Bias and unfairness in machine learning models: a systematic literature review

One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfairness and bias. This study aims to examine existing knowledge on bias and unfairness in Machine Learning models, identifying mitigation methods, fairness metrics, and supporting tools. A Systematic Literature Review found 40 eligible articles published between 2017 and 2022 in the Scopus, IEEE Xplore, Web of Science, and Google Scholar knowledge bases. The results show numerous bias and unfairness detection and mitigation approaches for ML technologies, with clearly defined metrics in the literature, and varied metrics can be highlighted. We recommend further research to define the techniques and metrics that should be employed in each case to standardize and ensure the impartiality of the machine learning model, thus, allowing the most appropriate metric to detect bias and unfairness in a given context

Causas e problemas de privacidade de dados em sistemas de big data analytics: uma revisão sistemática da literatura

This study aims to identify and describe data privacy issues and their causes based on the literature on the subject. This is an exploratory research based on a systematic literature review. It explores scientific databases and serves as a basis for future descriptive and explanatory research, which will allow the improvement of the information systems, adapting them to the privacy needs of citizens. Nine data privacy issues and seven causes were identified

The Contribution of Ethical Governance of Artificial Intelligence & Machine Learning in Healthcare

With the Internet Age and technology progressively advancing every year, the usage of Artificial Intelligence (AI) along with Machine Learning (ML) algorithms has only increased since its introduction to society. Specifically, in the healthcare field, AI/ML has proven to its end-users how beneficial its assistance has been. However, despite its effectiveness and efficiencies, AI/ML has also been under scrutiny due to its unethical outcomes. As a result of this, two polarizing views are typically debated when discussing AI/ML. One side believes that AI/ML usage should continue regardless of its unsureness, while the other side argues that this technology is too dangerous and should not be utilized at all. Given the fact that AI/ML can provide prompt and fairly accurate results, it is unrealistic to assume that AI/ML usage will end any time soon. Therefore, governance of AI/ML is needed to ensure that these technologies are reliable. Notably, AI governance has been positively reviewed and pushed for by scholars in the field. While AI governance does guarantee a sense of oversight on AI/ML, this form of governance is not sustainable. AI governance primarily focuses on the safety of the technology, with ethical, legal, and social factors serving as elements of AI governance. The safety of AI/ML is only one of the considerations for producing and ensuring ethical AI/ML. Ethical governance of AI/ML, which concentrates on incorporating ethics into all aspects of AI/ML—specifically, narrowing in on the stakeholders involved, will lead to not only a safer product but a more viable one as well. Thus, ethical governance of AI/ML must be advocated for in order to bring more awareness, which would lead to greater research and implementation of this type of governance. Although AI/ML can be used for a multitude of areas, the healthcare industry is slightly more significant, especially since these technologies directly affect the patients’ health. This dissertation explores the contribution of ethical governance of AI/ML in several facets of healthcare. As AI/ML requires big data to provide outcomes, the context of data analytics is discussed. Other areas the dissertation explores are clinical decision-making, end-of-life decisions, and biotechnology. While these topics certainly do not cover the whole healthcare field, the dissertation attempts to include a wide range of AI/ML functions from the beginning of its process (with data analytics) to the future of AI/ML (with biotechnology). With each of these areas of interest, various ethical governance principles are introduced and endorsed for to develop ethical AI/ML. The goal of this dissertation in discussing the contribution of ethical governance of AI/ML in healthcare is to provide a foundational groundwork for more future research of the ethical governance of AI/ML