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In response to the Covid-19 pandemic, the International Baccalaureate (IB) replaced final exams with a proprietary statistical grading algorithm. Students and teachers reported that the system produced unexpectedly low grades, causing significant harm to students' university admissions and scholarship prospects. The lack of transparency regarding the model's design and inputs led to widespread criticism and calls for accountability.
In response to the Covid-19 pandemic, the International Baccalaureate final exams were replaced by a calculated score, prompting complaints of unfairness from teachers and students.
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 algorithm unfairly represented students' individual capabilities by tying their grades to historical school performance and pooling data from other schools, resulting in unequal treatment.
The IB refused to disclose the formula or methodology of the grading algorithm, preventing external scrutiny and leaving students unable to understand how their grades were determined.
The primary failure was the statistical model's inability to robustly and accurately predict individual student performance, leading to erroneous and volatile grade assignments.
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 academic and financial harms were directly caused by the inaccurate grade predictions generated by the deployed IB statistical model, which was intended to provide fair grades but unintentionally produced flawed outcomes.
Added categories peak at 3: Substantial (Psychological Harm, Inferred)
The International Baccalaureate's deployment of a flawed grading algorithm in 2020 caused widespread disruption to university admissions and scholarships globally. While causing notable societal frustration and individual financial harm, the incident holds negligible direct national security implications.
The grading algorithm functioned as a tool, applying a specific statistical formula to student and school data inputs to generate predicted grades without autonomous planning or real-time environmental interaction.
Risk Level 2: High Risk. The system is an educational assessment tool that directly determines student grades and university admissions, which falls under 'Educational and vocational training systems affecting access to education and employment'.
The primary function of the algorithm was to assess and predict students' academic performance and assign final grades in place of canceled exams.
The report provides clear, detailed factual accounts of the algorithm's deployment, its inputs, and the direct consequences on students' lives. The role of the AI system is explicitly stated, and the scale of the impact is well-documented.
None, as the causal link between the algorithm's output and the resulting academic/financial harm is clear and uncontested.
Technology
Flawed Grading Algorithm
The statistical formula predicted lower than expected scores for students.
Bespoke Equation Mismatch
Bespoke equations for each school failed to accurately predict individual work.
Data Inputs
Historical School Data Use
Basing individual grades on past school performance created unfairness.
Data Pooling for New Schools
Using pooled data from other schools led to inaccurate grade predictions.
Noisy Small Class Data
Smaller classes had fewer data points, generating noisier results.
Human Factors
Unequal Student Effort
Late communication meant some students put less effort into assignments.
Process and Methods
Lack of Model Transparency
The formula and inputs were not disclosed for external scrutiny.
Hastily Deployed System
The system was rapidly deployed during Covid-19 without proper testing.
Inconsistent Deadlines
Assignments were submitted before students knew they would determine grades.
Management
Inadequate Risk Assessment
Management failed to assess the risks of unfair automated grading.
Poor Accountability
Management declined to explain the model and charged for appeals.
Regulatory Environment
Lack of Algorithmic Regulation
No regulatory standards governed the use of algorithms for grading.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
