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As we grapple with the rapid advancement of technology, we find ourselves on the precipice of a potential crisis in the world of Data Science, a crisis rooted firmly in the lessons of a recent debacle that rocked the world of psychology: the crisis of irreproducible results.

In psychology, this crisis was linked to the practice of ritualistic statistics, where researchers repetitively applied statistical tests with only a surface level understanding of the assumptions underpinning these statistical tools. Ignorance or sheer neglect of the theoretical grounding of their methods resulted in a flurry of psychological studies whose findings could not be replicated, thereby rendering them dubious, at best.

Today, data science, another empirical discipline, appears to be tilting towards a similar precipice. On one hand, data science has emerged as a spectacular amalgamation of multiple backgrounds. Physics, economics, statistics, computer science, and even art have all served as the fountainhead of countless data scientists. This diversity has brought a multitude of perspectives and approaches to data science, fueling its growth. On the other hand, it has also produced a veritable maze of expertise levels and knowledge gaps. For instance, a physicist might excel in algorithms but stumble on software engineering principles, while an economist might grasp statistical significance but fumble with coding best practices.

Machine learning, a subset of data science, coalesces the potential peril. The good news is that it's never been easier to start in Machine Learning, thanks to user-friendly languages like Python and an expansive array of open-source libraries. However, this low barrier to entry is also a curse. Much like psychologists performing ritualistic statistics, data scientists might fall into the trap of using machine learning algorithms as unfathomable "black boxes", running iteration after iteration with scant understanding of the assumptions or the theory riding beneath the code.

We call this the emergence of ritualistic Machine Learning, defined by a lack of solid understanding of software engineering, statistics, and mathematics. In this damning scenario, there's an overemphasis on following the trend or the 'sexy' algorithm, at the expense of scientific rigor and understanding.

For Data Science to sidestep the minefield of irreproducibility, it needs to address these issues head-on. To start, every AI/ML project should begin with a rigorous Exploratory Data Analysis. This will provide an understanding of the underlying data distribution, crucial for making informed decisions about which algorithms and processes to use. Data Scientists need to broaden their AI toolkit, venturing into less explored areas of AI along with the regular machine learning and deep learning models. They must also adopt existing best practices from software engineering, including code reviews, robust documentation, and solid design practices.

Furthermore, the adoption of explainable AI methods is gravely needed. A step towards transparency, these methods enable understanding of the decision-making process of machine learning models. Lack of explainability will only enlarge the target on the back of AI, imperiling the trust required for it to integrate deeply into industries and our lives.

We must seek accountability from data scientists. Given that software systems today are often driven by the models built by data scientists, they should be held to as stringent standards as software engineers. They are, after all, engineers themselves, building systems critical to, at times, life-altering decisions.

A reckoning awaits the world of data science, the echoes of which resonate from its psychological counterpart. By being proactive, insisting on rigor, transparency and accountability, we can ensure that Data Science remains a transformative force for the future, rather than a cautionary tale from the past.