On the threshold of injustice: manipulating DNA evidence

Slides:



Advertisements
Similar presentations
Overcoming DNA Stochastic Effects 2010 NEAFS & NEDIAI Meeting November, 2010 Manchester, VT Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA Cybergenetics.
Advertisements

DNA Mixture Interpretations and Statistics – To Include or Exclude Cybergenetics © Prescription for Criminal Justice Forensics ABA Criminal Justice.
Finding Truth in DNA Mixture Evidence Innocence Network Conference April, 2013 Charlotte, NC Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA.
How Inclusion Interpretation of DNA Mixture Evidence Reduces Identification Information American Academy of Forensic Sciences February, 2013 Washington,
Creating informative DNA libraries using computer reinterpretation of existing data Northeastern Association of Forensic Scientists November, 2011 Newport,
Preventing rape in the military through effective DNA computing Forensics Europe Expo Forensics Seminar Theatre April, 2014 London, UK Mark W Perlin, PhD,
How TrueAllele® Works (Part 1)
TrueAllele ® Casework Validation on PowerPlex ® 21 Mixture Data Australian and New Zealand Forensic Science Society September, 2014 Adelaide, South Australia.
Using TrueAllele ® Casework to Separate DNA Mixtures of Relatives California Association of Criminalists October, 2014 San Francisco, CA Jennifer Hornyak,
Revolutionising DNA analysis in major crime investigations The Investigator Conferences Green Park Conference Centre May, 2014 Aylesbury, Buckinghamshire.
No DNA Left Behind: When "inconclusive" really means "informative" Schenectady County District Attorney’s Office January, 2014 Mark W Perlin, PhD, MD,
More informative DNA identification: Computer reinterpretation of existing data Ria David, PhD Cybergenetics, Pittsburgh, PA Cybergenetics ©
Computer Interpretation of Uncertain DNA Evidence National Institute of Justice Computer v. Human June, 2011 Arlington, VA Mark W Perlin, PhD, MD, PhD.
How TrueAllele ® Works (Part 2) Degraded DNA and Allele Dropout Cybergenetics Webinar November, 2014 Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh,
Separating Familial Mixtures, One Genotype at a Time Northeastern Association of Forensic Scientists November, 2014 Hershey, PA Ria David, PhD, Martin.
Cybergenetics Webinar January, 2015 Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA Cybergenetics © How TrueAllele ® Works (Part 4)
Forensic Science & Criminal Law: Cutting Edge DNA Strategies Pennsylvania Association of Criminal Defense Lawyers September, 2015 Hotel Monaco, Pittsburgh,
Unleashing Forensic DNA through Computer Intelligence Forensics Europe Expo Forensic Innovation Conference April, 2013 London, UK Mark W Perlin, PhD, MD,
Rapid DNA Response: On the Wings of TrueAllele Mid-Atlantic Association of Forensic Scientists May, 2015 Cambridge, Maryland Martin Bowkley, Matthew Legler,
Getting Past First Bayes with DNA Mixtures American Academy of Forensic Sciences February, 2014 Seattle, WA Mark W Perlin, PhD, MD, PhD Cybergenetics,
Compute first, ask questions later: an efficient TrueAllele ® workflow Midwestern Association of Forensic Scientists October, 2014 St. Paul, MN Martin.
Virginia TrueAllele ® Validation Study: Casework Comparison Presented at AAFS, February, 2013 Published in PLOS ONE, March, 2014 Mark W Perlin, PhD, MD,
Murder in McKeesport October 25, 2008 Tamir Thomas.
When Good DNA Goes Bad International Conference on Forensic Research & Technology October, 2012 Chicago, Illinois Mark W Perlin, PhD, MD, PhD Cybergenetics,
DNA Mapping the Crime Scene: Do Computers Dream of Electric Peaks? 23rd International Symposium on Human Identification October, 2012 Nashville, TN Mark.
Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA Cybergenetics © Duquesne University October, 2015 Pittsburgh, PA What’s in a Match?
Open Access DNA Database Duquesne University March, 2013 Pittsburgh, PA Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA Cybergenetics ©
Jack Ballantyne and Mark Perlin International Conference on Inference and Statistics, July , Seattle, WA.
Exploring Forensic Scenarios with TrueAllele ® Mixture Automation 59th Annual Meeting American Academy of Forensic Sciences February, 2007 Mark W Perlin,
Objective DNA Mixture Information in the Courtroom: Relevance, Reliability & Acceptance NIST International Symposium on Forensic Science Error Management:
DNA Identification: Quantitative Data Modeling Cybergenetics © Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA TrueAllele ® Lectures.
Separating DNA Mixtures by Computer to Identify and Convict a Serial Rapist Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA Garett Sugimoto,
Seventh Annual Prescriptions for Criminal Justice Forensics Program Fordham University School of Law June 3, 2016 DNA Panel.
Understanding DNA Evidence Beaver County Courthouse March, 2016 Beaver, PA Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA Cybergenetics ©
When DNA alone is not enough: exoneration by computer interpretation
Fighting for DNA Justice: Genotyping Software in the Hillary Acquittal
DNA: TrueAllele® Statistical Analysis, Probabilistic Genotyping
A Match Likelihood Ratio for DNA Comparison
Forensic Stasis in a World of Flux
Overcoming Bias in DNA Mixture Interpretation
Validating TrueAllele® genotyping on ten contributor DNA mixtures
How to Defend Yourself Against DNA Mixtures
Error in the likelihood ratio: false match probability
Explaining the Likelihood Ratio in DNA Mixture Interpretation
PCAST report • DNA mixture limits 3 contributors 20% fraction
Distorting DNA evidence: methods of math distraction
Machines can work it out: Automated TrueAllele® workflow
Virginia TrueAllele® Validation Study: Casework Comparison
Solving sexual assault cases using DNA mixture evidence
Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA, USA
Suffolk County TrueAllele® Validation
American Academy of Forensic Sciences Criminalistics Section
Solving Crimes using MCMC to Analyze Previously Unusable DNA Evidence
Investigative DNA Databases that Preserve Identification Information
DNA Identification: Inclusion Genotype and LR
DNA Identification: Stochastic Effects
Mark W Perlin, PhD, MD, PhD Cybergenetics, Pittsburgh, PA, USA
DNA Identification: Biology and Information
DNA Identification: Biology and Information
Forensic match information: exact calculation and applications
The Triumph and Tragedy of DNA Evidence
Probabilistic Genotyping to the Rescue for Pinkins and Glenn
Forensic validation, error and reporting: a unified approach
DNA Identification: Biology and Information
DNA Identification: Mixture Interpretation
Exonerating the Innocent with Probabilistic Genotyping
Testifying about probabilistic genotyping results
David W. Bauer1, PhD Nasir Butt2, PhD Jeffrey Oblock2
Using probabilistic genotyping to distinguish family members
Reporting match error: casework, validation & language
Presentation transcript:

On the threshold of injustice: manipulating DNA evidence American Academy of Forensic Sciences Jurisprudence Section February, 2017 New Orleans, LA Mark W Perlin, PhD, MD, PhD & William P Allan, MS Cybergenetics Pittsburgh, PA Clinton Hughes, JD The Legal Aid Society New York, NY Cybergenetics © 2003-2017 Cybergenetics © 2017

DNA mixing and unmixing • extract • amplify • size • genotype DNA in a sample combine separate some people mixed DNA unmixed genotypes

Probabilistic genotyping Bayesian Noise Model baseline Much data Use all peaks Low data All genotypes Stutter Mine the data Variation More variables Objective computer operation

Less math, less capability Bayesian Incomplete Noise Model baseline Thresholds Much data Use all peaks Discard peaks Low data All genotypes Set dropout Stutter Mine the data Calibrate lab Variation More variables Give up Subjective human operator

Choices and consequences Objective probability can be fair Human choices introduce bias Subjectivity leads to unfair outcomes Gambling with justice

DNA evidence and results Death by strangulation of 12 year old boy 150 biological evidence items, with focus on DNA under victim’s fingernails Software finds match statistic of ten million, connecting fingernails to defendant DNA Unknown minor contributor is 0.4% or 1:250

Rule 702 Sufficient data Reliable method Reliably apply method to data

Sufficient data Mixture • ratio is 1:250 • less than 1 cell Fingernail data show low mixture amount & low peak heights for minor contributor Peak height • 30 to 70 rfu

Reliable method Mixture (validation) • ratio of 1:25 • many cells donor not donor o x Mixture (validation) • ratio of 1:25 • many cells donor Peak threshold • 30 rfu in study 1 2 3 cells not donor

Reliably apply method to data donor not donor o x Mixture (case) • ratio is 1:250 • less than 1 cell log(LR) 2 1 Peak threshold • 30 rfu in study • 50 rfu in case cell

Applying thresholds LR include exclude Different choices, different answers Software does not agree with itself

Choosing data 3 amplifications at D8 locus Victim Foreign Defendant Exculpatory V V expert report F D D X X X X X other choices

Double dropout “Q” means not 9, 10, 11 or 12 data allele Amp 1 Amp 2 Amp 3 9 55 80 97 10 1,315 2,009 2,653 11 95 121 12 969 1,757 2,368 Peak height data at the D18 locus “Q” means not 9, 10, 11 or 12 data allele Defendant’s 17,17 is not in the data Hp weight for Q,Q genotype = 15% Hd weight for Q,Q genotype = 14% Likelihood ratio is 15%/14% = 1.05 > 1 Non-data Q,Q matches defendant 17,17 Excluded from data, but inclusionary LR

Judge’s ruling The Expert conceded at the hearing that no internal validation studies were performed by the State crime lab for the use of the Software on casework samples developed at the lab. As a result the Expert was forced to pick and choose data from different “reliable sources” and input parameters into the program in such a way that he believed the system would tolerate. ORDERED that the defendant’s motion to preclude the prosecution from calling an expert witness to testify on their direct case regarding any conclusion reached by the use of the Software is granted as the prosecution cannot lay a foundation for the introduction of evidence that had not been internally validated.

Rule 403 Probative value unreliably applied method on insufficient DNA data Danger of unfair DNA prejudice, confusing the issues, misleading the jury

Discovery for Software Recommendations Discovery for Software • validation studies (internal & other) • user, procedure and training manuals • papers, reports, math description • data choices, parameter settings • all electronic DNA Data in the case • demand working Software program • run Software on Data to replicate results • run different Software on Data to confirm

TrueAllele YouTube channel More information http://www.cybgen.com/information • Courses • Newsletters • Newsroom • Presentations • Publications • Webinars http://www.youtube.com/user/TrueAllele TrueAllele YouTube channel perlin@cybgen.com