The Ethics of Algorithms: Key Problems and Solutions

Abstract

Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016 (Mittelstadt et al. 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms.

The Fourth Industrial Revolution – Smart Technology, Artificial Intelligence, Robotics and Algorithms: Industrial Psychologists in Future Workplaces

“…Platforms collect a great deal of data on market behavior and outcomes, and they have virtually complete control over the details that market participants can see and when. Those changes would have profound consequences for labor markets in terms of equity and efficiency (Tsamados et al, 2021 ).…”

Artificial intelligence and work: a critical review of recent research from the social sciences

“…Surveillance and control: algorithmic management raises obvious issues of privacy (Bhave et al 2020;Ebert et al 2021;Fukumura et al 2021;Tsamados et al 2022), not just at the workplace, but also at home, notably following the pandemic-induced shift to home-based working (Collins 2020). Privacy infringements can occur at all stages of the data cycle: at the time of collection, in the analysis of the data, in the use of the data, and when data ought to be erased.…”

Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers

“…Apply causal reasoning to identify and understand and discrimination based on protected attributes [128] 16. Implement both mathematical and ethically grounded fairness definitions [67,129] 17. Make models and classifier safe from adversarial attacks using game theory and GANs [130][131][132][133] 18.…”

“…similar percentages of false positives and/or false negatives for the different socioeconomic groups under consideration [67]. However, depending on the context, these mathematical implementations of fairness could be complemented by more ethically grounded ones [67,129] Models should also be trained to minimise the effect of adversarial attacks or of poisoned data samples. Evasion attacks can be protected against by devising systems composed of multiple classifiers, as outlined in [137], or other adversary-aware learning algorithms (e.g.…”