share, To address "bad actors" online, I argue for more specific definitions of... If we define bias as things that ‘produce outcomes that are not wanted’ [Suresh2019AFF], this list could of course be made considerably longer. Another example is a system that predicts crime rates in different parts of a city. ∙ The two common forms of biases that stem from the real-world and manifest in our datasets are reporting bias, and selection bias. The necessity of inductive bias in machine learning was mentioned in Section 3.1. 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. Barocas and Selbst [Barocas14] give as good overview of various kinds of biases in data generation and preparation for machine learning. [Wagner2015] present and apply several measures for assessing gender bias in Wikipedia. Since only 5% of Fortune 500 CEOs were women (2018), a search for ‘CEO’ resulted in images of mostly men. They join a coalition of 68 civil rights groups, hundreds of academics, more than 150,000 members of the public and Amazon’s own workers and shareholders. To identify this particular notion of bias, we propose using the term co-occurrence bias. Challenge your own ideas about AI development. 888 Several sub-types were also identified (see Section 3.3). Sometimes, labelling is not manual, and the annotations are read from the real world, such as manual decisions for real historical loan applications. Practitioners can have bias in their diagnostic or therapeutic decision making that might be circumvented if a computer algorithm could objectively synthesize and interpret the data in the medical record and offer clinical decision support to aid or guide diagnosis and treatment. We summarize our proposed taxonomy in Figure 1, with different types of biases organized in the three categories A biased world, Data generation, and Learning. More From Medium. Furthermore, the importance of causality in this context is widely recognized among ethicists and social choice theorists [Loftus18]. For example, the word claimed expresses an epistemological bias towards doubts, as compared to stated. social discrimination). Their claims of being bias free are based on internal evaluations.”, Maybe Amazon could use part of their $129 million tax rebate to work on fixing Rekognition. into the nature of false positives of bad code smells, Informed Machine Learning - Towards a Taxonomy of Explicit Integration Of the two industry-benchmark facial analysis datasets they tested, IJB-A and Adience, both are “overwhelmingly composed of lighter-skinned subjects (79.6% for IJB-A and 86.2% for Adience).”, “The Black Panther Scorecard” showing how different facial recognition systems perform on characters from Marvel’s Black Panther – Joy Buolamwini on Medium. Tell them to support stronger oversight of how artificial intelligence is trained and where it’s deployed. While most of the listed biases are specific for medicine and epidemiology, we identified the following fundamental types of measurement related bias that are highly relevant also for machine learning. In October this year, researchers uncovered a horrifying bias infecting an AI algorithm used by “almost every large health care system“. They found strong performance gaps between male and female faces. In several cases the meaning of terms differed between surveyed papers, and in some cases specific and important types of biases were only referred to as ‘bias’. Racial bias in machine learning and artificial intelligence Machine learning uses algorithms to receive inputs, organize data, and predict outputs within predetermined ranges and patterns. ∙ People have biases whether they realize it or not. identify a number of Natural Language Processing tasks that may cause such inherited bias: . And finally, lobby your government. Machine learning models are predictive engines that train on a large mass of data based on the past. Related article: How white engineers built racist code – and why it’s dangerous for black people – The Guardian. Is Bias in Machine Learning all Bad? However, there is of course also a possibility for the human annotators, to consciously or unconsciously, inject ‘kindness’ by approving loan applications by the same members ‘too often’. 0 Furthermore, even within machine learning, the term is used in very many different contexts and with very many different meanings. = Previous post. Bias exists and will be built into a model. Each xi, Without further restrictions, infinitely many functions perfectly match any given data set, but most of them are typically useless since they simply memorize the given data set but generalize poorly for other data from the same application. relation between bias occurring in the machine learning pipeline that leads to Fight back by staying vigilant and not getting carried away by the hype. Address "Bad Actors", Did JHotDraw Respect the Law of Good Style? Societal bias in AI is difficult to identify and trace. A quick note on relevance: searching Google News for “AI bias” or “machine learning bias” returns a combined 330,000 results. Many machine learning algorithms, in particular within deep learning, contain a large number of, . Bias control needs to be in the hands of someone who can differentiate between the right kind and wrong kind of bias. Bias and Fairness Part I: Bias in Data and Machine Learning. In the Biased world category, the main term is historical bias. In [Hardt16]. One example is given in [Torralba11], and is there denoted dataset bias, . In some cases, this may be a consciously chosen strategy to change societal imbalances, for example gender balance in certain occupations. Historical bias is the already existing bias and socio-technical issues in the world … For example, word embeddings may be transformed such that the distance between words describing occupations are equidistant between gender pairs such as ‘he’ and ‘she’ [BolukbasiEtAl2016]. Another approach to address biased models is to debias the data used to train the model, for example by removing biased parts, such as suggested for word embeddings [BrunetEtAl2019], by oversampling [geirhos2018imagenettrained], or by resampling [Li2019REPAIRRR]. Some people are even giving up and arguing that AI regulation may be impossible. Correcting the bias would raise that number to 46.5%. These implications of these findings are terrifying. According to the ACLU, “To conduct our test, we used the exact same facial recognition system that Amazon offers to the public, which anyone could use to scan for matches between images of faces. We identify five named types of historical bias. We propose the term specification bias to denote bias in the choices and specifications of what constitutes the input and output in a learning task, i.e. The bias of the world obviously has many dimensions, each one describing some unwanted aspect of the world. Societal AI bias is less-obvious, and even more insidious. With the possible exception of inductive bias, the various types of biases described in this paper are usually used with negative connotations - to describe unwanted behavior of a machine learning system. [Olteanu19] investigate bias and usage of data from a social science perspective. Such bias, which is sometimes called selection bias [campolo2018ai], or population bias [Olteanu19], may result in a classifier that performs bad in general, or bad for certain demographic groups. 333Reuters Technology News, Oct. 10, 2018. share, Despite the great successes of machine learning, it can have its limits ... This is not totally surprising since the conditions are related to common performance measures for classifiers, such as precision and recall, which are known to have the same contradictory relation. A possible reason could have been that the robot was trained with too few pictures of Asian men, and therefor made bad predictions on this demographic group. Olteanu et al. Reducing bias in AI begins with you. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. On August 15th, they announced that Rekognition can now detect fear. This leads to a biased assessment since poorly-performing funds are often removed or merged into other funds [Malkiel95]. In doing so, their actions reveal a societal bias towards assuming that men are better suited to these jobs. It occurs when the sampled data does not represent the population of interest, since some data items ‘died’. followed by an analysis and discussion on how different types of biases are Join one of the world's largest A.I. For a binary classification ˆY, and a binary protected group A, demographic parity is defined as follows: That is, ˆY should be independent of A, such that the classifier in average gives the same predictions to different groups. The authors of [ZhaoEtAl2017] show examples of this, and present techniques to detect and quantify bias related to correlations. This paper is organized as follows. In our survey we identified nine aspects of model bias, defined by statistical conditions that should hold for a model not being biased in a specific way. The most common loss function is defined as. And everyone needs to be more aware of societal biases, so we can look for it in our own work. AI regulation is lagging behind. We suggest the term inherited bias to refer to this type of bias. Section 4 contains a survey of various ways of defining bias in the model that is the outcome of the machine learning process. They come in a large variety of shades, and the Wikipedia page121212Wikipedia List of cognitive biases Aimed for Wikipedia editors writing on controversial topics, NPOV suggests to ‘(i) avoid stating opinions as facts, (ii) avoid stating seriously contested assertions as facts, (iii) avoid stating facts as opinions, (iv) prefer nonjudgemental language, and (v) indicate the relative prominence of opposing views’. This discrimination usually follows our own societal biases regarding race, gender, biological sex, nationality, or age (more on this later). The complexity is demonstrated by a 2014 study of Google Ads. Societal AI bias arises when an AI behaves in ways that reflect deep-rooted social intolerance or institutional discrimination. Hence, a measurement bias can occur either due to the used equipment, or due to human error or conscious bias. However, a more correct interpretation would be that the model is no more, or less, biased than the real world. Amazon realized their system had taught itself that male candidates were automatically better. We propose the term specification bias to denote bias in the specifications of what constitutes the input and output in a learning task (see Section 3.3.1), and we suggest the term inherited bias to refer to existing bias in previously computed inputs to a machine learning algorithm (see Section 3.3.5). Specific remarks concerning model bias are presented below. Unfortunately, correlations between observed entities can alone not be used to identify causal processes without further assumptions or additional information. The alternative would be to observe everything observable in the real world, which would make learning extremely hard, if not impossible. Bias and Variance in Machine Learning. A large body of research investigate bias properties of text, at sentence level, paragraph level, article level, or entire corpora such as Wikipedia news. Human bias when training data can wreak havoc on the accuracy of your machine learning model. To distinguish this from other types of bias discussed in this paper, we propose using the term model bias to refer to bias as it appears and is analyzed in the final model. Reporting bias in the context of machine learning refers to people's tendency to under report all of the available information, especially when it pertains to themselves. Darker-skinned females, for example, were misclassified up to 34.7% of the time, compared with a 0.8% error rate for lighter-skinned males. a model, and the eventual bias of the model (which is typically related to During this process, the annotators may transfer their prejudices to the data, and further to models trained with the data. In addition, several causal versions exist. Our survey of sources of bias is organized in sections corresponding to the major steps in the machine learning process (see Figure 1). And running the entire test cost us $12.33 — less than a large pizza.”. Each specific function in, This preference of certain functions over others was denoted bias by Tom Mitchell in his paper from 1980 with the title The Need for Biases in Learning Generalizations [Mitchell80], , and is a central concept in statistical learning theory. In these cases, the algorithms and data themselves may appear un-biased. lists more than 190 different types. As a first step, the data of interest has to be specified. In inductive learning, the aim is to use a data set {(xi,yi)}Ni=1 to find a function f∗(x) such that f∗(xi) approximates yi in a good way. In the following, possible sources of bias in each of these sub-steps will be surveyed. Just realize that bias is there and try to manage the process to minimize that bias. of false positives of bad code smells, Did JHotDraw respect the Law of Good Style? Loftus et al. It has been labeled biased against black defendants [Angwin16] The authors of  [ZhaoEtAl2017] show that in a certain data set, the label cooking co-occurs unproportionally often with woman, as compared to man. The EU’s General Data Protection Regulation (GDPR) set a new standard for regulation of data privacy and fair usage. 10/04/2020 ∙ by Simon Caton, et al. Happens as a result of cultural influences or stereotypes. If the model is going to be used to predict ‘the world as it is’, model bias may not be problem. In the case of categorical features and output, discrete classes related to both x and y, for example ‘low’, ‘medium’, and ‘high’. 222BBC News, Oct. 10, 2018. While this, at first, may not be seen as a case of social discrimination, an owner of a snowmobile shop may feel discriminated against if Google does not even find the shop’s products when searching for ‘snowmobiles’. Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data. For example, a classifier is biased with respect to FDR if the value of A affects the probability of incorrectly being allowed to borrow money. The null hypothesis is that there is no difference between the two sets of target words in terms of their relative similarity to the two sets of attribute words. model making predictions which tend to place certain privileged groups at the systematic advantage and certain unprivileged groups at the systematic disadvantage Lexalytics®, Semantria®, and the Lexalytics "Y" logo are registered trademarks of Lexalytics, Inc. Noah, wizardly wordsmith and editor extraordinaire, is an expert at turning complex technology into clear, compelling content. We view causal reasoning as critical in future work to identify and reduce bias in machine learning systems. And they’ve pitched Rekognition to Immigrations and Customs Enforcement (ICE), sparking mass protests. Bias Isn’t the Problem. Cognitive biases are systematic, usually undesirable, patterns in human judgment and are studied in psychology and behavioral economics. The main contribution of this paper is a proposed taxonomy of the various meanings of the term bias in conjunction with machine learning. As machine learning projects get more complex, with subtle variants to identify, it becomes crucial to have training data that is human-annotated in a completely unbiased way. share. Bias and Variance in Machine Learning e-book: Learning Machine Learning The risk in following ML models is they could be based on false assumptions and skewed by noise and outliers. Accessed Jan. 26, 2020. modifications to promote a clear terminology and completeness. And then they benchmarked these résumés against current engineering employees. is used in conjunction with machine learning in many different contexts, and Machine bias is the growing body of research around the ways in which algorithms exhibit the bias of their creators or their input data. For example, the fact that a person is female (A=0) should not increase or decrease the risk of incorrectly being refused, or allowed, to borrow money at the bank. 04/01/2020 ∙ by Thomas Hellström, et al. To identify unwanted correlations, a bias score for o, with respect to a demographic variable g∈G, is defined as. A biased dataset does not accurately represent a model’s use case, resulting in skewed outcomes, low accuracy levels, and analytical errors. 02/14/2020 ∙ by Daniel Speicher, et al. Humans are products of their experiences, environments, and educations. As noted in [Loftus18], this may require positive discrimination, where individuals having different protected attributes are treated very differently. The same holds at the level of human learning, as discussed in the area of philosophical hermeneutics [Hildebrandt19]. The difference between features such as ‘income’ and ‘ethnicity’ has to do with the, already cited, normative meaning of the word bias expressed as ‘an identified causal process which is deemed unfair by society’ [campolo2018ai]. In some cases, we suggest extensions and The word ‘bias’ has an established normative meaning in legal language, where it refers to ‘judgement based on preconceived notions or prejudices, as opposed to the impartial evaluation of facts’ [campolo2018ai]. Artificial intelligence is already at work in healthcare, finance, insurance, and law enforcement. kind is denoted self-selection bias [OnlineStat] and can be exemplified with an online survey about computer use. Given this complex situation, one should view the different aspects of model bias as dimensions of a multi dimensional concept. This threshold is usually manually set, and may create a bias against underrepresented demographic groups, since less data normally leads to higher uncertainty. In this paper, I take a Historical Bias. Gender Shades, a project that spun out from an academic thesis, takes “an intersectional approach to product testing for AI.” In their original study, the University of Toronto’s Inioluwa Deborah Raji and MIT’s Joy Buolamwini tested demos of facial recognition technology from two major US tech giants, Microsoft and IBM, and a Chinese AI company, Face++.
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