Equality of opportunity (EOP) is an extensively studied conception of fairness in political philosophy. In this work, we map recently proposed notions of algorithmic fairness to economic models of EOP. We formally show that through our proposed mapping, many existing definition of algorithmic fairness, such as predictive value parity and equality of odds, can be interpreted as special cases of EOP. In this respect, our work serves as a unifying moral framework for understanding existing notions of algorithmic fairness. Most importantly, this framework allows us to explicitly spell out the moral assumptions underlying each notion of fairness, and also interpret recent fairness impossibility results in a new light. Last but not least and inspired by luck egalitarian models of EOP, we propose a new, more general family of measures for algorithmic fairness. We empirically show that employing a measure of algorithmic (un)fairness when its underlying moral assumptions are not satisfied, can have devastating consequences on the subjects' welfare.

There are few industries artificial intelligence (AI) will not touch, but the security industry has only begun to scratch the surface of its potential. While unsure of the future applications of AI in the security world, Kurt Roemer, chief security strategist at Citrix, believes that AI needs to play a large role to keep on top of the issues that keep presenting themselves as a threat to the security industry. "AI is going to have a huge role in security and it ultimately has to because the security problem is extremely complex and it's much more than an individual or even a well established security team can keep up with," Roemer told ZDNet. "What AI can bring to bear is providing security professionals with actionable intelligence and with areas where they only need to make decisions," he said. "Workflows happen automatically and security according to plan happens in the background at all points in time without the humans in the loop."