Keeping Science in Data Science with Patrick Hall
Patrick Hall challenges data science norms, warns against magical thinking and malleable hypotheses, reflects on human-AI teaming and delivering value with AI.
Patrick Hall is the Principal Scientist at bnh.ai.
Patrick artfully illustrates how data science has become divorced from scientific rigor. At least, that is, in popular conceptions of the practice. Kimberly and Patrick discuss the pernicious influence of the McNamara Fallacy, applying the scientific method to algorithmic development and keeping an open mind without sacrificing concept validity. Patrick addresses the recent hubbub around AI sentience, cautions against using AI in social contexts and identifies the problems AI algorithms are best suited to solve. Noting AI is no different than any other mission-critical software, he outlines the investment and oversight required for AI programs to deliver value. Patrick promotes managing AI systems like products and makes the case for why performance in the lab should not be the first priority.
A transcript of this episode can be found here.
Patrick artfully illustrates how data science has become divorced from scientific rigor. At least, that is, in popular conceptions of the practice. Kimberly and Patrick discuss the pernicious influence of the McNamara Fallacy, applying the scientific method to algorithmic development and keeping an open mind without sacrificing concept validity. Patrick addresses the recent hubbub around AI sentience, cautions against using AI in social contexts and identifies the problems AI algorithms are best suited to solve. Noting AI is no different than any other mission-critical software, he outlines the investment and oversight required for AI programs to deliver value. Patrick promotes managing AI systems like products and makes the case for why performance in the lab should not be the first priority.
A transcript of this episode can be found here.
