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

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.

Who?
Through our research into how affective and ideational factors shape data cultures, we aim to develop an empirically grounded foundation for engaging differently situated groups of practitioners in critical and reflective dialogue, specifically in the domains of science (drug discovery), education (learning analytics) and the arts (arts practice). Through doing so we aim to foster the development of "data cultures" that allow organisations to exploit the possibilities of DM/ML techniques while simultaneously being responsive to their societal implications and epistemological limitations. Our partner organisations (GlaxcoSmithKline [GSK], JISC and Sheffield Doc/Fest) will work with us to achieve our objectives in their own organisations and sectors.
-Pharmaceutical sector. Our partners GSK will benefit through becoming better informed about how affective-ideational dynamics influence how DM/ML outputs are embedded in - and inform decision-making across - the drug discovery pipeline. Beyond GSK, these insights will be transferable to other organisations within the pharmaceutical sector.
-Higher education. Our partners JISC will benefit from enhanced understanding about how predictive learning analytics is being received by different practitioners within the context of the UK's Higher Education sector. This will enhance JISC's capacity to develop best practice guidelines for ethical practice, and HE institutions' ability to make effective and ethical decisions about the adoption and use of learning analytics.
-Cultural sector. Our artists-in-residence and cultural partners will benefit through developing their DM/ML practice, and opportunities to engage audiences with these practices. The Sheffield Doc/Fest, and other collaborating digital arts organisations, will gain insights which can inform their curation practices, and expand their art-science practice.

How?
In order to foster the development of more critical and reflective cultures of engagement with DM/ML practices in the above sectors, we will:
1. Involve practitioners in partner organisations and our artist in residence in the research throughout, and disseminate findings within these organisations
2. Engage research participants in a series of dialogue events that aim to foster critical cross-sector and cross-profession discussion among differently situated practitioners
3. Disseminate findings to practitioners via blogs and articles in practitioner publications, and at relevant festivals, conferences and events.

A further objective is to raise awareness of DM/ML and the affective-ideational context for DM/ML practice among members of the public. We will:
1. Host an artist in residence who will produce a DM/ML art work to be exhibited at Sheffield Doc/Fest Alternate Realities in the final year of the project
2. Run a work in progress public event as part of the residency, and public dialogue events that engage the public, practitioners and policy makers in constructive discussion about DM/ML
3. Disseminate findings on popular blogs and social media, at digital festivals and events.

A final objective is to develop skills and capacity among the members of the research team and our collaborators. We will:
1. Provide basic DM/ML training for all researchers on the project
2. Have a training budget of £1000 for each RA allocated based on a Training Needs Analysis
3. Support our artists in residence to apply for Arts Council funding in order to tour the exhibit nationally.