Modeling Epistemological Principles for Bias Mitigation in AI Systems: An Illustration in Hiring Decisions

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

Population aging and the rising costs of public pension in Brazil

This article examines the evolution of retirement in Brazil and its old-age support programs (public pension). The key objective is to show that given the current trend in population and size of the programs, their sustainability in the near future may be endangered. In this paper, we also provide a measure of public pension expenditure under different policy scenarios. This paper provides empirical evidence indicating that the absence of appropriate policies can aggravate adverse effects of population aging. We show that the public pension system works less efficiently than desired and that it is already in a weaker condition than systems in more developed nations. We contribute to the debate on how critical policy areas may reduce the potential economic impact of demographic changes.public pension system, population aging, demographic changes, forecasting.

Scaling impact: facility location selection for social enterprises

Migration crises and climate change pose numerous challenges to countries and international agencies. Nonetheless, social enterprises represent a change in the industry that fights these challenges. This thesis aims to elaborate on the expansion processes of social enterprises while focusing on the Facility Location Selection problem by presenting a model which can be used as a guide for companies operating in the social sector. Having had the opportunity to intern at Makers Unite, a social enterprise acting in the apparel industry, this firm will exemplify the theory throughout this thesis

Incumbent tenure and municipal governance: the Portuguese case

The present study analyses Portuguese municipalities’ data in order to understand how the incumbent tenure influences economic performance at a municipal level. The incumbents’ age is used as an instrumental variable to the mayors’ tenure and its effect on the local economic development and pre-electoral fiscal policy is measured through a Two-stage Least Squares estimation with random effects. Tenure proves to have an insignificant positive impact on all economic and fiscal variables analysed and specific time-effects preponderance is outlined

Secure storage and sharing of health data in a Blockchain environment

These days, the amount of digital health data is increasing. Legacy systems with privacy and security problems are used to store and share them. The blockchain technology appears as a disruptive solution to improve those systems. The main objective of this thesis work is to create a model based on the blockchain technology to securely store and share simple health assets. The days when blockchain was only related with cryptocurrencies, like bitcoin, are long past. Today, blockchain is considered an important technology to all applications that need immutable and traceable data, a cryptographic and distributed ledger and secure transactions. MedRec and Medical chain are examples of solutions, based on the blockchain technology, to manage health records that have been developed. The projected model describes a possible integration between MyXimi and a blockchain network. MyXimi users would be the terminals of the network. They will insert new health assets and get assets from the network. The network would consist of several distributed machines administrated by Compta. Those machines would store a public ledger of the health data transactions and all the sensitive data encrypted, always. As an application feature, management terminals would have access to anonymous health data for stats. To test the main features of the projected model it was created a demonstration system: XBlock. Within this system were used several methods to increase the privacy and security of the health data, such as, data encryption and data masking. XBlock was presented to a board of business and IT specialists, in Compta. In conclusion, XBlock proves itself as a true value to enhance the levels of security and privacy of health data when storing it and sharing it. As future work, is suggested the implementation of the propose model integrated with an health app. During this process, it’s important to improve the blockchain network, in terms of its performance and scalability

Human-in-the-loop image classification

Nos últimos anos, tem havido um crescimento na utilização de Machine Learning e uma necessidade crescente de aplicar modelos de Machine Learning a várias necessidades empresariais, desde a análise dos padrões de compra dos clientes até à tomada de uma decisão empresarial para fazer crescer esse mesmo negócio. Num ambiente empresarial acelarado que nos encontramos atualmente, desenvolver e disponibilizar um bom modelo pode não ser um processo muito célere. O principal motivo são os dados necessários para obter o bom modelo, visto que para obtê-lo pode ser necessário uma grande quantidade de dados e isto pode afetar o tempo de treino do modelo, ou pode ser necessário um pré-processamento dos dados, levando ao aumento do tempo para obter o bom modelo. Com isto, este trabalho apresenta uma possível solução para este problema, onde, através do Active Learning, o humano aplica etiquetas a uma pequena quantidade dados, de seguida são criados vários modelos com parâmetros diferentes para serem treinados até que um intervalo de valores seja atingido. Por fim, algumas métricas serão extraídas e analisadas para concluir qual o melhor modelo. Por fim é apresentada a previsão do modelo em conjunto com uma explicação com o que o modelo considerou importante.In recent years, there has been a growth in the use of Machine Learning and an increasing need to apply Machine Learning models to various business needs, from analysing customer buying patterns to making a business decision to grow that same business. In the fast-paced business environment we currently find ourselves in, developing and delivering a good model may not be a very fast process. The main reason is the data required to obtain the good model, since to obtain it may require a large amount of data and this may affect the training time of the model, or a pre-processing of the data may be required, leading to increased time to obtain the good model. With this, this work presents a possible solution to this problem, where, through Active Learning, the human applies labels to a small amount of data, then several models are created with different parameters to be trained until a range of values is reached. Finally, some metrics will be extracted and analysed to conclude which model is the best. Finally the prediction of the model is presented together with an explanation of what the model considered important