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Artificial Intelligence is transforming our world, sometimes in ways that its creators did not intend. In Wellcome Data Labs we are developing a new method of applying approaches from the social sciences to the way AI algorithms are produced to solve data science problems. The goal is to avoid potential negative consequences of the algorithms by identifying them earlier in the development process.
There have been attempts to set out such a way of working already. An example is Catalina Butnaru's excellent post proposing a new Agile ethics process. There is much to recommend this approach, not least that it is systematic and aligned closely in its steps to well-known steps of agile software development methodologies.
Butnaru - Mechanics - Agile - Ethics - Process
However, Butnaru does not address the mechanics of how her suggested Agile ethics process could be managed. Is it the team of data scientists and engineers themselves who are responsible for following the steps? Or their product manager? Or the UX team? Or a separate team to the engineers that audits their work?
We have been thinking about such questions a lot, since we are keen to test out how ethical approaches can be applied to the work of data scientists in practice and not just in theory.
Challenge - Process - Butnaru - Methodologies - Way
The key challenge we set ourselves is: how to apply a process such as Butnaru's, or one of the other rival methodologies, in a way that measurably reduces ethical issues, like inadvertent bias, but does not reduce the energy and effectiveness of our Agile product teams?
We think this can be done by encouraging social scientists to work as part of interdisciplinary teams with software developers and data scientists, adopting their agile and iterative methodologies.
Challenges - Example - Difficulty - Science - Researchers
I have outlined some of the challenges of doing this. For example, the difficulty of getting social science researchers to work at the same speed and to...
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