algorithmic bias examples

AI advances important goals, such as efficiency, health and economic growth but it can also have discriminatory effects, for instance when AI systems learn from biased human decisions. Non-response bias: Respondents who refuse to take part in studies and drop-out of research cause non-response bias. Algorithmic Bias Examples. Here are 5 examples of bias in AI: In 2018, Reuters reported that Amazon had been working on an AI recruiting system designed to streamline the recruitment process by reading resumes and selecting the best-qualified candidate. Researchers have found that facial analysis technologies had higher error rates for faces of color which is mainly due to unrepresentative training data. Sometimes this is obvious, for example when we compare sending somebody to prison as opposed to declining an increase in their credit card limit, but sometimes there may be more subtle discrimination, e.g. Algorithmic bias describes systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others. The algorithms developed in these areas can now help evaluate loan applications, predict a defendant's  likelihood of re-offending, or help clinicians determine what type of brain cancer their patient might be suffering from. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. Machine learning is highly susceptible to many forms of bias that can undermine model performance. Found insideThey have designed into them certain assumptions about the world and about people and what they should be like. Much has been written about 'algorithmic biases'. One famous example of a 'biased' algorithm was Google's image recognition ... While there are many real and potential benefits of using AI, a flawed decision-making process caused by Human bias embedded in AI output makes this a big concern for its real-world implementation. As more organizations explore the benefits of using artificial intelligence, there will inevitably be more real-world examples of AI bias. Many companies now use AI systems to perform tasks and sort through data that formerly would have been assigned to human workers. Algorithmic bias describes systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others. The temptation with examples of algorithmic bias, as in the case of the UK exams scandal, is that when they are exposed, the brakes are slammed on and a policy u-turn takes place, a practice that doesn’t tackle the underlying problem. However, these systems then proceeded to make bolder claims like they will soon be able to measure the intelligence, political orientation, and criminal inclinations of people from their facial images alone. Society has not been able to reconcile different views on this, and we cannot expect machines, no matter how "smart" they are, to do this for us. In other instances, such as with Giggle, the problem was a broader one; whether or not AI is at all suited to the task in question, of if other means altogether would be more appropriate. Its breakthrough computer vision and machine learning technology analyzes facial images and automatically reveals personalities in real-time. Advances in computer hardware have led to an increased ability to process, store and transmit data. These are examples of noise: variability in judgments that should be identical. These facial Analysis systems came out to be very accurate but biased. Bias in Algorithmic Decision making in Financial Services Barclays Response Barclays is a transatlantic consumer and wholesale bank with global reach, offering products and services across personal, corporate and investment banking, credit cards and wealth management, with a strong presence in our two home markets of the UK and the US. This gives the impression that clean data and good intentions could eliminate bias in machine learning. While AI can be a helpful tool to increase productivity and reduce the need for people to perform repetitive tasks, there are many examples of algorithms causing problems by replicating the (often unconscious) biases of the engineers who built and operate them. Students will examine examples of algorithmic bias in their own lives, the lives of others and within societal institutions. Job adverts for roles in nursing or secretarial work were suggested primarily to women, whereas job ads for janitors and taxi drivers had been shown to a higher number of men, in particular men from minority backgrounds. Such methods have been used by companies that receive a lot of applications throughout. Instead, we’ve gathered a few that best highlight two of its most glaring realities. Advertising tools used by big platforms like Facebook optimizes its decision based on the historical preferences of the people. Found inside – Page 151The first type of algorithmic bias is the sample selection bias, occurring when the training data over-represents a certain population. One of the most striking examples of this kind of bias is the malfunction of most of the existing ... 6. AI is also having an impact on democracy and … . AI bias is caused by bias in data sets, people designing AI models and those interpreting its results. In White’s presentation on algorithmic bias, he uses simple yet powerful examples of how algorithmic bias impacts our everyday lives and can reinforce existing societal stereotypes. Read More , BBC News, the guardian, The guardian. Over the next two sections, we will use the same framework to look at real-world examples of algorithmic bias. As a result of this model, some areas have excessive patrolling and some areas do not have it at all. She then further classifies algorithmic biases into harms of allocation and harms of representation. Since then, The ImageNet team has analyzed its dataset and tried to identify the sources of bias. Experts are agreed that de-biasing needs to take place on both the technical and social levels. For example, when predicting reoffending rates, one measure of fairness could be how similar the accuracy is across different protected characteristics. Police use algorithms to analyze data and predict where crimes might occur in the future. However, there is a growing concern that algorithms themselves may result in biased outcomes and recommendations, either because the data used to train them may reflect historical biases, or because they may detect patterns that we would consider discriminatory, for example by associating low income with higher crime rates, which might lead to biased outcomes for certain ethnic groups that earn less. “Bias can creep into the process anywhere in creating algorithms: from the very beginning with study design and data collection, data entry and cleaning, algorithm and model choice, and implementation and dissemination of the results.” What Can Minimize Large-Scale Algorithmic Bias and Effectively Harness the Power of AI? This tutorial provides the ICDE community with recent advances on the assessment and mitigation of data and algorithmic bias in personalized rankings.We first introduce conceptual foundations, by surveying the state of the art and describing real-world examples of how bias can impact ranking algorithms from several perspectives (e.g., ethics and system's objectives). Found inside – Page 919In some cases (as will be discussed below), the bias is not in the algorithm per se but rather in the data which it uses. Unfortunately, examples of algorithmic bias are not difficult to find. A study found that names associated with ... This occurs when algorithms reflect the implicit values of the humans involved in their creation or use, systematically “replicating or even amplifying human biases, particularly those affecting protected groups” (Lee et al.). Found inside – Page 77ProPublica (n.d.) monitors examples of “machine bias” in order to serve the public interest. ... These documented examples have motivated questions about algorithmic accountability. When algorithms are “black boxes” and decisions made ... Found inside – Page 171Algorithmic. bias. Algorithms are at the core of many data science models (see Chapter 11 for a comprehensive introducion. ... As an example, some groups may be underrepresented or systematically excluded from data collection efforts. Throughout our work on algorithmic bias, though, we’ve found that a second categor y is far more common: algorithms are aimed at the wrong target to begin with. We keep stumbling across examples of discrimination in algorithms, but that’s far better than their remaining hidden. Example of survivorship bias: Studying business performances in a certain industry may not take into account organizations that have failed and cease to exist now. Training vs Validation Usage: Protected Attributes should not be used in the training of Who should be involved and what values are implicated? In Value Sensitive Design, Batya Friedman and David Hendry describe how both moral and technical imagination can be brought to bear on the design of technology. Algorithmic bias. Three filters are of prime importance. This is problematic since machine learning - as a novel programming paradigm in which a mapping between input and output is inferred from data - poses a variety of open research questions regarding users' understanding. E orts to shine a light on algorithmic bias tend to focus on examples where either the data or the people building the algorithms are biased. While sometimes the effects of algorithmic bias are trivial (such as our social media feeds being anchored in puppy videos because the very first post you ever clicked on was one), at other times they can wreak havoc on a person’s life. What are the three algorithm constructs? This essay will use the taxonomy of algorithmic biascreated by Danks and London (2017) to differentiate between the various types of algorithmic bias and give examples of In the public and the private sector, organisations can take AI-driven decisions with far-reaching effects for people. But are we doing that? Current Examples of Algorithmic Bias In 1950 the British mathematician Alan Turing famously asked whether machines could think and propose… Apart from the obvious potential cost savings and the prospect of relieving employees from some of their more mundane daily tasks, many organisations hope that using such algorithms will allow them to overcome the biases inherent to human decision making, which are well-documented in areas such as credit scoring, criminal justice, and recruitment. When she wore a white mask, Buolamwini found that she was better recognized by a robot.¹ This is an unfortunate example of interaction bias, where an algorithm or computer system is … ***The Intent of this blog is just to show the importance of understanding Bias in Artificial Intelligence***. This is an example of bias in AI. However, a recent study shows that one such commonly used algorithm has significant racial bias. The views expressed are those … AI Solutions in Assisted Living — What Can Be Done? Very soon it started reflecting the bias in its comments and tweets. A video currently doing the rounds on social media features Officer Greg Anderson of the Port of Seattle Police’s Marine Patrol Unit giving his take on the enforcement of “tyrannical” measures during the coronavirus lockdown. Found inside – Page 179As I noted , several of the algorithmic bias examples I described might have been eased or resolved by greater transparency . In those cases , the lack of transparency was related to commercial advantage ; the companies involved didn't ... 3. But, of course, this bias isn’t really due to the AI itself, but to the people who program it. Found inside – Page 67Consider the example of word embeddings . Word embedding is a natural languageprocessing technique used to translate words into mathematical representations . It is also a popular example of algorithmic bias . Machine learning algorithms and artificial intelligence influence many aspects of life today and have gained an aura of objectivity and infallibility. Michael Rovatos is Professor of Artificial Intelligence at the University of Edinburgh, where he also heads up the Bayes Centre for Data Science and AI. Found inside – Page 135Two examples will illustrate the kinds of concerns raised. ... further than necessary and many decision rules, machine learning approaches and trained algorithms may be involved. ... A second example is so-called 'algorithmic bias'. Bias can creep into algorithms in several ways and can be introduced during each stage of the AI process. Unfortunately, the algorithm reinforced the biases of hiring for male-dominated roles like software engineering. As of April 2020 the app still hasn’t been officially launched. With over 325 years of history and expertise … A lot of hiring programs by big companies use AI for reviewing job applications and selecting the top talent. Students will examine examples of algorithmic bias in their own lives, the lives of others and within societal institutions. Throughout the world, artificial intelligence (AI) is increasingly being used to help make decisions about healthcare, loans, criminal sentences and job applications. Furthermore,  data protection and privacy law may hinder attempts to mitigate bias effectively in some cases. Found inside – Page 63A major source for algorithmic bias is a choice of an overly short time period for the sample to span. For example, it is a stability bias to assume that the past year is representative for the future. Testing an algorithm over multiple ... As you probably understand by now, examples of algorithmic bias are manifold. In the Netflix series “ Coded Bias,” Joy Buolamwini, an activist and computer scientist based at MIT, discusses how algorithms used for facial recognition can be biased. Researchers reviewed more than 50,000 records and have found a very significant flaw in these models which gives higher risk scores to white people and low scores to people of color. It is used to predict the likeliness of a criminal reoffending; acting as a guide when criminals are being sentenced. Another reason is that if the model is trained on Americans, it would have a lower performance on Asians and Africans and vice-versa as some medical conditions are more common in certain groups of people than others. We all have an unconscious prejudice … The Allegheny Family Screening Tool is a model designed to assist humans in deciding whether a child should be removed from their family because of abusive circumstances. Sometimes artificial intelligence feeds back to heighten human … Found insideAlgorithmic Bias Definition of Algorithmic Bias Algorithmic bias is among the most notable challenges facing AI and ML systems. Several definitions of algorithmic bias exist in the literature. Among them, we like this straightforward ... Understanding algorithmic decision-making: Opportunities and challenges . ISG Director, Wayne Butterfield AI Ethics: Dealing with Algorithmic Bias As more organizations explore the benefits of using artificial intelligence, there will inevitably be more real-world examples of AI bias. What kinds of problems are difficult to solve using algorithms? Algorithmic bias in higher education There are many well-documented examples of corporations recognizing and struggling with the potential harm caused by algorithmic bias. There is clear evidence of algorithmic bias in AI tools. Similar countermeasures can protect against algorithmic bias. The main concern here is if companies start using such systems that show bias on human faces, it becomes a matter of concern. The AI also automatically excluded many trans girls, meaning that if they wanted to use the app they would have to contact the makers directly to have their gender verified, which in itself entails ethical conundrums and raised questions about how sensitive the developers were to the real life application of their software. AI reasons from statistical correlations across data sets, while common sense is based heavily on conjecture. Erik Larson argues that hyping existing methods will only hold us back from developing truly humanlike AI. https://openinnovation.blog.gov.uk/2019/08/12/algorithms-the-good-the-bad-and-the-biased/. Found inside – Page 65To be more specific, algorithmic bias identifies systematic and repeatable errors that result in unequal results, such as the privilege of one arbitrary group of users over the others. For example, a credit score algorithm can refuse a ... These people may find themselves clicking on these types of ads without knowing that other social groups are shown better offers, thus, scaling the existing bias. Take for example, the well-documented racial biases among employers, less likely to call back workers with more more typically black names than … This is exactly where the feedback loop is amplifying the bias. This can be supportive of good decision making, reduce human error and combat existing systemic biases. Found inside – Page 58A notable example of AI bias in the law enforcement arena is the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) algorithm to predict recidivism (propensity for a convicted criminal to re-offend). any potential bias in data or design of algorithms. But there is also algorithmic bias – a distortion that can be caused by an AI system's technical design. Its important here to understand that arrests are different from a crime. algorithmic bias refers to the discrimination against different demo-graphics generated by computer algorithms. Found insideFor example, in the USA, a recidivism estimation algorithm, COMPAS, was used in the criminal justice system for parole hearings. The COMPAS system was shown to give systematically biased results. This bias, compounded by the misguided ... _______________ ‘One of the best books yet written on data and algorithms. . .deserves a place on the bestseller charts.’ (The Times) You are accused of a crime. Algorithmic Bias. TikTok's recommendation system can create a biased "feedback loop," this researcher said. data on which the algorithms are trained. These models have been showing 90% accuracy. Found inside – Page 183The problem of algorithmic bias has received considerable attention recently, due to the increasing deployment of automated ... In this section, we illustrate the operation of digits on a very simple example inspired by algorithmic bias ... The Open Innovation Team is a Moving beyond “algorithmic bias is a data problem” In the absence of intentional interventions, a trained machine learning model can and does amplify undesirable biases in the training data. Although ensuring unbiased outcomes is useful to attest whether a specific algorithm … A health care risk-prediction algorithm that is used on more than 200 million U.S. citizens, demonstrated racial bias because it relied on a faulty metric for determining the need.