## 1 Introduction  
Decision-making by algorithms is becoming a ubiquitous part of our societal and economic lives. Algorithmic decisions increasingly appear in a plethora of domains such as healthcare, legal, education, banking, e-commerce, etc. In healthcare, for example, algorithms are being used to routinely monitor biochemical signals in patients, and immediately alert clinicians when anomalies arise [1]. Deep learning algorithms are able to process anonymized electronic health records and flag potential emergencies, to which clinicians are then promptly able to respond. Similarly, in US courts, an algorithmic system known as COMPASS is used to estimate the risk of recidivism. Human Resource departments in various companies are increasingly resorting to algorithms that are able to filter from the initial set of potential applications to reduce human time and effort in the evaluation of applications [2]. Similarly, universities and colleges have begun using algorithmic predictions on big data to estimate which students will do well before accepting their admission applications [3]. With banks moving towards mobile payments to offer a seamless and fast customer experience, payment services based on machine learning algorithms verify and identify credit fraud in real-time. Similarly, insurance companies use automated data credibility assessment methods to quickly perform complex rounds of approval, verification, and evaluation so as to flag duplicate or otherwise unusual activities. Online retailers such as Amazon and Alibaba routinely deploy recommender systems algorithms in order to filter the set of product items that are displayed on the users dashboard. It is becoming evident that (with or without our desire) algorithmic decisions leave their footprints in our day to day activities from the way we do grocery shopping to the way we do banking. This increasing application and deployment of algorithmic decision-making in economy and society are driven by their high accuracy, effectiveness, low cost, and efficiency. Acceleration in the adoption of algorithmic decision-making is further supported by the access to mass volumes of data that is being currently collected in the digital economy as well as advancement in development of hardware such as General Processing Units (GPUs) and Tensor Precessing Units (TPUs). In addition to the notable benefits and growing prevalence of algorithmic decisions, we are also witnessing growing concerns and skepticism in academia and popular media with respect to algorithmic unfairness and the evidences that they may inadvertently discriminate against certain minority groups. Evidence has shown that algorithmic decisions not only counteract and expose biases but also afford new mechanisms for introducing biases with unintended and detrimental effects [4]. Specifically, algorithmic decisions have been shown to amplify biases and unfairness embedded in data in terms of sensitive features such as gender, culture, race, etc. For example, in their recent study, [PERSON], [PERSON], and [PERSON] [5] found that natural language processing algorithms do capture historic discrimination against gender, such as by more closely associating words like \"doctor\" with males and \"nurse\" with females. As such algorithms are trained on historical data, past discrimination and stereotypes prevalant in the society are reflected in their predictions. These concerns become particularly alarming when algorithmic decisions are interacting and influencing almost every aspect of economic and social life of groups and individuals. As an example, consider the work of [PERSON] et al. [6], who found that COMPASS is biased against African-American defendants. As the tools' error rates were asymmetric, African-American defendants were more vulnerable to be incorrectly labeled as higher-risk than they actually were when compared to their white defendants.