"What AI can teach us about human bias in decision making"

"What AI can teach us about human bias in decision making"

The collective power of the weak is mightier than that of the strongest individual. If only things were that simple. We are all human. We have limited knowledge. We may understand others well if they have had similar life experiences to ours. Human psychology tricks us, too. It makes us want to be around people who are like us — people with whom we can sympathize. That is all very innocent until you consider the cost to society. 'The lack of women's participation in clinical trials is the result of a long-held belief that the male perspective is the norm.' Gatekeepers, including doctors, surgeons, policy makers, and medical researchers, are predominantly male. Their viewpoints have become entrenched in the way we approach medicine. If you do not actively pursue diversity, the status quo will remain. That is just how decisions are made. The way we see the world is shaped by our personal experiences. Observable traits, such as race, ethnicity, gender, and age, are the starting point of diversity of thought.

However, the most compelling explanation can be found in the field of artificial intelligence and the algorithms associated with it. An approach called the ensemble method deserves mention here. It essentially harnesses cognitive diversity in the world of AI. The ensemble method works by dividing an existing dataset into small samples, which can then independently train different algorithms at the same time. In these smaller sample sets, these algorithms tend to be weaker. They make more errors in their predictions on their own. The wonderful thing is that you can aggregate their results. The collective insight, or total wisdom of these algorithms is far superior to that of a single algorithm. In fact, it is better than a single algorithm that is trained by all the data in one go. There are two key reasons for the power of this collective: firstly, algorithms tend to make different types of errors, and secondly, they learn from each other and improve as they go along. This is what computer scientists call co-evolution. The main idea is that a diverse set of algorithms will have superior performance. This is because of their complementary strengths and weaknesses. And they shine especially when making predictions in new, unseen data.

Just as with machine learning, humans need to actively seek out diverse perspectives of people who are wired differently because of their alternative life experiences. Only then can we build a better, more accurate picture of the truth. Furthermore, we can make better decisions for society when we understand things better.

"Variability, Not Repetition, is the Key to Mastery - Scott H Young"

Bruce Lee is reported to have said, "I fear not the man who has practiced 10,000 kicks once, but the man who has practiced one kick 10,000 times." Variability plays an essential and oft-neglected role in mastering complex skills. Considerable research shows that practicing in varied contexts with varied methods and performing with varied task constraints results in more robust learning than simple repetition. Contextual interference improves the transfer of learning to new situations. Identifying problems correctly and ensuring the correct technique is associated with the problem. The extra effort needed to retrieve the right response may be desirable. According to psychologist Robert Bjork's influential theory of memory, more difficult retrieval results in greater memory strengthening than easier retrieval. Experts tend to perceive the deep principles of a particular problem. In contrast, novices tend to get distracted by the superficial features.

Some theorists argue that we reason through storing multiple, specific instances of ideas. A substantial challenge in learning is that the mind economizes on effort. This means we often fall prey to psychological shortcuts that give us the correct answer, even if they won't benefit us in future situations. But having multiple methods for getting the right answer is also an important part of mastering complex skills. Regardless of whether thinking is fundamentally concrete or abstract (or some mixture of both), seeing multiple examples is central to learning. When variability was low, participants simply memorized the pattern. In contrast, when variability was high, they simulated the trajectory to find the likely destination.

Strategy variability will be highest when we have some, but not a lot, of experience. Having multiple strategies for solving a problem is vital when you aren't yet at the level of mastery. Most research supports the benefits of variability in practice. However, less-variable practice is often better for beginners or lower-performing students. Having variable methods may also backfire if some of those methods are buggy or flawed. These two considerations moderate the extreme stance that all variability is good when learning. Instead, we want to see a slow ramp-up in variability.

Incorporating the concepts from both articles, it becomes clear that diversity and variability are essential for better decision-making and mastering complex skills. Just as AI algorithms benefit from cognitive diversity, humans can also benefit from seeking out diverse perspectives to gain a more accurate understanding of the truth. Similarly, practicing with variability in contexts and methods can lead to more robust learning and skill mastery.

Here are three actionable pieces of advice:

1. Actively seek out diverse perspectives: Whether it's through collaborating with people from different backgrounds or seeking out alternative viewpoints, actively seeking diversity of thought can lead to better decision-making and a more comprehensive understanding of complex issues.

2. Embrace variability in practice: When learning new skills or trying to master complex tasks, incorporate variability in contexts, methods, and constraints. This can improve the transfer of learning to new situations and enhance memory strengthening, ultimately leading to more robust skill mastery.

3. Gradually increase variability: While variability is beneficial, it's important to gradually increase it, especially for beginners or lower-performing individuals. Starting with less-variable practice and gradually introducing more variability can optimize the learning process and prevent overwhelming learners with too many options.

In conclusion, both AI and human learning can teach us valuable lessons about the importance of diversity and variability. By embracing diversity of thought and incorporating variability in our decision-making processes and skill development, we can make better decisions, gain a deeper understanding of complex issues, and ultimately improve our overall performance.