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A Guide for Ethical Data Science: The Royal Statistical Society and Institute and Faculty of Actuaries produce a new ethical framework

Recent developments in data science ethics over the last number of months have shown that support and guidance for professionals has become an increasingly important topic; the European Insurance and Occupational Pensions Authority (EIOPA) established a ‘Consultative Expert Group on Digital Ethics in Insurance’ in September last year, the Actuarial Association of Europe published a document outlining its current perspective on the ethics and responsibility of data scientists and the European Actuarial Academy is hosting its first ‘Data Science and Data Ethics Conference‘ on the 29th and 30th of June in 2020. Much of the activity has discussed the evolving role of actuaries in data science, noting the opportunities within the space but acknowledging that a number of risks still need to be addressed.

To assist in navigating some of the risks posed by the ethical considerations of data science, in October 2019 the Institute and Faculty of Actuaries (IFoA) and the Royal Statistical Society (RSS) Data Science Section jointly published ‘A Guide for Ethical Data Science.’ This guide aims to address the challenges faced by their members while complementing existing ethical and professional guidance. It is non-mandatory and does not impose any obligations upon RSS or IFoA members.

The development of the guide began in 2018 with four workshops with data science professionals. These workshops centred on four key questions raised by the RSS and IFoA:

  • What does a good data science workflow look like?
  • How should data science fit into the structure of an organisation?
  • What do executives and managers need to know about data science?
  • What is a data scientist’s responsibility to wider society?

Although the data science practitioners found that best practice for data science is dependent on industry and even company-specific factors such as organisational design, historical workflows and the availability of skills within teams, they did broadly agree on several high-level principles and practices which have informed the guide. The guide does highlight, however, that some of the more complex issues faced by practitioners will require further thought and input from professionals.

The guide looks at five recurring ethical themes from existing frameworks relating to data science and artificial intelligence (AI):

  1. Seek to enhance the value of data science for society.
  2. Avoid harm.
  3. Apply and maintain professional competence.
  4. Seek to preserve or increase trustworthiness.
  5. Maintain accountability and oversight.
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