Since most applications came from men, the algorithm automatically disqualified female applicants as a result. However, the model was trained to vet applicants by observing patterns in resumes submitted over a decade-long period. In theory, this would allow the company to identify promising candidates amongst hundreds of resumes. Amazon RecruitmentĪmazon’s recruitment tool once used artificial intelligence to give job candidates a score between one and five stars. Had Google trained the algorithm with more examples of people with dark skin, the failure could have been avoided. In his book, Christian explains several cases where machine learning algorithms have caused embarrassing and sometimes damaging failures.Īn algorithm used by the search engine giant in facial recognition software tagged people with dark skin as gorillas. Real-world examples of the alignment problem This scenario represents the essence of the alignment problem. When training data is poor quality or simply insufficient, algorithmic output suffers. In other words, artificial intelligence is only as robust as the data used to train it. While machine learning algorithms scale well with the availability of data and computing resources, they are nonetheless complex mathematical functions comparing observations to programmed outcomes. This process is helped by high-speed internet, cloud computing, the internet of things (IoT), mobile devices, and a plethora of emerging technologies that collect data on anything and everything. Growing interest in machine and deep learning has meant the algorithms underpinning everything from baseball games to oil supply chains are being digitized. With every major field of artificial intelligence trying to replicate human intelligence, problems invariably arise when developers expect AI to act with the rationality and logic of a person. Understanding the alignment problemĪrtificial intelligence has come a long way in recent years, with humankind now creating machines that can perform remarkable feats.īut after six decades of intensive research and development, aligning AI systems with human goals and values remains an elusive task. In the book, Christian outlines the challenges of ensuring AI models capture “ our norms and values, understand what we mean or intend, and, above all, do what we want.” The alignment problem describes the problems associated with building powerful artificial intelligence systems that are aligned with their operators. The alignment problem was popularised by author Brian Christian in his 2020 book The Alignment Problem: Machine Learning and Human Values.

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Brian christian the alignment problem