Document Type
Article
Publication Date
7-2020
Journal Title
SciTech Lawyer
ISSN
1550-2090
Abstract
Person-based evidence is no longer the monolith it once was. With technological advancement has come the rise of so-called "machine-generated evidence." Unlike traditional forms of evidence, the reliability of machine-generated evidence primarily depends not on any person’s actions—neither the quality of their perceptions nor their ability to carry out tasks—but instead on the standardized processes and mechanisms internal to the machine that produced it. As technological advancement continues apace, and new, innovative forms of machine-generated evidence reach the courtroom, judges and lawyers will be required to respond in two important ways.
First must come awareness of the unique nature of machine-generated evidence. The legal profession must recognize that machine-generated evidence is categorically distinct from person-based evidence. It constitutes a difference in kind. Much of the present confusion, inaccuracies, and inefficiencies in our legal system’s treatment of machine-generated evidence is caused by attempts by judges and lawyers to treat machine-generated evidence as if it were person-based—as if its reliability depended on the actions of some individual. Only by recognizing the unique and, in many ways, paradigm-shifting features of machine-generated evidence can necessary reform occur.
Second, after recognizing that machine-generated evidence constitutes a difference in kind, the legal profession must consider how to best treat it in the courtroom. What doctrines and rules must change to better scrutinize machine-generated evidence? If the reliability of machine-generated evidence doesn’t (or shouldn’t) require a witness on the stand, what is the best way to evaluate it at trial? Answering these questions will require innovative and potentially radical thinking, as demonstrated by recent scholarship paving the way on this front.
First Page
4
Last Page
7
Num Pages
3
Volume Number
16
Issue Number
4
Series
ABA Periodicals
Publisher
American Bar Association
Recommended Citation
G. Alexander Nunn,
Machine-Generated Evidence,
16
SciTech Law.
4
(2020).
Available at:
https://scholarship.law.tamu.edu/facscholar/1957