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When working with stakeholders beyond your own team, there are many tools and best practices for improving the quality of engagement through activities like mapping relevant project stakeholders, organising stakeholder workshops, establishing shared language and understanding, and creating the foundation for effective collaboration.

This chapter will share guidance and resources for facilitating different forms of stakeholder engagement for data science collaborations drawing upon expertise from the Turing Research Application Management (RAM) team.

Stakeholder mapping

Key question: Who is involved in a collaboration, either as a user, impacted group, part of the community of practice, or in some other capacity?

The goal of stakeholder mapping is to understand the people and organisations involved in a collaboration, and to collect all this information in one place. This helps establish a shared understanding of who is involved in what capacity, which is a useful resource for onboarding new teammates and to make sure everyone is on the same page. This activity is also often a precursor to other engagement activities such as impact assessments, user experience workshops, collaboration cafes and more, as stakeholder maps can help identify who should be centred in the research process. Stakeholder mapping is a core activity of many Research Infrastructure Roles and may serve different goals, for example:

Stakeholder mapping is not meant to be a single source of truth. It is a tool to capture a team’s understanding of the stakeholder landscape, clarify relationships, and surface groups that may share common characteristics or challenges. A well thought-out, detailed stakeholder map will facilitate bringing a team onto the same page, which can help with developing engagement pipelines.

Example resources

RAM insights

Requirements gathering & alignment

Key question: How can we align stakeholders on shared language and goals?

Requirements gathering is a helpful exercise used to collect information from different stakeholders involved in a project which can then be used to mitigate potential misunderstandings, agree on shared terminology, and identify opportunities for collaboration. When bringing people from different backgrounds or organisations into a collaboration, it can be helpful to align on shared language and understanding on key concepts relevant to a project. This is motivated by the fact that different teams may have different ways of working and understandings of project outputs like “case study” or “user-friendly website” which can lead to miscommunication or misalignment of expectations. Once the information is gathered in a single place, you can identify questions and highlights from the interviews to prompt group discussion for alignment.

Example resources

RAM insights

Organising stakeholder workshops

Key question: What work before, during, and after a workshop should be done to run an effective stakeholder workshop?

Workshops are best suited for engagement activities where there is a need to bring people together to deeply engage on an area of interest with the purpose of working on a task, as opposed to gatherings which are purely driven by conversation. This ensures participants are taking active steps towards achieving a shared goal.

Organising an effective workshop is a multi stage process which can be segmented into three stages; pre-workshop, workshop, and post-workshop. We think it’s important to prepare for each stage with equal weight and consideration to ensure the best process for participants throughout.

Example resources

RAM insights

Strategy alignment workshops

Key question: How can we bring different ideas across a group of collaborators into a shared strategy?

A group of collaborators will struggle to work together and progress if its members do not agree on what they are trying to achieve together. Strategy alignment goes deeper than agreeing on day to day work - it asks what the team chooses to prioritise, what they value, what motivates them, and ultimately what real-world impact they hope to see from their work. A helpful model to inform this exercise is Simon Sinek’s Golden Circle method, where the team approaches their work in the following order:

It is important to take these conversations in order, so that strategy alignment can be built from the bottom-up.

Example resources

RAM insights

User-centric design

Key question: How can research outputs benefit from a user-centric design process?

Although many research tools are created for a very narrow purpose, we encourage researchers to consider the user experience (UX) throughout the process and to incorporate UX design principles during development to make the tool more accessible and usable for all potential users. Activities such as user journey mapping and user testing can greatly enhance your understanding of who is using the research tool and how they employ the tool in a real-world scenario. By considering potential users of the tool from the early stages of a project, you can include features that will make the tool easier to use and more relevant to potential stakeholders, and therefore more likely to be used and applied in practice.

Existing resources can also benefit greatly from making improvements to the UX by establishing heuristics to define criteria for usability and to identify areas in need of improvement. By utilising exercises and heuristics designed specifically to consider and evaluate the user experience, researchers will examine the assumptions built into the tool and gain a deeper understanding of how it can serve different users types. These insights can help a team to develop a roadmap for the tool to plan out future developments with varied stakeholders in mind, leading to more meaningful stakeholder engagements based around a deeper understanding of how those stakeholders are interacting with and repurposing a research output.

Example resources

RAM insights

Stakeholder impact assessments

Key question: How can we develop projects with continual sensitivity to the ways in which these impact stakeholders?

Stakeholder impact assessments (SIAs) are a tool used to gain anticipatory insight into the project’s likely impacts, defined as the possible harms and benefits. SIAs take the form of questionnaires addressing the ethical permissibility, transparency, accountability, and equity of projects. SIAs are used to identify and document the full range of potential impacts and provide project teams with a contextual awareness of the social environments impacted by their projects. This activity can help teams steer the direction of their projects preemptively to ensure that they support the wellbeing and sustainability of the individuals and communities they impact and mitigate any harms.

Evaluations of project impacts should ideally be done in collaboration with stakeholders. In the context of SIAs, stakeholders are defined as individuals and communities that are impacted or may impact projects. Stakeholders that are most vulnerable to potential project harms are considered the most salient. Engaging stakeholders in conducting SIAs helps secure the accuracy and integrity of SIA outcomes. This is because gaps in identity and experience between project teams and impacted individuals may cause there to be differences between how team members view project impacts and how stakeholders experience them. Facilitating proportionate stakeholder engagement and input throughout the AI lifecycle is a way to gain a richer understanding of the impacts that projects may have on stakeholders.

Conducting a SIA is not a one-off activity but an iterative one that occurs at key points throughout the design, development, and deployment stages of the AI project lifecycle, each time informing the direction of the project so that it continually adapts to changing contexts. After each iteration is conducted, project teams are asked to re-assess questions addressed within the Stakeholder Engagement Process. These questions motivate project teams to iteratively re-assess the extent to which their analysis of project stakeholders and reflection of team positionality continues to be accurate and relevant. These re-assessments are used to determine engagement objectives and methods for each following SIA.

Example resources

RAM insights

References
  1. Leslie, D. (2019). Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector. 10.5281/ZENODO.3240529