Topic 4: Ethics and Data Science

According to the Royal Statistical Society (RSS) Data Science Section and the Institute and Faculty of Actuaries (IFoA), ethics in data science “and the implications for industries and the wider public, are constantly evolving. As data science methods become more common within statistical and actuarial fields, there are both opportunities and challenges for individuals working in data science (‘practitioners’)” (Institute and Faculty of Actuaries & Royal Statistical Society, 2019).

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As the impact of data science to society is essential, data scientists are required to find results that could benefit the society, improving quality of human life. It is crucial for scientists to be able to balance the benefits with the discrepancies of the outcomes that derive from the data.

Data science can be a source of harm in terms of privacy and equality. The ethical considerations should focus on finding ways to respect boundaries and neutralize the potential harm.

A data scientist should be in the position to fully comprehend “the sources of error and bias in data, using ‘clean’ data (eg edited for missing, inconsistent or erroneous values), and supporting work with robust statistical and algorithmic methods that are appropriate to the question being asked” (Institute and Faculty of Actuaries & Royal Statistical Society, 2019).

If ethical principles among scientists are questionable, the public could lose trust, affecting the relationship between science and society. Transparency is crucial for building and maintaining trust.

Accountability involves careful consideration of when to delegate decision-making to systems rather than humans. It’s crucial to understand and communicate the consequences of such delegation, especially as it could result in the implementation of advanced AI systems without proper governance (Institute and Faculty of Actuaries & Royal Statistical Society, 2019).