The Right To Confront Your Accusers: Opening the Black Box of Forensic DNA Software
The results of forensic DNA software systems are regularly introduced as compelling evidence in criminal trials, but requests by defendants to evaluate how these results are generated are often denied. Furthermore, there is mounting evidence of problems such as failures to disclose substantial changes in methodology to oversight bodies and substantial differences in the results generated by different software systems. In a society that purports to guarantee defendants the right to face their accusers and confront the evidence against them, what then is the role of black-box forensic software systems in moral decision making in criminal justice? In this paper, we examine the case of the Forensic Statistical Tool (FST), a forensic DNA system developed in 2010 by New York City's Office of Chief Medical Examiner (OCME). For over 5 years, expert witness review requested by defense teams was denied, even under protective order, while the system was used in over 1300 criminal cases. When the first expert review was finally permitted in 2016, many problems were identified including an undisclosed function capable of dropping evidence that could be beneficial to the defense. Overall, the findings were so substantial that a motion to release the full source code of FST publicly was granted. In this paper, we quantify the impact of this undisclosed function on samples from OCME's own validation study and discuss the potential impact on individual defendants. Specifically, we find that 104 of the 439 samples (23.7%) triggered the undisclosed data-dropping behavior and that the change skewed results toward false inclusion for individuals whose DNA was not present in an evidence sample. Beyond this, we consider what changes in the criminal justice system could prevent problems like this from going unresolved in the future.
Krane, D. E.,
& Hughes, C.
(2019). The Right To Confront Your Accusers: Opening the Black Box of Forensic DNA Software. Proceedings of the 2019 AAAI/ACM Conference on AI, 321-327.