Ordre public exceptions for algorithmic surveillance patents
This chapter explores the role of patent protection in algorithmic surveillance and whether ordre public exceptions from patentability should apply to such patents, due to their potential to enable human rights violations. It concludes that in most cases, it is undesirable to exclude algorithmic surveillance patents from patentability, as the patent system is ill-equipped to evaluate the impacts of the exploitation of such technologies. Furthermore, the disclosure of such patents has positive externalities from the societal perspective by opening the black box of surveillance for public scrutiny.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
The Surveillance AI Pipeline
A rapidly growing number of voices argue that AI research, and computer vision in particular, is powering mass surveillance. Yet the direct path from computer vision research to surveillance has remained obscured and dif…
WordRep: A Benchmark for Research on Learning Word Representations
WordRep is a benchmark collection for the research on learning distributed word representations (or word embeddings), released by Microsoft Research. In this paper, we describe the details of the WordRep collection and s…
Word EmbeddingsAlgorithmic Information Design in Multi-Player Games: Possibility and Limits in Singleton Congestion
Most algorithmic studies on multi-agent information design so far have focused on the restricted situation with no inter-agent externalities; a few exceptions investigated truly strategic games such as zero-sum games and…
Scheduling"This is not a data problem": Algorithms and Power in Public Higher Education in Canada
Algorithmic decision-making is increasingly being adopted across public higher education. The expansion of data-driven practices by post-secondary institutions has occurred in parallel with the adoption of New Public Man…
Decision MakingManagementMachine Learning and Public Health: Identifying and Mitigating Algorithmic Bias through a Systematic Review
Machine learning (ML) promises to revolutionize public health through improved surveillance, risk stratification, and resource allocation. However, without systematic attention to algorithmic bias, ML may inadvertently r…