Depending on who you talk to, artificial intelligence (AI) is either the most exciting and transformative technology in our lifetime or the most dangerous and troubling. 

Science fiction has shaped some of these AI worries; movies like I, RobotThe Matrix and the entire Terminator franchise are built on the notion that machines can become conscious and independent in their thoughts and actions — an understandably alarming idea. But I think that the casual way we talk about AI in pop culture contributes to unfounded fears about the machines taking over and distracts from valid concerns about privacy. 

It’s helpful to shift this conversation from AI as a whole to the more specific domain of machine learning (ML), and how we can harness its advantages and minimize its drawbacks. 

How We Use Machine Learning 

Machine Learning is a subset of AI that describes how computer algorithms get better at solving their domain specific problems by leveraging large data sets with which to train themselves. ML refines and improves as it encounters new data, making deductions, learning and adapting. This process simply makes any software operate increasingly better over time but doesn’t suddenly make the software self-aware, à la Skynet. It’s more like a self-sharpening knife that works more effectively with each use. And the point is, the knife does not start to throw itself at you, but you do want to use it carefully so you don’t cut yourself unnecessarily. 

Over the last 20 years, machine learning has permeated every industry and become an integral part of your everyday life, including: 

 Financial services: anti-fraud and money laundering efforts

 Medical systems: image clarification and scanning

 Marketing and advertising: analyzing segmentation, impressions and placements for campaigns

 Search engines: query classifications, spelling suggestions and results ranking 

 Streaming video and music platforms: recommendations tailored to your interests

 Social media: algorithms to deliver content with whom you’re likely to engage

 Voice assistants: voice recognition to determine if you are issuing a command

 Maps: optimal routes between destinations and estimated commute times

The Pros And Cons Of Machine Learning

The reality is that the world around you is augmented by machine learning. When leveraged by reputable companies with high integrity and consumer data protection practices, ML makes life easier and safer.  

For example, consumer spending is a highly analyzed field. When you buy something with your credit card, ML-powered software compares it to your purchasing history. Learned patterns help protect you from theft, flagging unusual activity for further scrutiny and preventing someone from using your card for fraudulent purchases. 

Machine learning can also be used to safeguard your sensitive medical data, filter out spam in your inbox and personalize your entertainment and news feeds. Even ML-driven ad campaigns aren’t necessarily harmful for consumers. They may result in you seeing ads for products and services you actually want vs. generic ads you promptly ignore. If companies get to know your personal preferences and behaviors and use this data to delight you and meet your needs, this arrangement is a win-win. 

But some companies will try to take advantage of their access to your personal information. Machine learning processes all of the data a model has access to — from your browser history to the contents of your email to how much time you spend on certain websites — to make inferences. As a consumer, you need to decide when this is OK with you and when it isn’t. 

As emphasized by the recent Netflix documentary, The Social Dilemmawe are the product being bought and sold. Predominantly and predictably, ML is used to fuel capitalism and not altruism. It’s up to you to protect your privacy and well-being, since that is not the primary goal of most capitalistic endeavors. A good rule of thumb to remember is: the more businesses know about you, the better equipped they are to separate you and your money.

Here are three steps you can take to get the most out of machine learning’s benefits, while protecting yourself against its potential privacy violations. 

Know What You’re Sharing

Your personal data is fodder for machine learning, but are you aware of who has access to what? Most consumers are naïve to how much tracking companies do through your phone, computer, voice assistant and other devices. 

Make it your business to know which businesses you have implicitly or explicitly given permission to access your data and activity. How transparent are they about their privacy and data usage practices? Are they using your information to optimize and improve your experience or solely for their financial gain? Be skeptical; very little of this powerful technology is used to protect and empower individuals, families or small businesses. 

Implement The Strictest Privacy Controls

Review the privacy settings on all of your devices, browsers, email accounts and online profiles. For many, the default setting is not as strict as you would choose for yourself. Switch settings to the most conservative levels. If you find it’s too extreme, you can always loosen them later on. For instance, you may decide to unplug or mute your smart speaker when not in use, or restrict or delete personal information on your social media profiles. 

Check In Often

Stay up-to-date on which companies have permission to use your data and what their current consumer protection policies are. Aim to do a privacy audit about once a quarter, and don’t be afraid to break up with companies that don’t meet your standards. 

Machine learning is responsible for a long list of improvements in our lives. We can celebrate these advancements and still push for more control over how and why our personal data is fed to the machine. 

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