She addresses the most pressing issues about data gathering and surveillance, and she implores her audience to question data-collection processes. Her talk raises more questions than it answers.
Key Talking Points
- Many people view data collection as a form of surveillance, but it is much less purposeful.
- Companies often lack clear goals for the data they collect.
- Lack of transparency leads to anxiousness and builds an atmosphere of distrust.
- The practice of “sousveillance,” or scrutinizing the data collectors will become more prevalent.
- People need to question why companies collect their data, how firms treat that data, and how consumers can opt-in or out of such models.
- Data collection should be more purposeful, selective and should be prioritized on transparency and trust-building.
Why do any organization collect data?
The odds truth is that most companies don’t even know the answer themselves. For their part, consumers certainly don’t know why businesses collect personal data, and this makes users anxious.
“If you take one thing away from this talk, I want you to just ask yourselves, ‘Why? Why is the data being collected?’”
Many people view data collection as a form of surveillance. However, surveillance gathers intelligence for a specific purpose. The growth of the Internet of Things (IoT) and smart homes means that government agencies and corporate entities collect data simply because they can. They posit that the data might prove fruitful someday. In some cases, organizations amass data because they haven’t thought about not collecting it.
Data science is new, so society is only beginning to understand what to do with the research. People are just starting to think about what information organizations should and shouldn’t collect. As a result, “sousveillance” – the practice of monitoring those who are observing you – is becoming increasingly important.
“A lot of sensor data is being collected with no particular purpose in mind…Why? Because we can. Just because we can. Because it might be useful, perhaps. Because we haven’t thought about not doing it.”
The voice of the people
The public wants a say in data collection. People seek a conversation with data scientists and transparency into their practices. Consider the example of a woman who decided to keep her pregnancy private from digital trackers. When she searched for pregnancy topics online, she used Incognito mode. She made no online purchases. She didn’t post about the pregnancy on social media. And when she bought large baby-related items, such as a stroller, she paid cash.
As a result of her unconventional activity, the prevailing data model flagged her as potentially fraudulent, illustrating that data science’s models can make flawed predictions. Such models can generate distrust and might even dissuade people from using digital services and devices.
“We should start with models of trust first and then think about where we’re going forward.”
Must be purposeful, thoughtful and selective
Data scientists must be purposeful, thoughtful and selective. They should allow consumers to opt-in, or out, of monitoring. This issue is topical in the European Union, which may pass new regulations regarding digital “deletion” – that is, “the right to be forgotten.”
Data science’s models for predicting intentions and decision making can be useful. But foremost, researchers should use them in a collaborative way that builds trust with the people they are monitoring.
- Thinking Fast and Slow by Daniel Kahneman
- The Five Thieves of Happiness by John Izzo
- Before Happiness by Shawn Achor
- The Prince by Niccolo Machiavelli
- Principles by Ray Dalio