithm's code was proprietary and not open source, and the system suffered from 'inscrutability', meaning it could not justify why a specific grade prediction was made.
The custom-built grading algorithm failed to perform reliably due to coding errors, leading to incorrect grade calculations for thousands of students.
AI system
Due to a decision or action made by an AI system
Unintentional
Due to an unexpected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The grading errors were caused by unintended software bugs in the deployed grading algorithm, which produced incorrect grade calculations for thousands of students.
Added categories peak at 2: Minor (Psychological Harm, Indirect)
An unintentional coding error in Ireland's national grading algorithm led to incorrect results for thousands of students, disrupting university admissions and highlighting governance challenges in automated public decision-making. The incident possesses minor national security implications, primarily affecting government service delivery and public trust.
The algorithm functioned as a deterministic tool designed to execute a specific mathematical formula to calculate grades from student data inputs, without autonomous planning or interactive assistant capabilities.
High Risk: The system is an educational grading algorithm used to determine high-stakes Leaving Certificate results, which directly affects students' access to higher education, fitting the EU AI Act's definition of high-risk AI systems in education.
The primary goal of the algorithm was to calculate and assign grades to students based on their past academic performance, which is a form of automatic skill assessment. These grades were subsequently used to allocate university places.
The reports provide clear, consistent, and detailed factual accounts of the grading algorithm's errors, the developer involved, the scale of the impact, and the resulting societal disruption. There are no major conflicting accounts.
None. The incident is clearly a software failure in a high-stakes public grading system.
Technology
Coding Errors in Algorithm
A single line of code contained two errors affecting predicted grades.
Custom-Built System Lack of Maturity
The software was custom-built and not previously tested or used elsewhere.
Algorithm Inscrutability
The system could not explain or justify its grade predictions.
Data Inputs
Incorrect Subject Inclusion
CSPE subject results were wrongly included in the grading calculations.
Incorrect Subject Selection
Code substituted the worst two subjects instead of the best two.
Human Factors
Developer Coding Mistakes
Human error led to two critical bugs in a single line of code.
Process and Methods
Insufficient Software Testing
The grading code was not sufficiently tested before release.
Lack of Code Transparency
The code was not open source or reviewable by external experts.
Management
Severe Time Constraints
Urgency of the situation prevented thorough testing of the system.
Resourcing Constraints
Limited resources impacted the ability to adequately develop and test code.
Regulatory Environment
Lack of Algorithmic Transparency Laws
Absence of regulations requiring corporate algorithm transparency.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
