The study of social values in fairy tales opens the possibility to learn
about the communication of values across space and time. We propose to study
the communication of values in fairy tales from Portugal, Italy and Germany
using a technique called word embedding with a compass to quantify vocabulary
differences and commonalities. We study how these three national traditions of
fairy tales differ in their explicit references to values. To do this, we
specify a list of value-charged tokens, consider their word stems and analyse
the distance between these in a bespoke pre-trained Word2Vec model. We
triangulate and critically discuss the validity of the resulting hypotheses
emerging from this quantitative model. Our claim is that this is a reusable and
reproducible method for the study of the values explicitly referenced in
historical corpora. Finally, our preliminary findings hint at a shared cultural
understanding and the expression of values such as Benevolence, Conformity, and
Universalism across European societies, suggesting the existence of a
pan-European cultural memory.

通过词嵌入和量化词汇差异的罗盘技术，我们研究了葡萄牙、意大利和德国童话中价值观的传播，发现这三个国家的童话中明确的价值观存在差异，但初步研究结果表明仁慈、顺从和普遍主义等价值观在欧洲社会中具有共享的文化理解和表达，暗示了泛欧洲的文化记忆的存在。

童话中明确引用社会价值观：三个欧洲文化的比较

Explicit References to Social Values in Fairy Tales: A Comparison  between Three European Cultures

As machine learning models are increasingly used for high-stakes decision
making, scholars have sought to intervene to ensure that such models do not
encode undesirable social and political values. However, little attention thus
far has been given to how values influence the machine learning discipline as a
whole. How do values influence what the discipline focuses on and the way it
develops? If undesirable values are at play at the level of the discipline,
then intervening on particular models will not suffice to address the problem.
Instead, interventions at the disciplinary-level are required. This paper
analyzes the discipline of machine learning through the lens of philosophy of
science. We develop a conceptual framework to evaluate the process through
which types of machine learning models (e.g. neural networks, support vector
machines, graphical models) become predominant. The rise and fall of
model-types is often framed as objective progress. However, such disciplinary
shifts are more nuanced. First, we argue that the rise of a model-type is
self-reinforcing--it influences the way model-types are evaluated. For example,
the rise of deep learning was entangled with a greater focus on evaluations in
compute-rich and data-rich environments. Second, the way model-types are
evaluated encodes loaded social and political values. For example, a greater
focus on evaluations in compute-rich and data-rich environments encodes values
about centralization of power, privacy, and environmental concerns.

通过科学哲学的视角分析机器学习学科，开发一个概念框架以评估机器学习模型发展的过程，并探讨价值观如何影响该学科的发展和模型类型的崛起。我们认为模型类型的崛起是自我强化的，并且模型类型的评估会编码社会和政治价值观，例如围绕计算和数据密集型环境的评估方式关注权力集中、隐私和环境保护等价值观。因此，需要考虑纪律层面的干预以应对与学科整体相关的不良价值观。

机器学习中的价值观学科转变

Value-laden Disciplinary Shifts in Machine Learning

This paper introduces the notion of value-based argumentation frameworks, an
extension of the standard argumentation frameworks proposed by Dung, which are
able toshow how rational decision is possible in cases where arguments derive
their force from the social values their acceptance would promote.

介绍了基于价值观的论证框架的概念，该框架是对 Dung 提出的标准论证框架的扩展，能够展示在与社会价值观相符的论证中，如何做出合理的决策。