These were some of the questions tackled by panelists from academia, government, and industry at a workshop hosted by the LMI Research Institute (LRI). Operationalizing Data Ethics: Considerations for the AI/ML Development Lifecycle explored how data ethics principles may be implemented, enforced, and institutionalized to mitigate risks such as inherent discrimination and the misuse of data or analytical outcomes.
One consensus is that data governance frameworks must be adapted to remain relevant and applicable as AI/ML techniques bring about changes in analytical environments.
The work in this field, said Arlyn Burgess, associate director of operations and strategic initiatives at the University of Virginia School of Data Science (UVA SDS), “transcends traditional disciplinary boundaries to discover new insights, often by combining disparate datasets that would not likely be brought together otherwise.” She explained how helping students learn to view their work through an ethical lens is part of the school’s mission.
Horio moderated a panel with Jessica Young from the National Security Commission on Artificial Intelligence; Davey Gibian, chief business officer at Calypso; and Joe Norton, LMI’s director of data visualization and product development.
During Q&A with the audience—predominantly senior decision-makers from federal agencies—panelists discussed how organizations can leverage culture to promote ethical frameworks and how analysts should annotate algorithm-produced recommendations.
“I think the takeaway here is that there’s not necessarily a reinvention that we have to go through as far as ethics and data,” said one workshop participant. “As the field continues to grow, we’re realizing we need to build these things in. We’re not reinventing the wheel. We’re just pausing to think about the ramifications and all the other pieces that go with the tradecraft of data science.”