We examine whether these static fairness constraints mitigate or worsen the qualification disparity in the long-run. Our work can be applied to a variety of applications such as recruitment and bank lending. In these applications, aninstitute observesindividuals' features (e.g., credit scores), and makes myopic decisions(e.g., issue loans) by assessing such features against some variables of interest (e.g., ability torepay) which are unknown and unobservable tothe institute when making decisions.

This delayed impact of actions is prevalent in the real world.

Our theoretical results and experiments on static real-world datasets with simulated dynamics show that our framework can be used to facilitate social science studies.

Innovation,

Researchers from Massey University, the University of Tasmania and Stetson University reviewed multiple long-term studies into video games and aggression. They found no evidence of a substantial link between'aggressive game content' and signs of anger or rage later on in childhood. 'Poor quality studies' in the past likely exaggerated the impact of games on aggression, while better quality studies show the effects of gaming are'negligible'. Regulation of violent games also did not appear likely to reduce aggression in real life, suggesting parents shouldn't worry about their kids shooting up virtual enemies. Real-life displays of violence, such as mass shootings in the US, have famously been blamed on video games by some politicians, rather than lax gun regulation and easy access to firearms.

Ford said Tuesday it will delay until 2022 plans to launch an autonomous vehicle service, as the COVID-19 pandemic has prompted the company to rethink its go-to-market strategy. The news was shared as part of Ford's quarterly earnings, which was released after the market closed Tuesday. Ford reported a $2 billion loss in the first quarter compared to a profit of $1.1 billion in the same period last year. The company warned that losses during the second quarter will widen as the COVID-19 pandemic continues to disrupt its business. Ford is a bit different from other companies that have launched autonomous vehicle pilots in the United States.

By Rachel Thomas, Co-founder at fast.ai Goodhart's Law states that "When a measure becomes a target, it ceases to be a good measure." At their heart, what most current AI approaches do is to optimize metrics. The practice of optimizing metrics is not new nor unique to AI, yet AI can be particularly efficient (even too efficient!) This is important to understand, because any risks of optimizing metrics are heightened by AI.

Goodhart's Law states that "When a measure becomes a target, it ceases to be a good measure." At their heart, what most current AI approaches do is to optimize metrics. The practice of optimizing metrics is not new nor unique to AI, yet AI can be particularly efficient (even too efficient!) This is important to understand, because any risks of optimizing metrics are heightened by AI. While metrics can be useful in their proper place, there are harms when they are unthinkingly applied.

Most existing notions of algorithmic fairness are one-shot: they ensure some form of allocative equality at the time of decision making, but do not account for the adverse impact of the algorithmic decisions today on the long-term welfare and prosperity of certain segments of the population. We take a broader perspective on algorithmic fairness. We propose an effort-based measure of fairness and present a data-driven framework for characterizing the long-term impact of algorithmic policies on reshaping the underlying population. Motivated by the psychological literature on social learning and the economic literature on equality of opportunity, we propose a micro-scale model of how individuals respond to decision making algorithms. We employ existing measures of segregation from sociology and economics to quantify the resulting macro-scale population-level change. Importantly, we observe that different models may shift the group-conditional distribution of qualifications in different directions. Our findings raise a number of important questions regarding the formalization of fairness for decision-making models.