Abstract There is much debate about the ways in which artificial intelligence (AI) systems can include and perpetuate biases lead to unfair often illegal discrimination against individuals on basis of protected characteristics, such as age, race, gender disability. This chapter describes three cases discrimination. It starts with an account use AI hiring decisions that led based gender. The sec...

We study the decision of when to sue in a game in which multiple plaintiffs have similar cases against a single defendant. Two legal regimes are considered. In one, prior results are binding in all future cases. In contrast, under the prevalent asymmetric regime, adverse results are binding against the defendant in future actions, but a judgement adverse to one plaintiff will not be binding aga...

To evaluate the sensitivity and specificity of Community Health Survey (CHS), we analyzed data from 11,217 participants aged ≥ 19 yr, in 13 cities and counties in 2008. Three healthcare utilization indices (admission, outpatient visits, dental visits) as comparative variables and the insurance benefit claim data of the Health Insurance Review & Assessment Service as the gold-standard were used....

Submitted: Jun 12, 2013; Accepted: Jul 23, 2013; Published: Jul 25, 2013 Abstract: Companies around the world normally assign a portion of their benefits to their employees. This policy is used as an encouragement to increase the motivation of employees. If the profit sharing seems unfair, its expected results will be lost. In this paper, a model is developed to determine a desirable profit sha...

This study aims to analyze the community movements of female fishermen, obtain justice and fight discrimination for equal citizenship. Issuance Indonesian Law concerning protection fishermen as a formal law protect does not fully target groups. One in coastal communities, which is joined by organization, was trying request an adjustment occupation identity written National Identification Card (...

Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context of fairness-aware learning and demonstrate various attractive properties: (i...