To some modern day consumers, “Big Data” sounds a lot like “Big Brother.” Several companies have exacerbated these fears by prioritizing predictive accuracy over privacy. For example, in 2012, Target correctly predicted a teenage girl’s pregnancy before she told her father. Target had assessed the young woman’s purchase data and mailed her a range of baby product promotions—much to her father’s chagrin. While the product promos may seem innocuous, consumers were unnerved by Target’s tactics. In the years that followed, predictive analytics became subject to increased ethical scrutiny. In fact, major companies, including Google and Salesforce, have gone so far as to hire a “Chief Ethics Officer.”
This role may include ensuring regulatory compliance or even inspecting AI algorithms for bias. The rise of this job title signifies that companies, in addition to consumers, are taking data ethics seriously. But it’s important to also highlight the positive potential of predictive analytics. More than a dark twist in a sci-fi movie, it can help organizations detect fraud, optimize marketing, manage resources, and reduce risk. In some cases, it could even save lives. Government regulation might be able to assuage ethical concerns while helping predictive analytics along its trajectory.
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What is Predictive Analytics?
Predictive analytics uses historical data, algorithms, and machine learning to identify the likelihood of future outcomes. This is not a new technology—the U.S. Census Bureau has been using it for decades to uncover population data trends. But today, predictive analytics is becoming a modern business staple. Predictive analytics is already being used to aid marketers with lead generation and to predict the major life events of retail customers.
Beyond that, the technology has revolutionized industries such as banking, insurance, healthcare, and manufacturing. And we’ve only scratched the surface of its potential. In healthcare, for instance, it may soon be used to develop genetically-precise medicines. Predictive analytics may even have applications in social work and child welfare: it could save lives by reducing the time it takes to determine the need for an investigation.
The sheer complexity of predictive analytics presents inherent challenges. Large data pools are vulnerable to cyber-attacks. A lack of transparency could lead to algorithmic bias or discriminatory practices. Above all, consumer data must be protected to ensure privacy. Government regulation could effectively address these concerns, but would it stymie innovation in the process?
The Case for Government Regulation
Existing data regulations—including the EU General Data Protection Regulation (GDPR)—hinge upon the idea of consent. All companies and organizations with EU customers must notify their audience of their data collection and its use. Customers must then either provide consent, or opt out. Professor Dennis Hirsch, head of the Data and Governance program at Ohio State University, explained that the same framework can’t apply to predictive analytics. For example, people may not be aware when checking the box that gives their consent that their retail purchase history could be used to extrapolate sensitive personal information. Hirsch believes that predictive analytics ought to be regulated to protect privacy and discourage manipulation, bias, and procedural unfairness. In his view, regulation may even lead to a boon for consumers and organizations alike.
Regulation Protects Consumers
Merely asking for consent is not enough if consumers do not understand the conclusions that can be drawn from their data. Hirsch suggests that an “expert agency,” similar to the FDA, can evaluate these cases on behalf of consumers and design a framework to safeguard consumer data and ensure privacy. In addition to protecting the data itself, regulation could help eradicate manipulation and bias. If left unchecked, a biased algorithm could unfairly reject certain candidates for loans or insurance coverage. Such predatory behavior would cause grave social harm.
Regulation Safeguards Brands & Institutions
Regulation isn’t just good for consumers—it could give brands a boost as well. A Deloitte study on consumer trust and data protection confirms that data privacy and security is more than an issue of risk management. In fact, it’s a potential source of competitive advantage that could encourage brand-building and improve business reputation. Regulation may validate brands and institutions that leverage predictive analytics, thus building trust with consumers.
Regulation Can Foster Innovation
Contrary to popular belief, regulation isn’t always the enemy of progress. By reframing predictive analytics as a legitimate and transparent business practice, regulation could actually encourage innovation. It would legitimize predictive analytics in consumers’ eyes, thereby opening new application avenues. We might even see more organizations offering predictive analytics as a service. Regulation could also necessitate new technologies for authentication, encryption and anti-artificial intelligent bots, which could be key to sustaining the digital ecosystem.
What the Future Holds
Data ethics, consumer data protection, and security regulations are here to stay. GDPR is in full effect, and similar legislation, such as the California Consumer Privacy Act passed in 2018. Predictive analytics will likely be subject to specific regulation in the near future. While regulation will inherently limit its scope, it will also help rewrite the narrative. The ultimate goal of predictive analytics is to help people—whether it’s used to share a relevant product catalog, qualify the right candidate for a loan, or predict who may be at risk for chronic disease. It follows that organizations should innovate while protecting the people they hope to serve. Ethical practices will aid the advancement of predictive analytics regardless of the regulations that follow.