Meet the Automated Scientist: AI's Bold Leap into Facilitating Human Agency

In his seminal work, the late Daniel Kahneman described humility not as a superiority in knowledge or perspective, but rather as the ability to approach new information with an equal footing, evaluating its veracity rather than confirming pre-existing biases. This human tendency to seek affirmation of our beliefs is deeply interwoven with our self-perception and the image we wish to project to others. However, we rarely pause to question the foundations of our knowledge or reassess its relevance in an ever-changing world.

Those engaged in the work of innovation understand that success requires a relentless pursuit of knowledge and learning. Accordingly, I dedicate a significant portion of my leisure time to this endeavor, often reading up to two books a week. I've discovered that when I find something difficult to understand, the challenge doesn't lie in my ability to learn. Instead, it's my preconceived notions that create obstacles. The cumulative cultural, organizational knowledge, and social norms I've inherited from past generations often impede my learning process. To truly learn, I must shed these biases and approach new information with increased humility, particularly when it challenges my existing beliefs and paradigms. Failing to do so, I risk becoming an impediment to my own destiny and to the person I aspire to be.

Daniel Kahneman sheds light on this idea by explaining how our brains are equipped with cognitive shortcuts designed to save mental energy. Stereotypes are one such shortcut, shaping our perceptions of things, places, and, regrettably, people. These stereotypes foster emotional responses – prejudices – which, when acted upon, morph into discrimination.

This phenomenon ties back to the human quest for agency. Agency, the ability to apply knowledge and skills to influence outcomes, is continuously sought after and executed through our understanding of principles and actions influencing events. We then scrutinize environmental feedback to gauge the success of our endeavors. Kahneman and N.J. Enfield, however, point out a crucial bias in this process: our interpretation of reality is invariably colored by past experiences and the cultural knowledge, and social norms accumulated over our lifetime.

Consequently, when we attempt to articulate our experiences, we confront another challenge. As Enfield argues, language, while facilitating social coordination, is a poor descriptor of reality. Thus, our perception of reality is essentially a construct, first distorted internally, then conveyed to others.

This leads to fundamental questions about knowledge and certainty: How reliable are our perceptions and beliefs when even our own judgment is questionable? The team's efforts in developing a counterpart to augment human agency sought to address these issues. Thus, the Automated Scientist was born of Technology.

The challenge lies in creating a counterpart that aids in areas where human cognition falls short. How do we establish trust in this counterpart? Are there existing examples of such collaborations? This article explores these intriguing questions.

Shelley Nandkeolyar Ron Norris Subrata Sen Harirajan Padmanabhan Arthur Kordon Rajib Saha John B. Vicente Jr. PhD Sarath Chandershaker parabole.ai

Herb Dowdy Dr. Joshua Thomason, CPA, MBA

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