AInow guides numerous life-changing decisions, fromassessing loan applicationstodetermining prison sentences.
This can result in AI systems leading toBlack people being wrongfully arrested, orchild services unfairly targeting poor families.
The victims are frequently from groups that are already marginalized.
He told TNW his tips on mitigating the risks.
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Machine learningsystems need to provide transparency.
Explainability has been touted as a solution for years, but effective approaches remain elusive.
The machine learning explainability tools can themselves be biased, says Saucedo.
Its the usual software paradigm of garbage in, garbage out.
While theres no silver bullet, human oversight and monitoring can reduce the risks.
Saucedo recommends identifying the processes and touchpoints that require a human-in-the-loop.
The aim is to identify the touchpoints that require human oversight at each stage of the machine learning lifecycle.
Ideally, this will ensure that the chosen system is fit-for-purpose and relevant to the use case.
When I say domain experts, I dont always mean technical data scientists, says Saucedo.
Accountability
The level of human intervention should be proportionate to the risks.
In many cases, an advanced system will only increase the risks.
The operators of AI systems must also justify the organizational process around the models they introduce.
You need a framework of accountability
There is a need to ensure accountability at each step, says Saucedo.
Security
When AI systems go wrong, the company that deployed them can also suffer the consequences.
This can be particularly damaging when using sensitive data, which bad actors can steal or manipulate.
If artifacts are exploited they can be injected with malicious code, says Saucedo.
That means that when they are running in production, they can extract secrets or share environment variables.
The software supply chain adds further dangers.
The consequences can exacerbate existing biases and cause catastrophic failures.
Saucedo again recommends applying best practices and human intervention to mitigate the risks.
Check out the full list of speakershere.
Story byThomas Macaulay
Thomas is the managing editor of TNW.
He leads our coverage of European tech and oversees our talented team of writers.
Away from work, he e(show all)Thomas is the managing editor of TNW.
He leads our coverage of European tech and oversees our talented team of writers.
Away from work, he enjoys playing chess (badly) and the guitar (even worse).
