Marnie Chesterton

Okay.

Yeah, so this saves researchers time, and then it improves tracking of, you know, elephants, lions, rhinos.

Okay, this sounds good.

Yeah, I mean, it is good.

I mean, I obviously want to point out that it's not all good news, but this is part of the good news, at least.

I had a feeling that there was a but coming, Tristan.

Well, as you mentioned, the AI has environmental impacts and these can fall disproportionately on indigenous peoples.

So AI data centers rely heavily on critical minerals.

It's estimated that 54% of transition minerals are located in or near indigenous territories.

And those are minerals that are used for things like data center construction, EV batteries, making smart weapons, smartphones, etc.

They're critical to essentially the data infrastructure that we use today.

And because they're on indigenous territories or nearby, it often drives things like land grabbing, displacement, and biodiversity loss.

But there's also this really big issue that folks are talking about, and that's data governance.

So indigenous peoples typically maintain that indigenous data is collectively owned.

And existing national and international data privacy laws generally fail to recognize this.

So, for example, the General Data Protection Regulation of the European Union prohibits

It protects individuals, but it doesn't recognize collective ownership.

So as a result, companies can make technical use of data originating from indigenous territories like satellite images or climate and language data.

And they can do this without any obligation to obtain community consent.

So it's a really big topic of conversation now and looking at that sort of double-edged sword of AI use.