Data augmentation for fairness-aware machine learning: Preventing algorithmic bias in law enforcement systems
Researchers and practitioners in the fairness community have highlighted the ethical and legal challenges of using biased datasets in data-driven systems, with algorithmic bias being a major concern. Despite the rapidly growing body of literature on fairness in algorithmic decision-making, there remains a paucity of fairness scholarship on machine learning algorithms for the real-time detection […]
This chapter examines how privacy and trust are at risk in surveillance societies. It formulates several scenarios and policy recommendations addressing the risks and concludes by suggesting a research agenda aimed at mitigating future risks.
This paper describes surveillance impact assessment (SIA), a methodology for identifying, assessing and resolving risks, in consultation with stakeholders, posed by the development of surveillance systems. This paper appears to be the first such to elaborate an SIA methodology. It argues that the process of conducting an SIA should be similar to that of a […]