Fair Engineering of Machine Learning Systems – Lessons Learned from a Literature Review

With the growing prevalence of AI algorithms and their use to prepare and even execute decisions, there is increasing debate about whether the results of machine learning systems tend to be fairer or more unfair. When faced with engineering a fair machine learning solution in practice, trade-offs arise between conflicting fairness notions. We conduct a literature review on this topic. The results of our review indicate that a slight consensus exists that the human concept of fairness is much broader than what lies in the scope of current fairness metrics. We discuss the context of judging fairness metrics. We also find that, albeit much research already has been done, there is room for improvement when seeking to generalize the findings across different scenarios

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Human control over automation: EU Policy and AI Ethics

In this article I problematize the use of algorithmic decision-making (ADM) applications to automate legal decision-making processes from the perspective of the European Union (EU) policy on trustworthy artificial intelligence (AI). Lately, the use of ADM systems across various fields, ranging from public to private, from criminal justice to credit scoring, has given rise to concerns about the negative consequences that data-driven technologies have in reinforcing and reinterpreting existing societal biases. This development has led to growing demand for ethical AI, often perceived to require human control over automation. By engaging in discussions of human-computer interaction and in post-structural policy analysis, I examine EU policy proposals to address the problematizations of AI through human oversight. I argue that the relevant policy documents do not reflect the results of earlier research which have undeniably demonstrated the shortcomings of human control over automation, which in turn leads to the reproduction of the harmful dichotomy of human versus machine in EU policy. Despite its shortcomings, the emphasis on human oversight reflects broader fears surrounding loss of control, framed as ethical concerns around digital technologies. Critical examination of these fears reveals an inherent connection between human agency and the legitimacy of legal decision-making that socio-legal scholarship needs to address.Peer reviewe

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order