Probabilistic Genotyping Used for Criminal Prosecution
In a recent article published on The Markup, software that is used to match DNA in criminal prosecution has come under scrutiny. The software implements what is known as probabilistic genotyping, a method used when analyzing data that contains a mixture of DNA from multiple people. The software uses complex statistical algorithms to determine to compare the likelihood that a person’s unique DNA versus a random person’s DNA is present in a mixture.
Of course the benefit of such analysis tools could be the removal of potential bias or of human error. On the downside, however, the defendant and his or her defense team do not have access to the algorithms. Obviously there would be at least two concerns that have led to the DNA matching software coming under scrutiny:
- How would one know if the algorithm had been tampered with or altered to bias in one way or the other?
- How would one know if the software itself had been developed to any quality standard, and that the particular release of software included necessary error checks (we might call these risk controls in medical device lingo)?
Concerns with Use of Software
The article quoted Mats Heimdahl (Professor and Department Head, Computer Science & Engineering, University of Minnesota) and Jeanna Matthews, two computer science experts: “It is virtually certain that there are flaws in the TrueAllele software,” wrote in a declaration to the federal court. “On average, there will be six flaws for every 1,000 lines of code, and TrueAllele has 170,000 lines of code.”
As software professionals in the medical device software field, we have a responsibility to ensure both appropriate security controls and appropriate software lifecycle controls. For the software lifecycle process itself, of course IEC 62304 (See our post on 62304-FDA Compliance Training) is our standard and is recognized by US FDA as a consensus standard. However, beyond the process rigor, the difference between high quality software and software of lower quality is often directly related to the defensive coding designs and the built-in error checking and recovery designs. The exact methods used will depend upon the programming technologies selected, the intended use, the worst-case (or worst-cases) outcome, and other factors. We must understand the ramifications of software failures. If we believe the software experts in this column, there could be over 1,000 flaws or defects in that code. Our process needs to reduce that number and our defensive coding and error-checking should be designed to reduce the likelihood that, should any of those defects occur, an acceptable outcome or output would be generated.
Read the article at this link: https://themarkup.org/news/2021/03/09/powerful-dna-software-used-in-hundreds-of-criminal-cases-faces-new-scrutiny
