The Canadian Bar Association
Privacy

Privacy and ethics in an algorithmic age

It’s critical for businesses and public organizations to adopt a person-centred privacy model that reflects an ethical approach to using data, says Vancouver privacy and information lawyer Sara Levine.

“Organizations that use information are moving away from simply utilizing a checklist model for privacy compliance towards a deeper analysis about how to run a business ethically,” she tells AdvocateDaily.com.

"Until recently, the rules and compliance were the main focus around privacy and now the person is back in the foreground.”

Levine says companies are starting to place as much importance on privacy as they do on issues such as employee human rights. Privacy “is no longer relegated to a procedure or whether ‘we have a consent form,'" she says.

Organizations are grappling with wider questions such as, “Do my customers and clients understand what we’re doing? How can we be fairer and more transparent? How do we respect the individual behind the data?” she says.

In the past, privacy has been guided by "fair information principles," Levine says. The focus over the last 15 years has been on how businesses can operationalize those principles by establishing processes for consent, accountability, safeguards, and limiting use, disclosure and retention.

“The thinking has been that if businesses follow the rules, they are complying with privacy,” she says. “But what got pushed into the background was the idea that privacy is about fairness for people. Now there is a push to recognize that."

Regulators, advocates, academics and society are looking at privacy more closely because of the intersection between “Big Data” — a term referring to the vast amounts of new information being generated daily — machine learning and predictive analytics.

Business and government routinely collect, link and study data to identify previously unidentified facts about the past, such as when the retailer analyzes all its sales data to identify the most popular products, or a government health department tracks the progress of a disease. 

But now, with the advent of huge datasets and new, complex algorithms, it is possible to link multiple types of data drawn from enormous datasets to make predictions about the future. It’s called "predictive analytics" and is often used for marketing purposes. But it can also be used to determine such things as whether one individual — or segment of the population — will live longer than another, is more creditworthy, or poses a higher risk of recidivism.

”The thing is, these algorithms are created by people, and everyone has implicit, often unrecognized, biases,” Levine says. "This can result in unfairness for the individual if the prediction is wrong,” she says. “And sometimes, even if the prediction is right, it can still be unfair if it affects a human right.”

Discussion about this has been ongoing for a few years now. Levine points to a 2012 Office of the Privacy Commissioner of Canada report that highlights an overall fascination with using analytics to predict how people will behave in the public and private sectors.

But in the last year, there has been increasing public recognition that the issues can’t be resolved unless "we start thinking about privacy as more than just rules to be applied in a bureaucratic fashion," Levine says. 

“Because computers can perform analysis and make predictions about people by compiling data from many sources, the person can get lost in the data. We have to remember this is about individuals and it's paramount to protect their dignity, autonomy and human rights.

“Rules without the meaning are pointless,” she says. “They have to be discussed and applied with a strong ethical lens because these decisions affect large groups of people and individuals. This impacts individual freedom and our democracy. Big questions are being asked around privacy right now.”

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