While the operationalisation of high-level AI ethics principles into
practical AI/ML systems has made progress, there is still a theory-practice gap
in managing tensions between the underlying AI ethics aspects. We cover five
approaches for addressing the tensions via trade-offs, ranging from rudimentary
to complex. The approaches differ in the types of considered context, scope,
methods for measuring contexts, and degree of justification. None of the
approaches is likely to be appropriate for all organisations, systems, or
applications. To address this, we propose a framework which consists of: (i)
proactive identification of tensions, (ii) prioritisation and weighting of
ethics aspects, (iii) justification and documentation of trade-off decisions.
The proposed framework aims to facilitate the implementation of well-rounded
AI/ML systems that are appropriate for potential regulatory requirements.

通过权衡的五种方法，提出了一个框架，旨在解决高级人工智能伦理原则在实际系统中的操作问题，以应对潜在的监管要求。

在实施负责任的人工智能中解决伦理平衡

Resolving Ethics Trade-offs in Implementing Responsible AI

Deep learning is renowned for its theory-practice gap, whereby principled
theory typically fails to provide much beneficial guidance for implementation
in practice. This has been highlighted recently by the benign overfitting
phenomenon: when neural networks become sufficiently large to interpolate the
dataset perfectly, model performance appears to improve with increasing model
size, in apparent contradiction with the well-known bias-variance tradeoff.
While such phenomena have proven challenging to theoretically study for general
models, the recently proposed Interpolating Information Criterion (IIC)
provides a valuable theoretical framework to examine performance for
overparameterized models. Using the IIC, a PAC-Bayes bound is obtained for a
general class of models, characterizing factors which influence generalization
performance in the interpolating regime. From the provided bound, we quantify
how the test error for overparameterized models achieving effectively zero
training error depends on the quality of the implicit regularization imposed by
e.g. the combination of model, optimizer, and parameter-initialization scheme;
the spectrum of the empirical neural tangent kernel; curvature of the loss
landscape; and noise present in the data.

深度学习在理论和实践之间存在理论实践差距，最近的研究关注了过拟合现象及其影响因素，提出了插值信息准则来研究过参数化模型的性能和泛化能力。