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About this Skills Track

Skills Track Information

  • Title: Responsible Research and Innovation
  • Authors: Dr Christopher Burr and Claudia Fischer
  • Last updated: March 2023
  • Status: In Development
  • Citation Information: 
    Burr, C., Fischer, C., and Rincon, C. (2023) Responsible Research and Innovation (Turing Commons Skills Track). Alan Turing Institute. [10.5281/zenodo.7755693](10.5281/zenodo.7755693).

Responsible scientific research and technological innovation (RRI) is a vital component of a flourishing and fair society. As an area of study and mode of enquiry, RRI plays a central role within academic, public, private, and third-sector organisations. This skills track will explore what it means to take (individual and collective) responsibility for (and over) the processes and outcomes of research and innovation in data science and AI. The notion of 'responsibility' employed throughout this skills track will be grounded in an understanding of the moral relationship between science, technology, and society, exploring both historical and contemporary examples of RRI practices.

As well as looking at the theoretical basis of RRI, this skills track will also take a hands-on approach by exploring a variety of tools and procedures that can help operationalise and implement a robust notion of responsibility within research and innovation practices.

The skills track is organised around core and optional modules. It starts with two core modules on What is responsible research and innovation? followed by a module on The project lifecycle model - a heuristic model used to represent the different stages of an ML or AI project.

It then offers five optional modules (of which at least one must be completed), based on the SAFE-D principles: Sustainability, Accountability, Fairness, Explainability, and Data stewardship. These overarching ethical principles must then be put into practice through a series of tools and methods covered in the modules. Finally, the skills track ends with a core module on Responsible Communication and Open Science, focusing on the importance of making research open and reproducible, as well as communicating results in an accessible manner.

Learning Objectives

This skills track has the following learning objectives:

  • Understand what is meant by the term ‘responsible research and innovation’, including the motivation and historical context for its increasing relevance.
  • Identify and evaluate the ethical issues associated with the key stages of a typical data science or AI project lifecycle: (project) design, (model) development, (system) deployment.
  • Explore practical tools and mechanisms for operationalising the several ethical principles, which have been designed to guide the responsible design of data science and AI projects.
  • Understand the importance of responsible communication in the design, development, and deployment of data science and AI projects, and explore ways to exercise this responsibility.

Table of Contents

  • What is Responsible Research and Innovation?

    This module looks at foundational concepts and topics associated with responsible research and innovation (RRI).

    Go to module

  • The Project Lifecycle

    This module introduces the model and framework of the ML/AI project lifecycle, and explores its constituent stages.

    Go to module

  • The SAFE-D Principles

    A set of optional modules that explore the SAFE-D principles—a set of guiding principles for the responsible design of data science and AI projects.

    Go to module

  • Responsible Communication

    This module explores and critically examines what it means to act responsibly when communicating the processes by which a project is governed. (Coming Soon!)