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Artificial intelligence in hiring confers many benefits, but it also has some challenges which have concerned experts. [15] AI is only as good as the data it is using. Biases can inadvertently be baked into the data used in AI. [1] Often companies will use data from their employees to decide what people to recruit or hire.
The term "data science" has been traced back to 1974, when Peter Naur proposed it as an alternative name to computer science. [6] In 1996, the International Federation of Classification Societies became the first conference to specifically feature data science as a topic. [6] However, the definition was still in flux.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
The predicted growth in machine learning included an estimated doubling of ML pilots and implementations from 2017 to 2018, and again from 2018 to 2020. [4] MLOps rapidly began to gain traction among AI/ML experts, companies, and technology journalists as a solution that can address the complexity and growth of machine learning in businesses. [5]
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The challenge here, as with all synthetic-data-generation efforts, is in ensuring that the synthetic data is consistent in terms of meeting the constraints on real data. As the number of variables/features in the input data increase, and strong dependencies between variables exist, it becomes increasingly difficult to generate synthetic data ...