Human judgment combined with algorithms outperforms either alone

Evidence

Further, the leadership team should emphasize that legal technology will not be used to replace attorneys, nor will it be used to replace attorney judgment. As Fountaine, McCarthy, and Saleh (2019) have written for the private sector, “When AI is adopted broadly, employees up and down the hierarchy will augment their own judgment and intuition with algorithms’ recommendations to arrive at better answers than either humans or machines could reach on their own” (p. 65). Indeed, voluminous research has found that professional judgment improves when it is coupled with data-based analyses and guidance (Dawes, Faust, & Meehl, 1989; Gendreau, Little, & Goggin, 1996; Kuncel, Klieger, Connelly, & Ones, 2013). This combination of human decision-making informed by cognitive computing is sometimes called “collaborative intelligence” (Wilson & Daugherty, 2018), and the term rightly captures how legal technology should be used: to empower attorneys by placing them in a working partnership with AI. As the CEO of a multi-billiondollar company wrote in the Harvard Business Review, “A good person plus a good algorithm is far superior to the best person or the best algorithm alone” (Lake, 2018). For example, researchers employed a deep-learning algorithm to detect metastatic breast cancer from slides of biopsies, and the algorithm was accurate 92.5 percent of the time (Kleinberg et al., 2018). A physician who reviewed the same slides was accurate 96.6 percent of the time. However, when the two systems were combined—algorithm and physician—the accuracy rate rose to a near-perfect 99.5 percent. — Joseph Avery, Jim Sidanius, Joel Cooper, Gregory Davis, Amanda N., Bias in the Law, 1194

Categorization

• XREF: Connects to the reader’s interest in whether algorithmic/technical correction can override human judgment biases — combined human-machine systems here beat both, relevant to whether bias-correction backfires or augments. — connects to: Collective intelligence exceeds any individual’s without erasing individual expertise — Yves Citton, The Ecology of Attention, 552; Networked machine learning compounds individual errors into population-level knowledge faster than humans can. — Shoshana Zuboff, The Age of Surveillance Capitalism, 1319
• corroborated: 3 independent passages support this claim — Joseph Avery, Jim Sidanius, Joel Cooper, Gregory Davis, Amanda N., Bias in the Law: A Definitive Look at…, 1194