Patrick Steadman Aspiring clinician-scientist at The University of Toronto

Datakind: doing good data science

Recently DataKind, an organization that I have been following for some time, held a session exploring the need for a ‘hippocratic oath [in] data science’.

As research using massive amounts of data becomes more and more popular, there develops a need for an ethical line. Similar to physician’s ‘do no harm’ mantra and the intimate access they have to a person’s experience with health, the data scientists access to large swaths of data from distributed sources has the potential for harm and exposure of personal information. To begin a discussion on defining this ethical line and come up with a ‘hippocratic oath for data science’ the people at DataKind held an evening forum. It has left me with many thoughts on this, especially as data science becomes pervasive in all aspects of our lives (including our health), and I felt compelled to share.


The Hippocratic Oath for Data Science

Tim Rich closed the discussion beautifully with a call to action for us all: “My big beef with discussions on ethics is it ends at the door. Because we are all engaged in data science as our trade, writing down ethical statements is what needs to happen next. We can talk all day long but we need to start codifying and we need to work together by writing it down.”

While we almost certainly ended the evening with more questions than before, everyone at least left with more insight into the ethical challenges at play in our work. For further reading, Jake recommends the book, Raw Data is an Oxymoron, and we also love the Responsible Data Forum series, Data & Society as well as Data Science for Social Good fellows blog series for more stories from the forefront of data-for-good!

Some food for thought. Let me know what you think.

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