3. Responsible AI becomes a priority
Nearly every industry is adopting AI and machine learning. But ethical questions arise as technology becomes increasingly pervasive behind the scenes in our everyday lives.
The challenge of AI implementations at scale? At the outset, it works based on the available data and human understanding. But as new data enters the equation, the models change. If left unchecked, they can create unintended consequences – some obvious and others not yet known.
Take dating apps as an example. Millions of people use online dating apps powered by predictive models, which users train with each swipe left or right.
But at what point do humans oversee the self-learning algorithms powering decision-making processes? And when should regulators intervene to prevent potential biases or other consequences from the misuse of AI? Essentially, we need controls and measures to manage technology effectively, a framework for responsible AI.
Enterprise leaders can address this challenge by upskilling their workforce in data science techniques. By democratizing data across the organization, every employee can tap into the treasure trove of data at their fingertips, producing insights that show management how to lead a digital transformation at scale.