As data scientists, we often treat the data given to us as the raw input. We seldom question where the data came from and how it was collected. Where did these pre-existing inherited biases come from, and can they be eliminated?
Emergent biases are not inherited from the training data. That means these biases can still emerge even when we have trained the machine-learning (ML) algorithms with a cleansed data set with no known biases.
AI bias problem is currently being thought of as a data problem. And to a large extent, if we can fix the biased data, we would’ve addressed most of the AI biases. However, not all biases are bad. In fact, biases are often introduced in training data to improve the performance of the trained model.
Today, most AI practitioners in the industry are treating AI bias as a data problem. To a large extent, if we can fix the biased data, we fix the AI bias problem. However, fixing the biased data is not the only way to address the problem.
PROS has fortified its market position through a combination of common moats. With a renowned brand, proprietary AI, strategic customer relationships, and robust R&D investment, we stand as a formidable player. But PROS also has a very distinctive moat – the unique synergy between our Travel and B2B.
The simple and fundamental truth is that not all moats are created equal. The traditional barriers to entry, such as brand recognition, network effects, and economies of scale, while effective in their own right, are still vulnerable to the relentless march of time and competition. Nvidia and TSMC had developed highly unique moats.
Most successful companies that stand the test of time usually have some kind of moat. Cost (economy of scale, efficient supply chain, low-cost supplier, efficient operation), intangible assets (brand, regulation, IPs), and switching cost (network effect, integration complexity) are some common moat strategies.
Have you ever experienced a time when you were so immersed in what you were doing that you forgot about your physical feelings and the passage of time? This highly rewarding mental state is known as flow, and it is studied and characterized by a renowned psychologist Mihaly Csikszentmihalyi.
Today, XAI is proving that the infamous “black box problem” is really not a problem after all. Business leaders who continue to scapegoat the use of AI due to its black-box nature are essentially foregoing an efficient and reliable way to optimize their business decisions for something that is only a problem of the past.
Barry hosts a popular podcast where he interviews thought leaders, industry experts, and successful entrepreneurs, delving into a wide range of topics related to leadership, innovation, and personal growth. Today, I’m one of the few lucky ones who had the opportunity to talk to Barry and share some of my own unlearning moments.
What makes a black box model uninterpretable is just complexity, nothing more! And the black box problem is an inherent inability of our human brain to distill those complex models down to something simple enough for us to explain in English.