Most research on natural language processing treats bias as an absolute concept: Based on a (probably complex) algorithmic analysis, a sentence, an article, or a text is classified as biased or not. Given the fact that for humans the question of whether a text is biased can be difficult to answer or is answered contradictory, we ask whether an "absolute bias classification" is a promising goal at all. We see the problem not in the complexity of interpreting language phenomena but in the diversity of sociocultural backgrounds of the readers, which cannot be handled uniformly: To decide whether a text has crossed the proverbial line between non-biased and biased is subjective. By asking "Is text X more [less, equally] biased than text Y?" we propose to analyze a simpler problem, which, by its construction, is rather independent of standpoints, views, or sociocultural aspects. In such a model, bias becomes a preference relation that induces a partial ordering from least biased to most biased texts without requiring a decision on where to draw the line. A prerequisite for this kind of bias model is the ability of humans to perceive relative bias differences in the first place. In our research, we selected a specific type of bias in argumentation, the stance bias, and designed a crowdsourcing study showing that differences in stance bias are perceptible when (light) support is provided through training or visual aid.
翻译:大多数关于自然语言处理的研究将偏见视为绝对概念:根据(可能复杂的)算法分析、句子、文章或文本分类为偏向性或非偏向性。鉴于对人类而言,文本是否偏向的问题很难回答,或答案自相矛盾,我们问“绝对偏向分类”是否是一个有希望的目标。我们认为问题不是解释语言现象的复杂性,而是读者社会文化背景的多样性,这些问题不能统一处理:决定文本是否超越了非偏见和偏向之间的古老界线是主观的。通过询问“文本X比文本Y更[不那么,平等]有偏向性?”我们提议分析一个简单的问题,因为根据其构建,这个问题相当独立于观点、观点或社会文化方面。在这种模式中,偏见变成一种偏向关系,从最不偏向最偏颇的文本,而无需决定如何划线。这种偏向模式的一个先决条件是人类能够理解在第一方向上相对偏向偏向偏向差异的能力。在我们的研究中,我们选择了一种特定的偏向性观点,即我们选择了一种偏向式的倾向。