As an organization focused on using data science to support humanitarian issues, DataKind knows that ethics are of the utmost importance. In this blog, DataKind UK outlines the ethical principles their community has created. Read on and see what you think. Is this a code you’d adopt? What would you add? Join the conversation by leaving comments below and stay tuned for more on this topic.
Guest blog: Christine Henry and the DataKind UK team
"Ethics is knowing the difference between what you have a right to do and what is right to do." - Potter Stewart
As data scientists volunteering to help nonprofits, we hope that our work will have a positive impact on those around us. However, in the new frontiers of data science and artificial intelligence, it is sometimes difficult to know what right and wrong looks like or what the impact of our work will be. We can all agree that we don’t want to discriminate against people, but we also recognize that, in data science, labelling and categorising types of people and types of behaviour is at the heart of what we do. At DataKind, ethics around data and technology takes on an even more critical and serious note when you consider that the projects we work on are often about, and for, the most vulnerable populations in our society
DataKind’s projects often lead to a nonprofit partner reallocating scarce resources (money, food, or even advocacy and attention), and this may mean that some groups will go unsupported. The “do no harm” principle is not as simple to apply in our work. Our job is often about minimising harm and maximising positive impacts, rather than avoiding harm all together. And sometimes doing nothing is not necessarily better: a charity’s mission can be furthered by data analysis even if the analytical project is imperfect.
At DataKind UK, we’ve been thinking about some of these tough ethical questions. How do we ensure that the predictive models we build don’t have unintended consequences - and can we ever be sure of that? How can we assess the benefits of implementing an algorithm versus the possible risks? How do we ensure that we don’t allow these ethical challenges to prevent us from taking action when the status quo is worse?
We believe the best way to act ethically as an organisation is to directly confront these hard ethical questions and to support open, frank discussions. With input from our pro bono data scientists, we put together a set of principles to guide these discussions. The principles will help us think about risks within our community and share these concerns with our nonprofit partners. In creating the principles, we focused on understanding potential harms, looking carefully at data context and biases, and being transparent about analysis limits and the reasons for analysis choices.
See the principles we outlined below and learn more about how we crowdsourced these from our community.
On an evening this past October, we brought together 20 members of our volunteer community, plus a couple of DataKind friends and ethics experts. The brilliant Alix Dunn, Founder and Executive Director of the Engine Room, and conveniently the partner of DataKind UK’s Executive Director, adeptly facilitated the event. For Alix’s reflections on the discussion see here and check out the Responsible Data Forum here.
Rather than start from a blank page, we decided to “seed” the workshop with samples of other related documents that participants could take ideas from or react to. We selected half a dozen sets of principles from different fields and professions (e.g. government, corporations and academia).
Working in small groups, people pulled useful principles out of the sample documents, or hacked their own variants. We also supplied short (anonymised) case studies from past DataKind projects that included possible ethical issues, to help groups think about the real world application of the principles they were discussing. Lastly, we pulled together everyone’s principles into one shared document which, after some heavy editing, turned into the five principles outlined below.
We will begin rolling out the principles to volunteers starting new projects, and track ethical issues raised and what happens. Our volunteer-run Programmes Committee will also look to start building any required processes – for example, a tracking document for issues, identifying someone for volunteers to contact with ethical issues on a project or general level, and updating our existing scoping process to identify ethical issues at an early stage.
This is intended to be a living document for the DataKind UK community and anyone interested in ethical data science. The principles will be updated and adapted as necessary in response to future changes in data science practice; development of ethical standards in the broader data community; and the needs of charities, stakeholders or our community. We hope that the principles we outlined can be an example or starting point for other organisations and data science practitioners as well.
As a Datakind UK volunteer, I will strive to adhere to the following principles: