New Toronto Declaration Calls On Algorithms To Respect Human Rights
A coalition of human rights and technology groups released a new declaration on machine learning standards, calling on both governments and tech companies to ensure that algorithms respect basic principles of equality and non-discrimination. The Verge reports: Called The Toronto Declaration, the document focuses on the obligation to prevent machine learning systems from discriminating, and in some cases violating, existing human rights law. The declaration was announced as part of the RightsCon conference, an annual gathering of digital and human rights groups. "We must keep our focus on how these technologies will affect individual human beings and human rights," the preamble reads. "In a world of machine learning systems, who will bear accountability for harming human rights?" The declaration has already been signed by Amnesty International, Access Now, Human Rights Watch, and the Wikimedia Foundation. More signatories are expected in the weeks to come.
Beyond general non-discrimination practices, the declaration focuses on the individual right to remedy when algorithmic discrimination does occur. "This may include, for example, creating clear, independent, and visible processes for redress following adverse individual or societal effects," the declaration suggests, "[and making decisions] subject to accessible and effective appeal and judicial review."
Beyond general non-discrimination practices, the declaration focuses on the individual right to remedy when algorithmic discrimination does occur. "This may include, for example, creating clear, independent, and visible processes for redress following adverse individual or societal effects," the declaration suggests, "[and making decisions] subject to accessible and effective appeal and judicial review."
That's the big issue with big data. And the danger is that perceived "racism" will be corrected with "affirmative action": by verifying the AI using statistics on the outcome, and applying a bias.
People with certain economic characteristics are statistically more likely to default on a loan. What if the AI applies such strictly relevant data to approve or reject loan applicants, but the rejected group happens to be predominantly of a certain race? Verification of the AI will show a (non causal) relation between race and loan applicant score, and since we don't know how the AI arrived at its decision, people will assume racism. Will the bank be forced to correct for this and extend loans to people of this race with terrible credit scores, just to make up the numbers?
In cases where we are worried about racism, perhaps AI simply isn't practical, and we're better off judging each individual case ourselves on imperfect but clearly defined criteria that are free of undesirable bias.
If construction was anything like programming, an incorrectly fitted lock would bring down the entire building...