Artificial Intelligence (AI) has been used extensively in automatic decision making in a broad variety of scenarios, ranging from credit ratings for loans to recommendations of movies. Traditional design guidelines for AI models focus essentially on accuracy maximization, but recent work has shown that economically irrational and socially unacceptable scenarios of discrimination and unfairness are likely to arise unless these issues are explicitly addressed. This undesirable behavior has several possible sources, such as biased datasets used for training that may not be detected in black-box models. After pointing out connections between such bias of AI and the problem of induction, we focus on Popper's contributions after Hume's, which offer a logical theory of preferences. An AI model can be preferred over others on purely rational grounds after one or more attempts at refutation based on accuracy and fairness. Inspired by such epistemological principles, this paper proposes a structured approach to mitigate discrimination and unfairness caused by bias in AI systems. In the proposed computational framework, models are selected and enhanced after attempts at refutation. To illustrate our discussion, we focus on hiring decision scenarios where an AI system filters in which job applicants should go to the interview phase.

Subjective questions such as 'does neymar dive', or 'is clinton lying', or 'is trump a fascist', are popular queries to web search engines, as can be seen by autocompletion suggestions on Google, Yahoo and Bing. In the era of cognitive computing, beyond search, they could be handled as hypotheses issued for evaluation. Our vision is to leverage on unstructured data and metadata of the rich user-generated multimedia that is often shared as material evidence in favor or against hypotheses in social media platforms. In this paper we present two preliminary experiments along those lines and discuss challenges for a cognitive computing system that collects material evidence from user-generated multimedia towards aggregating it into some form of collective decision on the hypothesis.

Computational Simulations are important tools that enable scientists to study complex phenomena about which few data is available or that require dangerous human interventions. They involve complex and heterogeneous components, including: mathematical equations, hypothesis, computational models and data. In order to support in-silico scientific research this complex environment needs to be modeled and have its data and metadata managed enabling model evolution, prediction analysis and decision-making. This paper proposes a scientific hypothesis conceptual model that allows scientists to represent the phenomenon been investigated, the hypotheses formulated in the attempt to explain it, and provides the ability to store results of experiment simulations with their corresponding provenance metadata. The proposed model supports scientific life-cycle through: provenance management, exchange of hypothesis as data, experiment reproducibility, model steering and simulation result analyses. A cardiovascular numerical simulation illustrates the applicability of the model and an initial implementation using SciDB is discussed.

In view of the paradigm shift that makes science ever more data-driven, we consider deterministic scientific hypotheses as uncertain data. This vision comprises a probabilistic database (p-DB) design methodology for the systematic construction and management of U-relational hypothesis DBs, viz., γ-DBs. It introduces hypothesis management as a promising new class of applications for p-DBs. We illustrate the potential of γ-DB as a tool for deep predictive analytics.

As the problems of scientific interest raise in scale and complexity, scientists have to tacitly manage too many analytic elements. Hypotheses are worked out to drive research towards successful explanation and prediction, which characterizes science as a dynamic activity that is partially ordered towards progress. This paper motivates and introduces research lattices, carrying out a lattice-theoretic approach for hypothesis representation and management in large-scale science and engineering. The goal of this work is to equip scientists with tools to manipulate and query hypotheses while keeping track of research progress. We refer to SciDB's array data model and discuss how data and theories could be managed in a unified model management framework.