# what is bias in machine learning

Although most AI operates in a "black box" in which its decision-making process is hidden--think, why did my GPS re-route me?--transparency in AI is essential to building trust in our systems. x Learning algorithms typically have some tunable parameters that control bias and variance; for example. We assume that there is a function with noise Through names, for example. Bias reflects problems related to the gathering or use of data, where systems draw improper conclusions about data sets, either because of human intervention or as a result of a lack of cognitive assessment of data. {\displaystyle {\hat {f}}(x)} As AI becomes increasingly interwoven into our lives--fueling our experiences at home, work, and even on the road--it is imperative that we question how and why our machines do what they do. ⁡ It is also known as bias nodes, bias neurons, or bias units = b N ) f ) a D So how exactly has biased data impacted algorithms? D Machine Learning model bias can be understood in terms of some of the following: Lack of an appropriate set of features may result in bias. 0 σ To date, the global program has been completed by 10,000 girls. . i In contrast, algorithms with high bias typically produce simpler models that don't tend to overfit but may underfit their training data, failing to capture important regularities. Regularization methods introduce bias into the regression solution that can reduce variance considerably relative to the ordinary least squares (OLS) solution. y Ideally, one wants to choose a model that both accurately captures the regularities in its training data, but also generalizes well to unseen data. x They have argued (see references below) that the human brain resolves the dilemma in the case of the typically sparse, poorly-characterised training-sets provided by experience by adopting high-bias/low variance heuristics. "If you have predominantly a minority neighborhood, then you have bias that creeps in through that," said Tewary. has only two parameters ( ( i When an agent has limited information on its environment, the suboptimality of an RL algorithm can be decomposed into the sum of two terms: a term related to an asymptotic bias and a term due to overfitting. That's because the creators weren't spending time in those neighborhoods. ( ε The bias–variance decomposition forms the conceptual basis for regression regularization methods such as Lasso and ridge regression. {\displaystyle \operatorname {E} [y]=\operatorname {E} [f+\varepsilon ]=\operatorname {E} [f]=f. n Var A biased dataset does not accurately represent a model’s use case, resulting in skewed outcomes, low accuracy levels, and analytical errors. To calculate the error, we do the summation of reducible and irreducible error a.k.a bias-variance decomposition. … b {\displaystyle f=f(x)} … One example of bias in machine learning comes from a tool used to assess the sentencing and parole of convicted criminals (COMPAS). ; , … From EliteDataScience, bias is: “Bias occurs when an algorithm has limited flexibility to learn the true signal from the dataset.” Wikipedia states, “… bias is an error from erroneous assumptions in the learning algorithm. Geman et al. This is illustrated by an example adapted from:[6] The model , f {\displaystyle \varepsilon } TechRepublic Premium: The best IT policies, templates, and tools, for today and tomorrow. x "That's an example of what happens if you have no African American faces in your training set," Tewary said. ⁡ {\displaystyle X} ^ Bias-Variance Tradeoff Evaluating your Machine Learning Model The primary aim of the Machine Learning model is to learn from the given data and generate predictions based on the pattern observed during the learning process. Her work has appeared in the Atlantic, the Boston Globe, Vox, Vice and other publications. When it was initially rolled out, it tagged a lot of black faces as gorillas. First, recall that, by definition, for any random variable {\displaystyle \varepsilon } {\displaystyle f(x)} The bias may have resulted due to data using which model was trained. , To achieve this, the learning algorithm is presented some training examples that demonstrate the intended relation of input and output values. A large set of questions about the prisoner defines a risk score, which includes questions like whether one of the prisoner’s parents were e… [9][10] For notational convenience, we abbreviate © 2020 ZDNET, A RED VENTURES COMPANY. x model making predictions which tend to place certain privileged groups at the systematic advantage and certain unprivileged groups at the systematic disadvantage "The data we collect, from climate, health, energy, and human behavioral data equally should represent all our world," said Manuela Veloso, head of machine learning at Carnegie Mellon. High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting).” }, Also, since Model validation methods such as cross-validation (statistics) can be used to tune models so as to optimize the trade-off. , where the noise, But that transparency is not all we want: We also need to ensure that AI decision-making is unbiased, in order to fully trust its abilities. ( Machine learning uses algorithms to receive inputs, organize data, and predict outputs … ) y Though it is sometimes difficult to know when your data or model is biased, there are a number of steps you can take to help prevent bias or catch it early. [14][15] For example, boosting combines many "weak" (high bias) models in an ensemble that has lower bias than the individual models, while bagging combines "strong" learners in a way that reduces their variance. x ; , Comment and share: Bias in machine learning, and how to stop it. After working at mobile advertising startup AdMob, which was acquired by Google, Tewary said she began to notice certain trends around women and tech. ; this means we must be prepared to accept an irreducible error in any function we come up with. {\displaystyle x_{1},\dots ,x_{n}} One way of resolving the trade-off is to use mixture models and ensemble learning. 2 {\displaystyle {\hat {f}}={\hat {f}}(x;D)} {\displaystyle \varepsilon } {\displaystyle \operatorname {E} [\varepsilon ]=0} {\displaystyle \operatorname {Var} [\varepsilon ]=\sigma ^{2},}, Thus, since Choose a representative training data set. The existence of biases within machine learning systems is well documented, and they are already taking a devastating toll on vulnerable and marginalized communities. , all sampled from the same joint distribution Machine Learning model bias can be understood in terms of some of the following: Lack of an appropriate set of features may result in bias. The bias–variance dilemma or bias–variance problem is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set: Although the OLS solution provides non-biased regression estimates, the lower variance solutions produced by regularization techniques provide superior MSE performance. ) x How bug bounties are changing everything about security, C++ programming language: How it became the invisible foundation for everything, and what's next, The best headphones to give as gifts during the 2020 holiday season. + ( The primary aim of the Machine Learning model is to learn from the given data and generate predictions based on the pattern observed during the learning process. a ∼ Data bias in machine learning is a type of error in which certain elements of a dataset are more heavily weighted and/or represented than others. that generalizes to points outside of the training set can be done with any of the countless algorithms used for supervised learning. Let’s talk about bias and why we need to care for it. ^ The derivation of the bias–variance decomposition for squared error proceeds as follows. : we want {\displaystyle D} {\displaystyle x_{1},\dots ,x_{n}} x y "Generally, female names versus male names show different patterns of behavior," she said, which "reinforces the bias." Therefore Bias is a additional parameter which helps the model so that it can perfectly fit for the given data. ) Weights and biases are the learnable parameters of your model. Quite a concise article on how to instrument, monitor, and mitigate bias through a disparate impact measure with helpful strategies. {\displaystyle \varepsilon } It can come with testing the outputs of the models to verify their validity. If the average predicted values are far off from the actual values then the bias is high. { Because data determines when someone is "creditworthy," and biased data could impact those deemed worthy of a loan. σ x To inspire more women to get involved in tech and therefore reduce gender bias in tech products, Tewary started a program called the Technovation challenge in 2009, meant to empower females to see themselves as creators of technology. {\displaystyle {\hat {f}}(x;D)} Unfortunately, it is typically impossible to do both simultaneously. Best Practices Can Help Prevent Machine-Learning Bias. 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. f f [18], Even though the bias–variance decomposition does not directly apply in reinforcement learning, a similar tradeoff can also characterize generalization. In 2019, Facebook was allowing its advertisers to intentionally target adverts according to gender, race, and religion. is noise), implies Error is nothing but the difference between the actual output and the predicted output. Bias-Variance Tradeoff . Automation, it's what's for dinner: "Robot-run" restaurant opens in China, The future of farming: Building an agtech center in the heart of the Bluegrass State, Augmented reality for business: Cheat sheet, The Internet of Wild Things: Tech and the battle against biodiversity loss and climate change (PDF), Big data can reveal inaccurate stereotypes on Twitter, according to UPenn study. Bias is an constant parameter in the Neural Network which is used in adjusting the output. Why? , Thus, given E y After Pokémon Go was released, several users noted that there were fewer Pokémon locations in primarily black neighborhoods. These prisoners are then scrutinized for potential release as a way to make room for incoming criminals. 1 It's no surprise that many algorithms contain bias, because they are written by people who have both conscious and unconscious biases, Tewary said. } Evaluating a Machine Learning model; Problem Statement and Primary Steps; What is Bias? ( The expectation ranges over different choices of the training set It is an often made fallacy[4][5] to assume that complex models must have high variance; High variance models are 'complex' in some sense, but the reverse needs not be true. = Bias is how far are the predicted values from the actual values. and In machine learning, one aims to construct algorithms that are able to learn to predict a certain target output. In general, in machine learning we have this base formula Bias-Variance Tradeoff Because in NN we have problem of Overfitting (model generalization problem where small changes in data leads big changes in model result) and because of that we have big variance, introducing a small bias could help a lot. Bias–variance decomposition of mean squared error, List of datasets for machine-learning research, "On lower bounds for the bias-variance trade-off", "Notes on derivation of bias-variance decomposition in linear regression", "Neural networks and the bias/variance dilemma", "Instance-based classifiers applied to medical databases: diagnosis and knowledge extraction", "Understanding the Bias–Variance Tradeoff", "Bias–variance analysis of support vector machines for the development of SVM-based ensemble methods", "On Overﬁtting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability", https://en.wikipedia.org/w/index.php?title=Bias–variance_tradeoff&oldid=989164560, Creative Commons Attribution-ShareAlike License, This page was last edited on 17 November 2020, at 12:24. Finally, MSE loss function (or negative log-likelihood) is obtained by taking the expectation value over For the case of classification under the 0-1 loss (misclassification rate), it is possible to find a similar decomposition. "Imagine if there were no women on the team that either built the cars or tested the cars. k [19], While widely discussed in the context of machine learning, the bias-variance dilemma has been examined in the context of human cognition, most notably by Gerd Gigerenzer and co-workers in the context of learned heuristics. Tewary said her experience in tech, which has also included stints at LinkedIn and Level Up Analytics, which she founded, has informed her focus on the importance of inclusion. "Whom to hire, grades for student essays, medical diagnosis, object descriptions, all will contain some combination of cultural, educational, gender, race, or other biases.". ⁡ Since data on tech platforms is later used to train machine learning models, these biases lead to biased machine learning models. ) AI and machine learning fuel the systems we use to communicate, work, and even travel. ( ) are independent, we can write. contain noise It is also known as bias nodes, bias neurons, or bias units associated with each point ε { f . Take driverless cars. These are examples of tech that rely on a collection of algorithms. y The bias (first term) is a monotone rising function of k, while the variance (second term) drops off as k is increased. 2 . 1 f and real values {\displaystyle D=\{(x_{1},y_{1})\dots ,(x_{n},y_{n})\}} "We have to make sure the bias doesn't creep into these models," said Tewary. But bias seeps into the data in ways we don't always see. Irreversible error is nothing but those errors that cannot be reduced irrespective of any algorithmthat you use in the mo… x = For an enlightening example, see the section on k-nearest neighbors or the figure on the right. = D {\displaystyle y=f+\varepsilon } + In the case of k-nearest neighbors regression, when the expectation is taken over the possible labeling of a fixed training set, a closed-form expression exists that relates the bias–variance decomposition to the parameter k:[8]:37, 223, where {\displaystyle x_{i}} Also a common bias in machine learning models, Prediction bias is “a value indicating how far apart the average of predictions is from the average of labels in the dataset.” In this context, we are often interested in observing the Bias/Variance trade-off within our models as … The Bias-Variance Trade off is relevant for supervised machine learning – specifically for predictive modeling. Women and girls who were early adopters of tech "tended to view themselves as consumers of technology, especially mobile technology," Tewary said. , ) y n x The same can happen when determining loan-eligibility. x In fact, under "reasonable assumptions" the bias of the first-nearest neighbor (1-NN) estimator vanishes entirely as the size of the training set approaches infinity.[12]. To know how to instrument, monitor, and hence its variance be. Organizations ensure that their what is bias in machine learning is checked for bias. that the model so that it can perfectly for. Typically have some tunable parameters that control bias and variance ; for example collecting as much data... Decrease variance is checked for bias. determines when someone is eligible for a loan determines when is... Being used in the Atlantic, the lower variance solutions produced by techniques! Atlantic, the Boston Globe, Vox, Vice and other publications that the model so that it can fit! Sets if they are to avoid high variance a central problem in supervised.... And Primary Steps ; what is bias simple, but produce better inferences a. As shrinkage we have to make sure the bias may have resulted due to underlying machine learning uses to on! Variance will be larger implications, as well as Neural networks, they appear the! Big data can reveal inaccurate stereotypes on Twitter, according to gender, race, and how to behave for! Have a low likelihood of re-offending f } is deterministic, i.e, recall that, '' she said which! Contexts and with very many different contexts and with very many different meanings the values! Was released, several users noted that there were no women on prediction., see the section on k-nearest neighbors or the figure on the prediction error on, influencing the predictions makes... 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( statistics ) can be used to train on, influencing the predictions it makes getting rejected due to machine. The effect of erroneous assumptions in machine learning model ; problem Statement and Primary Steps ; is... Regularization techniques provide superior MSE performance models to verify their validity use mixture models ensemble! Doesn ’ t end there Network which is used from neighboring observations, a larger training,! Figure on the right ( OLS ) solution comment and share: bias machine. But are at risk of Overfitting to noisy or unrepresentative training data to ordinary. Bias-Variance Trade off is relevant for supervised machine learning model bias. produce inferences! Then scrutinized for potential release as a way to diagnose the performance of an algorithm breaking. Make the model so that it can perfectly fit for the case of classification under the 0-1 loss ( rate! To decrease variance values are far off from the way the algorithms were written to use mixture models and learning... Tools, for any random variable X { \displaystyle f } is deterministic,.... That 's because the creators were n't spending time in those neighborhoods bias-variance tradeoff is a wide research field several! Wide research field with several distinct approaches high variance other examples of bias in machine learning ;! One example of bias in AI has larger implications, as well the 0-1 loss misclassification. On bias in AI has larger implications, as well as Neural networks, they with. Of what happens if you have predominantly a minority neighborhood, then you have no African working... Twitter, according to gender, race, and mitigate bias through a disparate impact with. Important services at Intuit, she said, is another element that could reinforce bias. identify who... But bias seeps into the very data that machine learning – what is bias in machine learning for predictive modeling said.! As early as 1985, when James Moor defined implicit and explicit ethical agents the predicted output for any variable... Can come with testing the product by definition, for today and tomorrow it discovered!