Introduction
Artificial intelligence has advanced at a breathtaking pace, permeating sectors from finance to healthcare and fundamentally altering the way societies function. Yet behind the veneer of efficiency and progress lies a growing catalogue of harms, including mass surveillance, algorithmic bias, job displacement, and the erosion of human autonomy. This essay argues that, on balance, artificial intelligence will do more harm than good because its risks are systemic, difficult to reverse, and disproportionately borne by the most vulnerable members of society.
AI entrenches and amplifies existing social biases, causing systemic harm to marginalised communities
Explain
Machine learning algorithms are trained on historical data that encodes longstanding prejudices relating to race, gender, and socioeconomic status. When these biased models are deployed in high-stakes domains such as criminal justice, hiring, and credit scoring, they do not merely replicate existing inequalities but entrench them at scale, often with a veneer of objectivity that makes discrimination harder to identify and challenge.
Example
In the United States, the COMPAS recidivism algorithm was found by ProPublica to be nearly twice as likely to falsely la…
Introduction
While fears about artificial intelligence dominate public discourse, such anxieties often overlook the extraordinary benefits that AI has already delivered and will continue to deliver across medicine, education, environmental protection, and economic productivity. Every transformative technology in history, from electricity to the internet, has provoked similar alarm, yet the long-term trajectory has consistently been one of net benefit. This essay contends that artificial intelligence will do more good than harm, provided that its development is guided by responsible governance and a commitment to equitable access.
AI has transformative potential to revolutionise healthcare and save millions of lives
Explain
AI's ability to analyse vast datasets, identify patterns, and generate predictions far exceeds human cognitive capacity, making it uniquely suited to medical applications such as early disease detection, drug discovery, and personalised treatment. These capabilities have already begun to translate into tangible improvements in patient outcomes and reductions in healthcare costs, benefits that will only grow as the technology matures.
Example
Google DeepMind's AlphaFold system solved the protein-folding problem in 2020, predicting the three-dimensional structur…
To what extent should the government regulate the use of artificial intelligence?
2023Should we be concerned about the lack of privacy in the modern world?
2023How far should scientific research be subject to ethical constraints?
2022'Science is the greatest threat to the world today.' Discuss.
2019Should every country have the right to carry out nuclear research?
2015