The Royal Statistical Society partnered with the Institute and Faculty of Actuaries (IFoA) to publish a practical guide for data science practitioners regarding the ethical use of data science. The guide was launched at RSS HQ on 7 October 2019.
Data science – the increasing use of large datasets for analysis and decision-making – is becoming of increasing interest with the growth of new data sources and increased computing power. As a result, the ethical significance of data science, and the implications for industries and the wider public, is constantly evolving.
As data science methods become more common practice within statistical and actuarial fields, there are both opportunities and challenges for practitioners. The new guide, for both RSS and IFoA members as well as other data scientists, was developed through engaging with practitioners around the UK and builds upon existing tools and frameworks. This guide focuses on five broad principles of data ethics and ways of considering these within data science work; avoiding harm, supporting the value of data science for society, maintaining professional competence, increasing trustworthiness and maintaining accountability and oversight.
Additional information and resources: ethical data science is also available to download.
Data analytics: the skills needed in STEM was a report of a conference held on 16 November 2016 in partnership with the Royal Society. The report highlights the issues around supply and demand for data scientists in the UK, the skills needed, tools available for data analytics and potential solutions to address skills gap in this area.
Published by Nesta, Creative Skillset and the RSS on 9 July 2014.
By Kevin McConway for the RSS.
This RSS reported on the opportunities and ethics of Big Data, published in February 2016, summarising the programme and discussion of a consultation event convened in partnership with St George’s House in November 2015.
With focus on the data economy and data ethics.
The call for evidence was made by the Centre for Data Ethics and Innovation.
On the Implications of Artificial Intelligence.
Updated for 2019.