# Allison Cohen Allison Cohen (Senior applied AI project manager, AI for Humanity team, Mila) was a guest on AI with Sach, a podcast hosted by Sacha Gucciardo in Montréal. ## Episode 17: AI for Humanity: A Conversation with Allison Cohen (November 2024) Allison Cohen of Mila explains why socially beneficial AI needs slower timelines, community-defined success metrics and wider access to AI education. Page: https://aiwithsach.com/episodes/17-ai-for-humanity-a-conversation-with-allison-cohen/ > And what I've realized in doing this work is that if we are going to be successful in deploying AI tools that have the type of impact that we're looking to see, we need to kind of move slowly because we need to build trusting relationships with the community groups and individuals who will be adopting this technology > — at 7:28 (https://www.youtube.com/watch?v=N8n5nfmghlg&t=448s) > And how you measure success will end up, of course, having a big impact on how you build these tools. If you're measuring click rate, you may optimize for, um, you know, click bait titles or manipulative tactics. And we don't want to do that. > — at 10:20 (https://www.youtube.com/watch?v=N8n5nfmghlg&t=620s) > I also heard how, you know, in certain indigenous schools, there was no science teacher, for example. And so if you're not getting that kind of exposure early on or if you're not socialized to believe you can have a career in this field, there's going to be a number of people that are sort of left behind at that stage. > — at 17:30 (https://www.youtube.com/watch?v=N8n5nfmghlg&t=1050s) > But because we have decided that the values and priorities of this project are more to serve survivors of human trafficking and support them in rebuilding their lives than it is to support law enforcement agents looking to intervene in a human trafficking case, we've really defined success in terms of community trust and community uptake of the tool. > — at 20:30 (https://www.youtube.com/watch?v=N8n5nfmghlg&t=1230s) > because so much of my training coming from that social science background was around interventions that were incredibly harmful. And so I saw its potential, but I also saw AI's potential for harm. > — at 3:45 (https://www.youtube.com/watch?v=N8n5nfmghlg&t=225s) > there's a lot of interest in moving fast and breaking things, as we know, is the motto that has sort of come out of Silicon Valley. And given how how much of an impact is likely to have and given how scalable that impact really is, there's there's almost this desire to be the first mover to have secured that kind of impact as quickly as possible. > — at 6:32 (https://www.youtube.com/watch?v=N8n5nfmghlg&t=392s) > the only way to do that is to make sure you have an organizational structure or set of incentives that allows you to pause and allows you to, for example, work with someone who is specialized in AI ethics > — at 11:40 (https://www.youtube.com/watch?v=N8n5nfmghlg&t=700s) > There is, of course, plenty of controversy around emotional interpretation using artificial intelligence, because what kind of data set are you using and does it predominantly contain instances of white men and women, for example, or men and women from Canada, but then can't generalize to other cultures where emotion might be expressed very differently. > — at 23:23 (https://www.youtube.com/watch?v=N8n5nfmghlg&t=1403s) > So this is sort of when you start peeling back the layers of whether the tool is going to be human rights respecting in practice, whether it is really AI for humanity or whether it is disguised as being socially oriented, but in reality creates harms that are much more detrimental than the benefits. > — at 24:29 (https://www.youtube.com/watch?v=N8n5nfmghlg&t=1469s) > maybe, you know, the technology won't replace these professionals fully, but maybe it actually can drive down the cost significantly so that more people can gain access to important services. > — at 25:37 (https://www.youtube.com/watch?v=N8n5nfmghlg&t=1537s) > But I think it's always important to make sure that we are staying very critical of the narrative that's being put forward. Because, again, we know how exclusionary the field is, and we know how exclusionary it is to sort of be the spokesperson for the field. > — at 26:54 (https://www.youtube.com/watch?v=N8n5nfmghlg&t=1614s) > I'm thinking about all the examples of the exploitation of data workers and the fact that that's sort of been swept under the rug for a really long time. But I really hope that people start to get more engaged in the field and the barriers to entry start lowering because it's going to continue to have such a dramatic effect on our world. > — at 27:27 (https://www.youtube.com/watch?v=N8n5nfmghlg&t=1647s) > I think, to some extent, there's something somewhat Canadian about wanting the technology that we're building to be incredibly human rights respecting. > — at 4:31 (https://www.youtube.com/watch?v=N8n5nfmghlg&t=271s)