Artificial intelligence (AI) is one of the most promising and fastest growing technologies, with the potential to revolutionize a wide range of industries, from medical to legal. Machine learning is a subset of AI in which the AI ​​is not explicitly programmed but rather “learns” to make decisions from training. Deep learning is a particularly powerful and currently very popular technique within machine learning. Deep learning is based on a neural network of “nodes” arranged in layers connected by weighted connections. These neural networks can be trained on datasets to perform functions that are beyond the scope of an ordinary algorithm relying only on basic logic. It can perform tasks such as recognizing and distinguishing different animals in images or controlling autonomous vehicles. In 2015, Deepmind's AlphaGo AI beat European Go champion Fan Hui in his first match and the world champion in 2016 before competing online against a variety of the world's best Go players and winning all 60 of his matches. AlphaGo used a deep learning neural network to determine which moves to play. This type of game was only possible thanks to AI, as the game contains approximately 10,761 game states; too many problems to be handled with a traditional algorithm. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”? Get the original essay AlphaGo was formed by analyzing thousands of games played by expert Go players, then playing against itself to improve your initial knowledge. In 2017, the AlphaGo team unveiled a new version of its AI called AlphaGo Zero. This AI did not initially train on human data, but taught itself the game from scratch by repeatedly playing against itself. AlphaGo zero outperformed the initial AlphaGo and used less computing power to do so. This is because it was not influenced by the ineffective human biases inherent in the data provided. This self-learning approach, however, can only work in an artificial environment like Go, where the rules are simple and easy to define. In the real world, a computer cannot simulate every aspect of an environment and so an AI solving real-world problems depends on data to train on. As seen with AlphaGo, this introduces human biases into the algorithm's decision-making. Although often benign, there are cases where AI also learns negative human biases. One example is the COMPAS AI algorithm used to help judges determine an offender’s risk of reoffending. A case analysis conducted by ProPublica found that the algorithm favored white people and placed higher risk on those with darker skin color. The creators of the program Northpointe Inc. (now Equivalent) insisted that it was not racist since race is not one of the inputs the algorithm is trained on. In a similar case, a computer science professor who was building an image recognition program noticed that when his algorithm was trained on public datasets, some even endorsed by Facebook and Microsoft, the associations between classic cultural stereotypes such than women who cook or shop and men. and sports equipment was not only on display but even amplified. The problem of AI inheriting negative human biases is not just due to fear of offending, but.