With the widespread use of AI systems and applications in our everyday lives, it is important to take fairness issues into consideration while designing and engineering these types of systems. Such systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that the decisions do not reflect discriminatory behavior toward certain groups or populations. We have recently seen work in machine learning, natural language processing, and deep learning that addresses such challenges in different subdomains. With the commercialization of these systems, researchers are becoming aware of the biases that these applications can contain and have attempted to address them. In this survey we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined in order to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and how they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

A Survey on Bias and Fairness in Machine Learning

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

https://repository.tudelft.nl/islandora/object/uuid%3A7bdae86a-0209-4e53-83d0-c531dd3465ec/datastream/OBJ/download

Artificial Intelligence (AI) : Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Economics Of Discrimination

Utility Analysis and the Consumption Function: An Interpretation of Cross-Section Data

We explore data from a field test of how an algorithm delivered ads promoting job opportunities in the science, technology, engineering and math fields. This ad was explicitly intended to be gender...

/pdf/algorithmic-bias-an-empirical-study-of-apparent-gender-based-3dw2kc94j0.pdf

Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads

Firms can now offer personalized recommendations to consumers who return to their website, using consumers' previous browsing history on that website. In addition, online advertising has greatly improved in its use of external browsing data to target Internet ads. Dynamic retargeting integrates these two advances by using information from the browsing history on the firm's website to improve advertising content on external websites. When surfing the Internet, consumers who previously viewed products on the firm's website are shown ads with images of those same products. To examine whether this is more effective than simply showing generic brand ads, the authors use data from a field experiment conducted by an online travel firm. Surprisingly, the data suggest that dynamic retargeted ads are, on average, less effective than their generic equivalents. However, when consumers exhibit browsing behavior that suggests their product preferences have evolved (e.g., visiting review websites), dynamic retargeted ad...

/pdf/when-does-retargeting-work-information-specificity-in-online-4pgz306rlu.pdf

When Does Retargeting Work? Information Specificity in Online Advertising

A common assumption underlying the analysis of consumers' choices among optional tariffs is that consumers choose the tariff that maximizes their surplus and, thus, the tariff that leads to the lowest billing rate for a given amount of usage.Yet there is evidence that many users prefer a flat rate even though their billing rate would be lower with a pay-per-use tariff (flat-rate bias), and some users prefer a pay-per-use tariff even though they would save money with a flat rate (pay-per-use bias). The authors conduct four empirical analyses based on three different data sets. They show that the flat-rate bias is more important and has a greater regularity and time persistence than the pay-per-use bias. They classify potential causes of the flat-rate bias as “insurance effect,” “taxi meter effect,” “convenience effect,” and “overestimation effect” and show that the insurance, the taxi meter, and the overestimation effects lead to a flat-rate bias. They provide evidence that underestimation of usag...

Paying Too Much and Being Happy About It: Existence, Causes, and Consequences of Tariff-Choice Biases

In communication, information, and other industries, three-part tariffs are increasingly popular A three-part tariff is defined by an access price, an allowance, and a marginal price for any usage in excess of the allowance Empirical nonlinear pricing studies have focused on consumer choice under two-part tariffs We show that consumer behavior differs under three-part tariffs and assess how consumer demand uncertainty impacts tariff choice We develop a discrete/continuous model of choice among three-part tariffs and estimate it using consumer-level data on Internet usage Our model extends prior work in accommodating consumer switching to competitors, thereby capturing behavior in competitive industries more accurately Our empirical work shows that demand uncertainty is a key driver of choice among three-part tariffs Consumers' expected bill increases with the variation in their usage, steering them toward tariffs with high allowances Consequently, demand uncertainty decreases consumer surplus and increases provider revenue A further analysis of consumers' responsiveness to the different elements of a three-part tariff under the provider's current pricing structure reveals that prices affect a consumer's tariff choice more than her usage quantity and that the allowance plays a strong role in consumer tariff choice Based on our results, we derive implications for pricing with three-part tariffs

/pdf/does-uncertainty-matter-consumer-behavior-under-three-part-47jjgp6pdu.pdf

Does Uncertainty Matter? Consumer Behavior Under Three-Part Tariffs

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Papers

Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads

When Does Retargeting Work? Information Specificity in Online Advertising

Paying Too Much and Being Happy About It: Existence, Causes, and Consequences of Tariff-Choice Biases

When Does Retargeting Work? Information Specificity in Online Advertising

Does Uncertainty Matter? Consumer Behavior Under Three-Part Tariffs