Fairness and Abstraction in Sociological Systems — Bedrock concepts in computer science such as abstraction and modular design are used to define notions of fairness and discrimination, to produce fairness-aware learning algorithms, and to intervene at different stages of a decision-making pipeline to produce “fair” outcomes. In this paper, however, we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We outline this mismatch with five “traps” that fair-ML work can fall into even as it attempts to be more context-aware in comparison with traditional data science. Noted researcher (and Friend Of O’Reilly) danah boyd is a co-author.
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Graviton: Trusted Execution Environments on GPUs — Graviton enables applications to offload security- and performance-sensitive kernels and data to a GPU, and execute kernels in isolation from other code running on the GPU and all software on the host, including the device driver, the operating system, and the hypervisor.