The Balancing Act Between AI, Data Privacy, and Customer Trust
By Spiros Margaris (Margaris Ventures)
Privacy is a complex thing. We all want to control our privacy and protect it from others: we may not want them to know or be able to share intimate things about our lives. However, the funny thing is that a lot of people do not mind sharing their data throughout their digital journey, even though this can be much more intimate and telling regarding who they are and what they like. In other words, many people do not mind companies knowing their intimate secrets or desires, but often mind other people knowing the same information.
Globally, regulators have taken action or are planning to take action to protect people — whether they care about their privacy or do not understand why their digital footprint needs protection. In 2018, the European Union introduced the General Data Protection Regulation (GDPR) law, creating a playbook which other countries can follow to protect the privacy and personal data of their “digital citizens.”
As we seek more personalized services from tech giants, both individuals and companies face a dilemma. Such services are only made possible by collecting our personal data and analyzing it with sophisticated artificial intelligence (AI) models. How do companies provide the increasingly personalized services their users demand and still comply with strict privacy regulations?
By and large, companies want to deploy AI to advance their business while also complying with regulatory privacy laws and trends. Businesses are now trying to address how consumer data can be used while also protecting the privacy of the people who provide it. For instance, IBM data fabric solution provides a holistic view across hybrid and multicloud to deliver trusted outcomes.
Governments are also interested in advancing AI adoption and development, with many studies indicating that this significantly advances economic growth. So, alongside regulating and protecting consumers through laws like the GDPR, it is essential to create an environment in which companies can innovate and deploy AI models. This will enable companies and governments to compete successfully on a global scale and grow their economies.
However, balancing both interests — protecting consumer privacy and advancing AI adoption — is difficult. Not to mention the need to constantly adapt policies in line with global competitive trends.
Moreover, for companies that want to advance their business model to unlock the capabilities and benefits of AI and machine learning (ML), it is essential to collect as much data as possible. Any strict regulatory measures — regardless of how well-intended — could slow down or even stop such initiatives. Therefore, these measures may reduce companies’ abilities to successfully compete — locally and globally.
As much as companies feel that privacy laws and data restrictions are bad for business, there are benefits beyond consumer protection that can indirectly benefit their AI efforts and build customer trust.
All participants — governments, companies, and consumers — benefit from AI and ML regulation. As seen with the GDPR provisions, regulations set a framework that builds a comfort zone of trust. This comfort will help AI advancement in the long term — even if it occurs at a slower rate than it would without regulation. It will also promote adoption by businesses and consumers.
Regulation which slows down AI progress will give all participants time to adjust to the changes and impacts it will have on society. Consumers need time to take in these changes to build trust. These regulatory measures must be in the interest of all participants, as it helps build trust in AI and, therefore, for countries to compete successfully. At a company level, bad implementation experiences should not diminish the benefits of these technologies.
For companies to collect and use data from various sources while complying with privacy laws like the GDPR, they need technology solutions that protect the consumer while enabling a successful AI strategy. For instance, European companies must comply with GDPR principles, such as lawfulness, fairness and transparency, purpose limitation, data minimization, accuracy, storage limitation, integrity and confidentiality, and accountability. Such strict privacy laws present quite a challenge for companies to overcome while still innovating and advancing their AI strategies. However, overcoming this challenge is the only path for companies to succeed and win customer trust.
The future of AI and ML will need to evolve in tandem with global privacy laws. Companies will need to look out for technological solutions that comply with privacy laws and future policy trends and implications. The AI industry also needs to keep educating the public and policymakers, to ensure that the ongoing AI journey is in the interest of everyone.
Smart companies know that operating a forward-looking, data-driven business model requires adapting to society’s needs and upcoming regulatory changes. The ones that are best at anticipating these forces and adjusting their business model accordingly will have a head start on their competition.
I believe that we can advance AI to its full potential while also protecting individuals’ privacy rights, gaining customer trust, and enhancing human lives. It will not be easy, but to achieve this goal, we have to aim high and give it everything we have, even if at times it feels like aiming for the moon.
I would like to end with an inspiring quote by John F. Kennedy given at Rice University on September 12, 1962.
“We choose to go to the Moon in this decade and do the other things, not because they are easy, but because they are hard, because that goal will serve to organize and measure the best of our energies and skills, because that challenge is one that we are willing to accept, one we are unwilling to postpone, and one which we intend to win, and the others, too.”
