FutureProof Legal

Introduction In my last two posts, I discussed the importance of bridging bias in AI systems with responsible human oversight and examined the evolving nature of the American justice system. The common thread in both discussions was that technological solutions, coupled with human input, often yield better outcomes than either human judgment or AI alone. This same principle can readily apply to something near and dear to many of us: sports. Thanks for reading FutureProof Legal! Subscribe for free to receive new posts and support my work. From Referee to RHLF: A New Take on Officiating The notion of reinforcement learning from human feedback (RLHF) —where an AI model refines its decision-making by learning from real-world responses—has intriguing potential in sports. Traditionally, referees and umpires make instantaneous calls, often with limited visibility or under high-pressure circumstances. Instant replay reviews help mitigate error, but they ...

1. Why This, Why Now? Hello and welcome! I’m excited to launch this Substack as a platform for sharing insights and sparking discussion about the intersection of law, technology, and disruptive innovation . Every day, we witness the accelerating pace of technological advancements— artificial intelligence , blockchain , biotechnology , and more—and the question is no longer if these innovations will transform our world, but how we choose to navigate and shape this transformation. Thanks for reading FutureProof Legal! Subscribe for free to receive new posts and support my work. As an attorney, I’ve seen firsthand how emerging technologies can create both opportunities and obstacles. Legal systems are often playing catch-up, legislators debate new regulations, and businesses grapple with learning—sometimes the hard way—how to innovate responsibly. I’m creating this space now because it’s never been more crucial for us to understand where technology, law, and policy collid...

I grew up in what I consider the “golden era” of football—the 1980s and 1990s—when defenses could singlehandedly dominate a game. Legendary linebackers like Lawrence Taylor , Mike Singletary , and Ray Lewis unleashed ferocity on the field, striking fear into the hearts of opposing offenses. Meanwhile, the secondary was patrolled by iconic safeties like Ronnie Lott and Steve Atwater , whose hard-hitting style defined the toughness of that era. Back then, it wasn’t uncommon for teams with merely adequate quarterbacks to compete for championships, provided they had a formidable running game, a stout offensive line, and a dominant defense. The classic example is the Washington team that relied on John Riggins and the renowned “Hogs,” a towering offensive line that could bulldoze just about any defensive front. Today, in stark contrast, the formula to win a championship feels painfully simple—if you don’t have an elite quarterback, you’re essentially out of contention before the season...

In my previous post, we explored how AI could enhance the accuracy of referee decisions, offering a level of precision and consistency that human judgment sometimes struggles to achieve. Today, I want to expand this conversation by examining a topic that mirrors these challenges: the interplay between bias in artificial intelligence (AI) systems and human judgment , especially in high-stakes scenarios like officiating a football game . . Identifying the Error: The First Step Toward Accountability Whether it’s a missed call on the field or a biased decision by an AI system , the first and most crucial step is recognizing the mistake. But what happens when we look deeper—when we ask whether the decision would have been the same if the player involved had a different reputation or status ? Consider a hard hit during a football game: Would the referee’s decision change if the hit were on the star quarterback instead of a lesser-known player? Would prior incidents, like the p...