Family-Match adoption algorithm fails to live up to promises

An AI-powered tool introduced to increase the likelihood of orphans and adoptive families being a good match in the USA had little effect in the states where it was used.

Developed by former social worker Thea Ramirez, Family-Match provides an algorithmically-generated 'relational fit' score on the basis of information about a child submitted by foster parents or social workers, and by people looking to adopt. It then presents a list of the most suitable potential parents for every child.

However, an AP investigation found that two states had dropped the Family-Match after initial pilots, and that social workers in Florida, Georgia, and Virginia complained that it was not useful, and that it pairs foster kids with unwilling families. The algorithm appeared to pair every child with the same set of parents, an assistant director at Virginia's social services organisation told AP. 

Florida social worker Connie Going told AP that the algorithm gives false hope to waiting parents by failing to deliver successful matches, and ultimately makes her job harder. 'We’ve put our trust in something that is not 100% useful,' Going said. 'It’s wasted time for social workers and wasted emotional experiences for children.'

State officials also noted that Adoption-Share, the non-profit that runs Family-Match, provides little transparency about how its algorithm works. Per AP, Ramirez appeared to have 'overstated the capabilities of the proprietary algorithm to government officials as she has sought to expand its reach'.