Inaccurate perception definition how to#
How to prevent biasĪwareness and good governance can help prevent machine learning bias an organization that recognizes the potential for bias can then implement and institute best practices to combat it that include the following steps: If the data population has enough variety in it, biases should be drowned out by the variance.Īs such, the objective in machine learning is to have a tradeoff, or balance, between the two in order to develop a system that produces a minimal amount of errors. In other words, variance is a problematic sensitivity to small fluctuations in the training set, which, like bias, can produce inaccurate results.Īlthough bias and variance are different, they are interrelated in that a level of variance can help reduce bias. These fluctuations, or noise, however, should not have an impact on the intended model, yet the system is using that noise for modeling. Unlike bias, variance is a reaction to real and legitimate fluctuations in the data sets. Like bias, variance is an error that results when the machine learning produces the wrong assumptions based on the training data. This happens when an important data point is left out of the data being used -something that can happen if the modelers don't recognize the data point as consequential.ĭata scientists and others involved in building, training and using machine learning models must consider not just bias, but also variance when seeking to create systems that can deliver consistently accurate results. Using pictures of happy workers to train a system meant to assess a workplace environment could be biased if the workers in the pictures knew they were being measured for happiness a system being trained to precisely assess weight will be biased if the weights contained in the training data were consistently rounded up. As the name suggests, this bias arises due to underlying problems with the accuracy of the data and how it was measured or assessed. For example, using data about medical professionals that includes only female nurses and male doctors would thereby perpetuate a real-world gender stereotype about healthcare workers in the computer system. In this case, the data used to train the system reflects existing prejudices, stereotypes and/or faulty societal assumptions, thereby introducing those same real-world biases into the machine learning itself. For example, using training data that features only female teachers will train the system to conclude that all teachers are female.
In this type of bias, the data used is either not large enough or representative enough to teach the system. This happens when there's a problem with the data used to train the machine learning model. This occurs when there's a problem within the algorithm that performs the calculations that power the machine learning computations. Common scenarios, or types of bias, include the following: There are various ways that bias can be brought into a machine learning system. Types of cognitive bias that can inadvertently affect algorithms are stereotyping, bandwagon effect, priming, selective perception and confirmation bias. Or the individuals could introduce biases because they use incomplete, faulty or prejudicial data sets to train and/or validate the machine learning systems.
These individuals could either create algorithms that reflect unintended cognitive biases or real-life prejudices. Machine learning bias generally stems from problems introduced by the individuals who design and/or train the machine learning systems. Faulty, poor or incomplete data will result in inaccurate predictions, reflecting the " garbage in, garbage out" admonishment used in computer science to convey the concept that the quality of the output is determined by the quality of the input. Machine learning, a subset of artificial intelligence ( AI), depends on the quality, objectivity and size of training data used to teach it. Machine learning bias, also sometimes called algorithm bias or AI bias, is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process.
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