Why is it so hard to do AI for good?
DataKind UK has been working in data for good since 2013, helping over 100 UK charities to do data science for the benefit of their users. Some of those projects have delivered above and beyond expectations; others haven't. Duncan Ross and Giselle Cory explain how to identify the right data for good projects and how this can act as a framework for avoiding the same problems across industry.
| Talk Title | Why is it so hard to do AI for good? |
| Speakers | Duncan Ross (Times Higher Education), giselle cory (DataKind UK) |
| Conference | Strata Data Conference |
| Conf Tag | Making Data Work |
| Location | London, United Kingdom |
| Date | April 30-May 2, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
How do you identify social challenges that are appropriate for data science? How can you avoid the biggest pitfalls, and how can you ensure solutions are sustainable? The reality is that it’s easy to create biased or racist models and spin up rough-and-ready prototypes. It’s hard to build innovative solutions that are sustainable and have positive social impact. Traditional business models often don’t work when your customer base is a vulnerable community. Leapfrogging institutions doesn’t work as there needs to be genuine partnerships with those working on the social problems. Duncan Ross and Giselle Cory explore how DataKind and its volunteers have tackled these problems (and yes, they will admit to their mistakes). Join in to gain insights into how to identify the right AI or data for good cases and how this can act as a framework for avoiding the same problems across industry.
