As the founders of the Institute for Ethical Artificial Intelligence in Education, we are appalled by the manner in which an algorithm has been used to decide students' A-level and GCSE grades (A-level results: No 10 hints algorithm set to be ditched in England, 17 August). The exam grading algorithm may not be sophisticated AI, but to be ethical it should still adhere to certain principles, including that it should impact positively on teaching and learning; be fair and explainable to the people on whom it impacts; and that it should use data that is not biased towards or against any particular group of people. The algorithm used this year to decide the grades for students who have worked hard in extremely difficult circumstances adheres to none of these principles. It is, without question, unethical and harmful to education in general and a significant number of pupils in particular. It is a gross abuse of the algorithmic approach.

An expert on artificial intelligence has called for all algorithms that make life-changing decisions – in areas from job applications to immigration into the UK – to be halted immediately. Prof Noel Sharkey, who is also a leading figure in a global campaign against "killer robots", said algorithms were so "infected with biases" that their decision-making processes could not be fair or trusted. A moratorium must be imposed on all "life-changing decision-making algorithms" in Britain, he said. Sharkey has suggested testing AI decision-making machines in the same way as new pharmaceutical drugs are vigorously checked before they are allowed on to the market. In an interview with the Guardian, the Sheffield University robotics/AI pioneer said he was deeply concerned over a series of examples of machine-learning systems being loaded with bias.

There is thus a great need to define inputs, outputs, and their interactive relationships clearly. Inevitably, technologists would code fairness as a narrowly defined modular property of the machine learning system. However, fairness is not a well defined nor universally applicable concept, to begin with as it has to be understood amidst a particular social context. Abstracting away this context is thus an abstraction error. With the presence of this error, AI would have an ineffective, inaccurate and misguided interpretation and thus, quantification of fairness when it is introduced to varying societal systems.