"A few years ago, when I was working at a traditional law firm, the partners gathered with us with barely any excitement. "Rejoice," they announced, unveiling our new AI assistant that would make legal work faster, easier, and better. An expert was brought in to train us on dashboards and automation. Within months, her enthusiasm had curdled into frustration as lawyers either ignored the expensive tool or, worse, followed its recommendations blindly."
"Many traditional law firms have rushed to adopt AI decision support tools for client selection, case assessment, and strategy development. The pitch is irresistible: AI reduces costs, saves time, and promises better decisions through pure logic, untainted by human bias or emotion. These systems appear precise: When AI was used in cases, evidence gets rated "strong," "medium," or "weak." Case outcomes receive probability scores. Legal strategies are color-coded by risk level."
"But this crisp certainty masks a messy reality: most of these AI assessments rely on simple scoring rules that check whether information matches predefined characteristics. It's sophisticated pattern-matching, not wisdom, and it falls apart spectacularly with borderline cases that don't fit the template. And here's the kicker: AI systems often replicate the very biases they're supposed to eliminate. Research is finding that algorithmic recommendations in legal tech can reflect and even amplify human prejudices baked into training data."
Traditional law firms rushed to adopt AI decision-support tools for client selection, case assessment, and strategy development, attracted by promises of lower costs, faster work, and supposedly objective decisions. These systems present crisp ratings—evidence labeled strong, medium, or weak; outcomes given probability scores; strategies color-coded by risk—but rely largely on simple scoring rules and pattern-matching against predefined characteristics. The approach fails with borderline cases that defy templates and can replicate or amplify human prejudices present in training data. AI tools can be useful when improved and used critically, but current implementations often encourage blind reliance and obscure limitations.
Read at Fast Company
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