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Could artificial intelligence improve decision-making in refugee cases?

In theory, under the right legal conditions, predictive technologies powered by artificial intelligence (AI) could help ensure that fewer refugees are sent home to face persecution. For example, the Canadian government has been experimenting with the use of AI in immigration assessments and decision-making for a number of years. However, the right conditions do not yet exist in Canada and, under the law as it stands, AI will only hurt claimants. To understand why, it is necessary to understand the role that uncertainty plays in refugee hearings in Canada.

Under Canadian law, claimants must prove each of the assertions in their application for refugee status. If the decision maker is unsure as to whether an assertion is proven, they will reject it. If the decision maker is paralysed by doubt – “I’m not convinced that I should accept this assertion, but I’m also not convinced that I should reject it” – the law basically says: “Forget your second set of uncertainties. Since the claimant bears the legal burden of proof, only your first set of uncertainties matters. If you are not convinced that the assertion is proven, you should reject it. Full stop.”

To see this in action, imagine that the decision maker accepts that the claimant’s ex-husband wants to kill her and that he will be able to find her if she returns home. The remaining question from a legal perspective is whether the police will protect her.

Under current legislation, the claimant must prove that the police will not protect her. If evidence on this point is scarce, or partial or conflicting, and the decision maker is unconvinced, she loses.

The only doubts that matter are doubts about the assertion that she is trying to prove, that the police will not protect her. If the decision maker has equivalent doubts about whether the police will protect her, it doesn’t matter – these doubts are legally irrelevant.

In a system like this, any uncertainty hurts the claimant. This kind of decision-making model resolves doubt at the claimant’s expense and errs on the side of rejection. But the drafters of the United Nations’ 1951 Refugee Convention were clear that doubt should resolve in the claimant’s favour.

Like criminal law, which has long recognised ‘Blackstone’s ratio’ – “it is better that ten guilty persons escape than that one innocent suffer” – the convention makes clear that it is, by orders of magnitude, a worse mistake to send a person home to persecution than to offer protection to someone who does not need it.

A different kind of decision-making model, one that would respect the convention’s error preference, would have the decision maker compare the assertion being put forward by the claimant (the police will not protect her) and the counter-theory (they will protect her) and decide which is more persuasive.

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