IT auditors are increasingly being called upon to understand and use machine learning techniques in their work. As the technology becomes more widespread, it is essential that auditors have a solid understanding of how it works so they can properly assess its risks and vulnerabilities.
Machine learning algorithms are used by computers to make predictions based on data sets. The aim of this process is usually to improve the performance or accuracy of a system or process by fine-tuning its responses to recurring patterns. Machine learning has been applied in many different industries, including finance, health care, marketing, and transportation research.
Auditing using machine knowledge is critical for two reasons: First, machines can't lie - they don't get tired or emotional like humans do; second, because most wrong decisions made during testing are due to lack of familiarity with the subject matter rather than malice or bad intentions on the part of those performing testing..
As audit teams become ever more reliant on automated tools and software for their workflows (and as these tools continue evolving), it's important that members possess an understanding not just of how these technologies work but also why they're effective given specific business needs..
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