Michael Ford (post until Oct. 31/18)

The trouble with artificial intelligence

Artificial intelligence (AI) has significant potential to improve our lives and make us more efficient, Toronto litigator Sharon Bauer writes in The Lawyer’s Daily.

AI can control the temperature in our homes, help banks identify fraud, diagnose cancer and make vehicles autonomous, says Bauer, a partner with Wolfe Lawyers.

“AI can also automate decision-making such as whether we are approved for a loan, what newsfeeds we see, and what our auto insurance premiums will be,” she writes.

By analyzing huge volumes of data and recognizing patterns, algorithms progressively improve on achieving their desired outcomes without human intervention, Bauer says.

“This is also known as machine learning. The only way for the system to improve is by inputting more data, often personal data, into it,” she writes.

As the algorithms process more data, the neural networks evolve and become more complex, Bauer explains, adding we rely on AI systems to make decisions on our behalf even though we know very little about how they are made and whether they are accurate.

“There are various concerns with the development of AI,” she writes. “One concern is the protection of personal data, upon which algorithms feed. Personal data is any information [that] can identify an individual, whether directly or indirectly. Although most personal data is anonymized, sophisticated algorithms can re-identify individuals.

"It is important to safeguard privacy rights and allow individuals to determine how they share their personal information and for what purpose.”

Bauer says another concern around AI is the lack of transparency in its decision-making capabilities — it’s difficult to ascertain if the technology came to an impartial or correct conclusion if we don’t know how it reached that outcome.

The more we rely on AI, she writes, the more we need to ensure that we have trustworthy systems that protect personal information.

“Although Canada lacks explicit AI privacy legislation, we can rely on general privacy principles and seek further guidance through the European Union’s General Data Protection Regulation (GDPR),” Bauer says.

She says organizations should inform people in advance as to why they are collecting the information and what purpose it will serve.

“For example, Facebook should not use personal data it collects through its algorithms to determine whether someone is approved for a mortgage,” she writes. “If the original purpose of collecting personal data changes, new consent should be obtained, otherwise there is no informed consent.

“Only adequate and relevant data should be collected to achieve the system’s intended purpose. It is difficult to reconcile the paradox between needing a huge data set to develop accurate AI and the data minimization privacy principle.”

Data minimization is not only about limiting its quantity but also the nature of the information, Bauer says, adding even limited data may still include personal details.

Pseudonymization or encryption techniques can be used to safeguard personal identities while the risk of irrelevant information being picked up must be continuously assessed, the article says.

While transparency is one of the most important privacy principles, it is very difficult to achieve with AI, Bauer writes.

“First, individuals have the right to know how their data is being processed. Second, individuals have the right to know how, through its automated decision-making process, AI systems reach their outcomes. Gaining transparency into the process will provide transparency into the outcome,” she says.

Complicating matters is that the “black box” of AI is almost impossible to explain because algorithms naturally evolve beyond the knowledge of the developer, Bauer writes.

“Machine learning, as opposed to algorithms with rules, results in greater accuracy, however less transparency,” she says.

When organizations are entrusted with personal details, Bauer says they must be held accountable so that it's handled in a responsible and ethical manner. And those producing AI products must also be bound by the same data protection regulations as any other firm collecting and processing personal information, she writes.

“Organizations developing AI should implement privacy by design, ensuring privacy protection is built into their system,” Bauer says. “Ongoing audits need to be conducted by the organizations to ensure that personal data is protected and used lawfully. Audits should also ensure that the outcome of the algorithm is correct and non-discriminatory; this is a difficult task as indicated above.

Differential Privacy, currently used by Apple, Google and Uber, is a new technique used to protect privacy without reducing the data set. The encrypted personal data is injected with 'noise' so that sensitive personal information is obscured.

"The AI system picks up on patterns and learns information about a group as opposed to an individual. The system cannot extract personal information about a particular person, which also allows for a more secure network. While differential privacy provides security and protection to personal data, it may encourage data collectors to obtain even more data to analyze.”

Ironically, Bauer says, one way to solve its privacy and transparency issues is by using AI. She cites a recent Globe and Mail article in which Dr. Ann Cavoukian, former privacy commissioner of Ontario, suggested that if everyone had their own AI personal agent, it could learn their preferred privacy settings and conditions.

“If an application or device needed access to our personal data, our AI agent would transfer only the data required for that particular purpose, thereby minimizing the data collected,” Bauer writes. “If our data is used for a different purpose, then our AI agent would retrieve the data and a new request would have to be negotiated with our agent.”

She says one way to increase public confidence in these systems is the development of algorithms that can engage in transparent decision-making.

“Regulating AI need not limit innovation,” Bauer writes. “Instead, it can increase public trust in the system. AI has allowed us to fly drones, engage in online banking and have a personal assistant in our own home. Surely, we can use AI to find a solution that will encourage innovation and protect privacy.”

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