In response to public scrutiny of data-driven algorithms, the field of data science has adopted ethics training and principles. Although ethics can help data scientists reflect on certain normative aspects of their work, such efforts are ill-equipped to generate a data science that avoids social harms and promotes social justice. In this article, I argue that data science must embrace a political orientation. Data scientists must recognize themselves as political actors engaged in normative constructions of society and evaluate their work according to its downstream impacts on people's lives. I first articulate why data scientists must recognize themselves as political actors. In this section, I respond to three arguments that data scientists commonly invoke when challenged to take political positions regarding their work. In confronting these arguments, I describe why attempting to remain apolitical is itself a political stance--a fundamentally conservative one--and why data science's attempts to promote "social good" dangerously rely on unarticulated and incrementalist political assumptions. I then propose a framework for how data science can evolve toward a deliberative and rigorous politics of social justice. I conceptualize the process of developing a politically engaged data science as a sequence of four stages. Pursuing these new approaches will empower data scientists with new methods for thoughtfully and rigorously contributing to social justice.
翻译:在对数据驱动算法的公开审查中,数据科学领域采用了道德培训和原则。虽然伦理道德可以帮助数据科学家思考其工作的某些规范性方面,但这种努力不足以产生数据科学,避免社会伤害和促进社会正义。在本条中,我主张数据科学必须包括政治方向。数据科学家必须承认自己是参与社会规范建设的政治行为者,并根据其对人们生活的下游影响评价其工作。我首先说明为什么数据科学家必须承认自己是政治行为者。在本节中,我回应了数据科学家在受到挑战时通常援引的三种论点,即数据科学家在就其工作采取政治立场时通常援引的三种论点。在面对这些论点时,我描述了为什么试图保持非政治性本身就是一种政治立场,一种根本保守的一号政治立场,以及为什么数据科学试图促进“社会公益”的工作危险地依赖未经区分和递增的政治假设。我然后提议一个框架,说明数据科学如何演变为审议和严格的社会公正政治政治。我设想了发展政治上参与的数据科学的过程,作为四个阶段的顺序。我描述了为什么试图保持政治性的政治立场——一种根本保守的一手法,使数据能够以新的方法为社会公正提供数据。