Foteini Agrafioti is the chief science officer of the Royal Bank of Canada and the head of Borealis AI.
At the Artificial Intelligence, Ethics, and Society conference in New Orleans last month, machine learning researchers presented a program that uses neural networks to classify potential gang activity from within a database of criminal behaviour. The algorithm was designed in the hopes of helping authorities automate the process of identifying gang-related crimes, giving police a sense of whether there may be any retaliatory activity after a crime. This advance knowledge would ideally provide them with a window of opportunity to curb further violence.
These predictive measures are meant to provide intended outcomes. But the tool’s creators surely didn’t intend the outcome that did emerge at the conference: swift backlash. Critics suggested the algorithm’s potential to erroneously label individuals as gang members could ruin lives and contribute to a growing sense of mistrust against police. When grilled over these potential misuses, the Harvard University computer scientist who presented the work waved the question off, saying: “I’m just an engineer.”
Statements like this from within the machine learning community reveal a divide over ethical responsibility. On one side are researchers who see their work as a function of pure scientific inquiry that should be allowed to advance without interference; on the other side are those who loudly demand that the scientists and companies building today’s AI technologies have an obligation to consider the broad and long-term impact of their work. Most fall somewhere along this spectrum.Read More