Cutting-edge algorithms and new research will continue to drive the advancement of machine learning. However, there’s a more straightforward way of resolving many challenges in machine learning today, especially when it comes to ethics and better alignment with human values. And it is not surprisingly, not focused on the technology, but rather on the people using it.
For advanced AI and machine learning systems already in production, the focus is on delivering the intended value of the system, which is no longer a question of leveling up the technology or mathematical techniques behind it. Value-oriented approaches can be supported holistically by both the human expertise involved in creating AI and by the technology itself.
Traditionally, machine learning is not reliant on human intuition and how humans approach processes. That is by design. Machine learning algorithms instead are designed to pick up patterns in the data, oftentimes without many baked-in constraints or assumptions about underlying relationships within a dataset. Pattern recognition is the greatest strength of these algorithms, but at the same time, it is a potential weakness too. Machine learning algorithms may blindly exploit corners of the data that are not helpful for the real-life application, in service of maximizing accuracy on ...
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