Existing so-called conflict-based models of executive control aim to explain how an agent, without constantly controlling its own processing (what is both cognitively costly and inefficient), can know when to apply strong control, but when to withdraw it. These models predict that the strength of control is adjusted proportionally to the level of conflict among competing stimuli/response tenden...

While fairness-aware machine learning algorithms have been receiving increasing attention, the focus has on centralized learning, leaving decentralized methods underexplored. Federated Learning is a form of where clients train local models with server aggregating them to obtain shared global model. Data heterogeneity amongst common characteristic Learning, which may induce or exacerbate discrim...

Distributing the spoils of a joint enterprise on the basis of work contribution or relative productivity seems natural to the modern Western mind. But such notions of merit-based distributive justice may be culturally constructed norms that vary with the social and economic structure of a group. In the present research, we showed that children from three different cultures have very different i...

This paper investigates to what extent fair trade programmes, are indeed ‘fair’. This is accomplished by comparing fair trade with free trade and protectionist trade regimes on their compliance of the criteria set by the fair trade movement itself. This comparison is made using comparative cost based and economies of scale models. It is found that whether or not fair trade is superior to free t...

We study various classical secure computation problems in the context of fairness, and relate them with each other. We also systematically study fair sampling problems (i.e., inputless functionalities) and discover three levels of complexity for them. Our results include the following: – Fair exchange cannot be securely reduced to the problem of fair cointossing by an r-round protocol, except w...

Fairness in machine learning is getting rising attention as it directly related to real-world applications and social problems. Recent methods have been explored alleviate the discrimination between certain demographic groups that are characterized by sensitive attributes (such race, age, or gender). Some studies found data itself biased, so training on causes unfair decision making. Models tra...

The FAIRPoints organization, co-founded by the authors, aims to provide a platform for conversations take place around realistic and pragmatic implementations of FAIR (Findable, Accessible, Interoperable, Reusable) principles. uniqueness effort stems from an additional aim: capture conversation contributions in form “bite-sized” objects – “points” way that facilitates dynamic composition instru...