💡 Preventing cognitive bias in the era of machine learning 💡 A few years ago, I began delving into the mechanisms of cognitive biases to understand their influence on decision-making processes. This exploration led me to the groundbreaking research of Daniel Kahneman, a Nobel laureate in Economics (2002), and his book "Thinking, Fast and Slow." Kahneman's work illustrates how minds work through two distinct systems: System 1, characterized by fast, intuitive, and unconscious thinking, and System 2, known for its slow, deliberate, and analytical approach. The ascendancy of Machine Learning (ML) over traditional rule-based systems in recent years suggests a shift toward a dominance of System 1 in AI. ML algorithms, with their ability to learn patterns and associations from data, resemble the automatic, intuitive processes of System 1. In contrast, historical rule-based systems, while more deliberate and analytical like System 2, have taken a back seat in many applications. This shift raises intriguing and important questions about the nature of decision-making in the age of AI. Are we increasingly relying on rapid, heuristic-driven judgments at the expense of slower, more thoughtful analysis? How do cognitive biases inherent in System 1 thinking influence the outcomes of ML algorithms, and what are the implications for fairness, transparency, and accountability? While Machine Learning (ML) offers unparalleled efficiency and scalability, we must remain vigilant against its pitfalls. These include hallucinations and biases stemming from incomplete data sets and poorly distributed learning data leading to erroneous decisions, emphasizing the need for careful data curation and robust validation processes in ML endeavors. Striking a balance between the rapid intuition of System 1 and the thorough analysis of System 2 is paramount. By harnessing the strengths of both systems and integrating principles of cognitive science into ML development and deployment, we can harness the power of AI while mitigating its inherent biases. Ultimately, the active involvement of human judgment in monitoring AI outcomes serves as a critical guardrail to prevent bias and ensure ethical, equitable, and effective decision-making in the digital age. I'm curious to hear your thoughts! 💭
Hi Herve, my response is too long ... I had to republish it 😎 Below is the follow-up to system 1 / system 2 to which I refer !
Great read Hervé, we read so much stuff on AI, more or less meaningful…this is peace is insightful.
CEO at Optimistik | Data Analytics Solutions | Process Manufacturing Industry
6moTotally agree with you Herve, this book is really a must read. AI can bring many positive improvements but we must keep in mind that Intelligence is a human trait, even if AI is mimicking System 1 quiet well, it is far away from being able to perform the task of our System 2. Having said that, we should not forget that our brain is doing all of that using less than 3 watts !