Patterns in Practice: cultures of data mining in science, education and the arts

“实践模式”将探讨从业者的信仰、价值观和感受如何相互作用，以塑造他们如何参与数据挖掘和机器学习——“狭义人工智能”的形式。数据和算法正在成为跨部门组织决策者越来越重要的资源。数据挖掘和机器学习技术使分析师能够在组织持有的大量数据中发现隐藏的模式，从而产生可由组织内部或更远的地方的其他人采取行动的预测性见解。随着这些技术的应用越来越普遍，它们也变得越来越有争议。最近剑桥分析公司（Cambridge Analytica）为政治竞选目的挖掘Facebook数据的案例就是一个最近的例子。各行各业的从业者都在询问良好的数据实践是什么样子的，以及如何培养它们，英国政府最近成立了数据伦理与创新中心（Centre for data Ethics and Innovation）来研究这些问题。虽然许多数据科学家对这些技术及其克服人类判断局限性的潜力感到兴奋，但对于其他实践者群体来说，它们可能被视为对隐私的侵入性威胁，对专业见解的不受欢迎的挑战，或者被视为产生低质量信息的过度炒作方法。诸如此类的信仰、价值观和感受，受到从业人员所处文化的影响，是决定这种类型的人工智能在不同实践背景下如何采用和应用的关键因素。它们还塑造了不同的从业者群体如何相互联系，以及他们的数据主题。最终，从业人员的信仰、价值观和感受决定了他们如何理解在不同环境中应用这些技术时，什么是可取的和合乎道德的。在《实践中的模式》一书中，我们将结合访谈、焦点小组和观察来探索不同从业者群体的信仰、价值观和感受如何塑造他们参与数据挖掘和机器学习的方式，并影响数据实践文化的演变。我们将研究那些使用数据挖掘和机器学习技术开发和实施应用程序的人，以及那些被要求使用这些应用程序的输出来告知他们的决策的人的信念、价值观和感受。由于应用的新颖性、对个人和社会的影响以及商业利益的参与等因素都会影响人们对这些技术应用的信念和感受，我们决定在科学、教育和艺术三个截然不同的领域探讨从业人员的看法：(1)挖掘化学数据，为制药行业的药物发现提供信息；(2)英国大学的预测学习分析；(3)数据挖掘在艺术领域的新应用。通过探索从业者的不同视角，我们的目标是构建一个丰富的图景，了解他们对数据挖掘在不同背景下的应用的看法和感受。在此经验基础上，我们的目标是让不同行业的从业者群体参与进来，以增强他们对自己和他人的信仰、价值观和感受如何影响他们如何参与数据挖掘和机器学习应用的理解，以及这如何影响这些应用如何嵌入或不嵌入到不同的组织环境中。利用这种更深层次的理解，我们的目标是授权与我们合作的部门的从业者和相关利益相关者（即公众成员，政策制定者）促进批判性和反思性“数据文化”的发展（Bates, 2017），能够利用数据挖掘和机器学习的可能性，同时批判性地响应其社会影响和认识论局限性。

Patterns in Practice will explore how practitioners' beliefs, values and feelings interact to shape how they engage with and in data mining and machine learning - forms of 'narrow AI'.Data and algorithms are becoming increasingly important resources for decision makers in organisations across sectors. Data mining and machine learning techniques allow analysts to find hidden patterns in the vast troves of data that organisations hold, producing predictive insights that can be actioned by others within the organisation or further afield. As applications of such techniques have become more common place, they have also become more controversial. The recent case of Cambridge Analytica mining Facebook data for political campaigning purposes is a recent example. Across sectors practitioners are asking what good data practices look like and how they can be fostered, and the UK government has recently launched the Centre for Data Ethics and Innovation to examine such issues.While many data scientists are excited by these techniques and their potential to overcome perceived limitations of human judgement, for other groups of practitioners they can be perceived as an intrusive threat to privacy, an unwelcome challenge to professional insight, or dismissed as overhyped methods that produce poor quality information. Beliefs, values and feelings such as these, influenced by the cultures that practitioners are embedded within, are crucial factors that shape how the adoption and application of this type of AI unfolds in different contexts of practice. They also shape how different groups of practitioners come to relate to one another and the subjects of their data. Ultimately, practitioners' beliefs, values and feelings shape how they come to understand what is desirable and ethical with regard to the application of such techniques in different contexts.In Patterns in Practice, we will use a combination of interviews, focus groups and observations to explore how the beliefs, values and feelings of different groups of practitioners shape how they engage with data mining and machine learning, and influence the evolution of cultures of data practice. We will examine the beliefs, values and feelings both of those developing and implementing applications that use data mining and machine learning techniques, and those being asked to use the outputs of such applications to inform their decision making. Since factors such as the novelty of application, individual and social implications, and the involvement of commercial interests can impact on people's beliefs and feelings about the application of such technologies, we have decided to explore practitioners' perceptions within three contrasting sectors in science, education and the arts: (1) mining chemical data to inform drug discovery in the pharmaceutical industry, (2) predictive learning analytics in UK universities, and (3) novel applications of data mining in the arts. Through exploring a diverse range of practitioners' perspectives, we aim to build a rich picture about what they believe and how they feel about the application of data mining in different contexts. Building upon this empirical foundation, we aim to engage different groups of practitioners across the sectors to enhance their understanding of the ways in which their own and others' beliefs, values and feelings can impact upon how they engage with data mining and machine learning applications and how this shapes how such applications become embedded, or not, into different organisational contexts. Drawing on this deeper understanding, we aim to empower practitioners in the sectors we work with and relevant stakeholders (i.e. members of the public, policy makers) to foster the development of critical and reflective "data cultures" (Bates, 2017) that are able to exploit the possibilities of data mining and machine learning, while being critically responsive to their societal implications and epistemological limitations.