What is artificial intelligence, machine learning, and deep learning? Why does it matter to humans and industries, and will robots take our jobs? Why is it that we can't rely only on the experts, data scientists and machine learning engineers to build the perfect AI products, and expect them to solve the bias in AI issues? How do we ensure that we're building AI for good?

My background in digital taught me to measure project performance, analyze market and competitive data and provide actionable insights. As a result, I deliver growth strategies, revenue and profit opportunities in line with top business objectives. 

Data is gold in optimizing business efficiency and driving growth; it is crucial to have a data strategy so enough data is captured in business processes and scrubbed so it can be used for analysis, or for training the most accurate AI models.

But then, I realized: data is not everything, and algorithm is not king.


Algorithms are taking over human decision making. In the future, our lives may depend on algorithms working right and real-time, for example, in self-driving cars. And accurate algorithms are trained by massive amount of data. The more data you have, the better representing of your data set is to best find the patterns.

However, we need to understand that there won’t be a “known” and “good” pool of data. It has been exposed numerous times by researchers, that datasets can be laced with bias, because they are curated by people, and whoever curates the datasets injects their bias into the datasets, often unknowingly.

If trained algorithms and AI go on to become a product, many of them will come with the “black box” baggage. Things may go wrong, and the users or consumers have no where to turn to for justice - because the algorithms are faceless.


Humans need to be in the design and decision process to make sure the results are fair and ethical.

Business leaders need to make sure they have the right mechanisms to harvest or generate the right/neutral data. There are also research project around injecting noise to effectively anonymize people’s data while still retaining the valuable information that helps program the system.

The machine takes into the data, gives a recommendation, but if there is a human layer as the ultimate goalkeeper, we can create a more transparent system.

Ongoing testing and training are also needed to make sure the results stay just and fair.

Besides being part of the design, humans need to monitor and supervise these systems so they are a positive part of our futures.