Sunday, July 23, 2017

The Source and Soundness of PCAST's 5% Rule

The President’s Council of Advisors on Science and Technology (PCAST) Report on comparative pattern matching in forensic science has a deceptively simple rule for the admissibility of evidence of a match between a questioned and a known sample: if examiners would declare that the two samples have the same source as often as one time in 20 when analyzing pairs of samples actually that come from different samples, then the comparisons are “scientifically unreliable.” The report gives no explanation of how it arrived at this rule beyond the following enigmatic paragraph: 1/
False positive rate (abbreviated FPR) is defined as the probability that the method declares a match between two samples that are from different sources (again in an appropriate population), that is, FPR = P(M|H0). For example, a value FPR = 0.01 would indicate that two samples from different sources will be (mistakenly) called as a match 1 percent of the time. Methods with a high FPR are scientifically unreliable for making important judgments in court about the source of a sample. To be considered reliable, the FPR should certainly be less than 5 percent and it may be appropriate that it be considerably lower, depending on the intended application. 2/
Five percent has a crisp, authoritative ring to it, but why is 5% “certainly” the maximum tolerable FPR for courtroom use of the test? And what “intended applications” would demand a lower FPR? Is the underlying thought that greater “scientific reliability” is required as the gravity of the case increases—from a civil case, to a misdemeanor, to a major crime, on up to a capital case?

Statistical Practice as the Basis for the 5% Rule

Inasmuch as the paragraph is found in an appendix entitled "statistical issues," we should expect statistical concepts and practice to help answer such questions. And in fact, 5% is a common number in statistics. In many applications, statistical hypothesis tests try to keep the risk of a false rejection of the “null hypothesis” H0—a false-positive conclusion—below 5%. Researchers and journal editors in many fields prize results that can be said to be “statistically significant,” usually at the 0.05 level or better. The expression p < 0.05 is therefore a common accoutrement of experimental or observational results indicating an association between variables. Likewise, the Food and Drug Administration demands clinical trials to show that a new drug is effective for its intended use (“validity,” if you will), with “the typical ‘cap’ on the type I [false positive] error rate ... set at 5% .”3/ In the forensic pattern-matching context, the null hypothesis H0 in the PCAST paragraph would be that a questioned and a known sample are not associated with the same source.

Thus, to the extent PCAST was thinking of the 5% FPR as the significance level required to reject H0, its emphasis on 5% is well grounded in statistical practice. Using certain standard levels of significance, particularly 5%, can be traced to the 1920s. The eminent British statistician Sir R. A. Fisher wrote:
It is convenient to draw the line at about the level at which we can say: ‘Either there is somethng in the treatment, or a coincidence has occurred such as does not occur more than once in twenty trials.’ ... If one in twenty does not seem high enough odds, we may, if we prefer it, draw the line at one in fifty (the 2 per cent point), or one in a hundred (the 1 per cent point). Personally, the writer prefers to set a low standard of significance at the 5 per cent point, and ignore entirely all results which fail to reach that level. A scientific fact should be regarded as experimentally established only if a properly designed experiment rarely fails to give this level of significance. 4/
For FPRs larger than 5%, the reports of criminalists do not meet (Fisher’s) criterion for establishing a “scientific fact.” Their conclusions of positive association for such error-prone procedures are not, in PCAST’s words, “scientifically reliable.”

Having equated PCAST’s unexplained choice of 5% with a common implementation of statistical hypothesis testing, we also can see why the report suggested that a “considerably lower” number might be required for scientific “reliability.” A 5% FPR lets in examiner conclusions that might be wrong about one time in twenty when defendants are innocent and there is no true association between the questioned item and the known one. False positives tend to increase the rate of false convictions, whereas false negatives tend to would increase the rate of false acquittals. The norm that false convictions are worse than false acquittals counsels caution in relying on an examiner’s conclusion to convict a defendant. And if false convictions in the most serious of cases are worse still, we can see why the PCAST report stated that “the FPR should certainly be less than 5 percent and it may be appropriate that it be considerably lower, depending on the intended application.” Five percent may be good enough for an editor to publish an interesting paper purporting to have discovered something new in social psychology, but this scientific convention does not mean that 5% is good enough for a criminal conviction, let alone one that would lead to an execution.

So we can see that PCAST’s 5% figure did not come from thin air. Indeed, some statisticians and psychologists think that it is too weak a standard—that the general rule in science ought to be p < 0.005. 5/ Nevertheless, the general use of the arguably lenient 5% significance level does not establish that the 5% rule is legally compelled. The law incorporates the intensified concern for false positives into the burden of persuasion for the evidence as a whole. The jury is instructed to acquit unless it has no reasonable doubt that a defendant in a criminal case is guilty; in contrast, in a civil case, the plaintiff can prevail on a mere preponderance of the evidence. But these burdens do not apply to individual items of evidence. The standard for admitting scientific—and other—evidence does not change with how much is at stake in the particular case. After all, the probative value of scientific evidence is no different in a criminal case than in a civil one. Although the PCAST report insists that its statements about science are merely designed to inform courts about scientific standards, if “scientific reliability” depends on the “importance” of the “judgments in court” and varies according to “the intended application,” then PCAST's "scientific reliability" turns out to be based on what is considered socially or legally “appropriate.”

Beyond the FPR

In sum, it is (or would have been) fair for PCAST to point out that it is uncommon for results at higher significance levels than 0.05 to be credited in the scientific literature. But a more deeply analytical report would have noted that there is uneasiness in the statistical community with the hypothesis testing framework and particularly with over-reliance on the p < 0.05 rule. (Today's mail includes an invitation to attend a "Symposium on Statistical Inference: A World Beyond p < 0.05" sponsored by the American Statistical Association.)

Only part of the world beyond p < 0.05 comes from the fact that the FPR is not the only quantity that determines “scientific reliability.” Superficially, the false-positive error probability might look like the appropriate statistic for considering the probative value of a positive finding, but that cannot be right. Scientific evidence, like all circumstantial evidence, has probative value to the extent it changes the probability of a material fact. That there is much more to probative value than the FPR therefore is easily seen through the lens of Bayes’ rule. As the PCAST report notes, in this context, Bayes' theorem prescribes how probability or odds change with the introduction of evidence. The odds after learning of the examiner’s finding are the odds without that information multiplied by the Bayes factor: posterior odds = prior odds × BF.

The Bayes factor thus indicates the strength of the evidence. Stronger evidence has a larger BF and hence a greater impact on the prior odds than weaker evidence. The Bayes factor is a simple ratio. The FPR appears as the denominator, and the sensitivity—or true positive rate—forms the numerator. In symbols, BF = sensitivity / FPR.

The report acknowledges that sensitivity matters (for some purposes at least). Earlier, the report states that “[i]t is necessary to have appropriate empirical measurements of a method’s false positive rate and the method’s sensitivity. [I]t is necessary to know these two measures to assess the probative value of a method.” 6/  Because it takes both operating characteristics to express the probative value of the test, PCAST cannot sensibly dismiss a test as having so little probative value as to be considered “scientifically reliable” on the basis of only one number. Realizing this prompts the next question for devising a rule in the spirit of PCAST's—namely, what is the sensitivity that, together with an FPR of 5%, would define the threshold for “scientific reliability”?

One might imagine that PCAST would consider any false-negative rate in excess of 5% as too high. 7/ If so, it follows that the scientists are saying that, in their view of what is important or what is the dominant convention in various domains, subjective pattern matching must shift the prior odds by a factor of at least .95/.05 = 19 to be considered “scientifically reliable.” On the other hand, if the scientists on PCAST think it is appropriate for a false-negative probability to be ten times the maximum acceptable false-positive probability, then their minimum for “reliability” would become a FNR of 50% and a FPR of 5%, for a Bayes’ factor of only ten.

What Does the Law Require?

Whether the cutoff comes from the FPR alone or the more complete Bayes factor, the very notion of a sharp cutoff is questionable. The purpose of a forensic-science test for identity is to provide evidence that will assist judges or jurors. Forensic scientists who present results and reasonable estimates of the likelihoods or conditional error probabilities associated with their conclusions are staying within the bounds of what is scientifically known.

Consider a hypothetical pattern-matching test for identity for which FPR = 10% and sensitivity = 70% as shown by extensive experiments, each of which demonstrates an ability to distinguish sources from nonsources with accuracy above what would be expected by chance (p < 0.05). According to the PCAST report, this test would be inadmissible for want of “scientific reliability” or “foundational validity” because the FPR of 10% is too high. But if this were a test for a disease, would we really want a diagnosing physician to ignore the positive test result just because the FPR is greater than 5%? The positive finding from the lab would raise the prior odds from, say, 1 to 2, to 7 to 2 (corresponding to an increase in probability from 33% to 78%). Like the physician trying to reach the best possible diagnosis, the judge or jury trying to reach the best possible reconstruction of the events could benefit from knowing that an examiner, who can perform at the empirically established level of accuracy, has found a positive association.

The logic behind a high hurdle for scientific evidence is that “it is likely to be shrouded with an aura of near infallibility, akin to the ancient oracle of Delphi.” 8/ As one federal judge (an advisor to PCAST) wrote in excluding the testimony of a handwriting expert:
[I]t is the Court's role to ensure that a given discipline does not falsely lay claim to the mantle of science, cloaking itself with the aura of unassailability that the imprimatur of ‘science’ confers and thereby distorting the truth-finding process. There have been too many pseudo-scientific disciplines that have since been exposed as profoundly flawed, unreliable, or baseless for any Court to take this role lightly. 9/
Under this rationale, a court should be able to admit the positive test result if the jury is informed of and can appreciate the limitations of the finding. A result that is ten time more probable when the samples have the reported source than when they have different sources is not unreliable “junk science.” Of course, it may not be the product of a particularly scientific (or even a very standardized) procedure, and that must be made clear to the factfinder. When the criminalists employing the highly subjective procedure truly have specialized knowledge—as evidenced by rigorous and repeated tests of their ability to arrive at correct answers—their findings can be presented along with their known error rates without creating “an aura of near infallibility.” A blanket rule against all expert evidence that has known error rates in excess of 5% is unsound.

This critical view of PCAST's 5% rule does not reject the main theme of the report—that when a forensic identification procedure relies on a vaguely defined judgmental process (such as "sufficient similarities and explicable dissimilarities in the light of the examiner's training and experience"), well-founded estimates of the ability of examiners to make the correct judgments are vital to admitting source attributions in court. Of course, Daubert v. Merrell Pharmaceuticals 9/ did not make any single factor, including a "known or potential rate of error," absolutely necessary for admitting all types of scientific evidence. But the Daubert Court painted with an amazingly broad brush. The considerations that will be most important can vary from one type of evidence to another.  When it comes to source attributions from entirely subjective assessments of the similarities and differences in feature sets, there is a cogent argument that the only acceptable way to validate the psychological process is to study how often examiners reach the right conclusions when confronted with same-source and different-source samples.

  1. Thanks to Ken Melson for calling to my attention to this paragraph.
  2. PCAST Report at 161-52.
  3. Russell Katz, FDA: Evidentiary Standards for Drug Development and Approval, 1(3) NeuroRx 307–316, (2004), doi: 10.1602/neurorx.1.3.307.
  4. R.A. Fisher, The Arrangement of Field Experiments, 33 J. Ministry Agric. Gr. Brit. 504 (1926), as quoted in L. Savage, On Rereading R.A. Fisher, 4 Annals of Statistics 471 (1976).
  5. Kelly Servick, It Will Be Much Harder To Call New Findings ‘Significant’ If This Team Gets Its Way, Jul. 25, 2017, 2:30 PM, Science, DOI: 10.1126/science.aan7154.
  6. PCAST Report at 50 (emphasis added).
  7. However, the report made no mention of the fact that the false-negative rate was higher than that in at least one of the two experiments on latent print identification of which it approved.
  8. United States v. Alexander, 526 F.2d 161, 168 (8th Cir. 1975).
  9. Almeciga v. Center for Investigative Reporting, Inc., 185 F. Supp. 3d 401, 415 (S.D.N.Y. 2016) (Rakoff, J.).
  10. 509 U.S. 579 (1993).

Wednesday, July 5, 2017

Multiple Hypothesis Testing in Karlo v. Pittsburgh Glass Works

The following posting is adapted from a draft of an annual update to the legal treatise The New Wigmore on Evidence: Expert Evidence. I am not sure of the implications of the calculations in note 23 and the fact that the age-based groups are overlapping. Advice is welcome.

The Age Discrimination in Employment Act of 1967 (ADEA) 1/ covers individuals who are at least forty years old. The federal circuit courts are split as to whether a disparate-impact claim is viable when it is limited to a subgroup of employees such as those aged fifty and older. In Karlo v. Pittsburgh Glass Works, 2/ the Third Circuit held that statistical proof of disparate impact on such a subgroup can support a claim for recovery. The court countered the employer’s argument that “plaintiffs will be able to ‘gerrymander’ arbitrary age groups in order to manufacture a statistically significant effect” 3/ by promising that “the Federal Rules of Evidence and Daubert jurisprudence [are] a sufficient safeguard against the menace of unscientific methods and manipulative statistics.” 4/ In Daubert v. Merrell Dow Pharmaceuticals, the Supreme Court famously reminded trial judges applying the Federal Rules of Evidence that they are gatekeepers responsible for ensuring that scientific evidence presented at trials is based on sound science. By the end of the Karlo opinion, however, the court appeals held that the Senior District Judge Terrence F. McVerry had been too vigorous a gatekeeper when he found inadmissible a statistical analysis of reductions in force offered by laid-off older workers.

The basic problem was that plaintiffs claimed to have observed statistically significant disparities in various overlapping age groups without correcting for the fact that by performing a series of hypothesis tests, they had more than one opportunity to discover something "significant." By way of analogy, if you flip a coin five times and observe five heads, you might begin to suspect that the coin is not fair. The probability of five heads in a row with a fair coin is p = (1/2)5 = 1/32 = 0.03. We can say that the five heads in the sample are "statistically significant" proof (at the conventional 0.05 level) that the coin is unfair.

But suppose you get to repeat the experiment five times. Now the probability of at least one sample of 5 flips with 5 heads is about five times larger. It is 1 - (1 - 1/32)5 = 0.146785, to be exact. This outcome is not so far out line with what is expected of a fair coin. It would be seen about 15% of the time for a fair coin. This is weak evidence that the coin is unfair; certainly, it is not as compelling as the 3% p-value. So the extra testing, with the opportunity to select any one or more of the five samples as proof of unfairness, has reduced the weight of the statistical evidence of unfairness. The effect of the opportunity to search for significance is sometimes known as "selection bias" or, of late, "p-hacking."

In Karlo, Dr. Michael Campion—a distinguished professor of management at Purdue University with degrees in industrial and organizational psychology—compared proportions of Pittsburgh Glass workers older than 40, 45, 50, 55, and 60 who were laid off to the proportion of younger workers who were laid off. He found that the disparities in three of the five categories were statistically significant at the 0.05 level. 5/ The disparity for the 40-and-older range, he said, fell “just short,” being “ significant at the 13% level.” Dr. Campion maintained that “[t]hese results suggest that there is evidence of disparate impact.” 6/ He also misconstrued the 0.05 level as “a 95% probability that the difference in termination rates of the subgroups is [] due to chance alone.” 7/ The district court expressed doubt as to whether Dr. Campion was a qualified statistical expert 8/ and excluded the testimony under Daubert as inadequate “data snooping.” 9/

Apparently, Judge McVerry was more impressed with the report of Defendant’s expert, James Rosenberger — a statistics professor at Pennsylvania State University and a fellow of the American Statistical Association and the American Association for the Advancement of Science. The report advocated adjusting the significance level to account for the five groupings of over-40 workers. The Chief Judge of the Third Circuit, D. Brooks Smith (also an adjunct professor at Penn State), described the recommended correction as follows:
The Bonferroni procedure adjusts for that risk [of a false positive] by dividing the “critical” significance level by the number of comparisons tested. In this case, PGW's rebuttal expert, Dr. James L. Rosenberger, argues that the critical significance level should be p < 0.01, rather than the typical p < 0.05, because Dr. Campion tested five age groups (0.05 / 5 = 0.01). Once the Bonferroni adjustment is applied, Dr. Campion's results are not statistically significant. Thus, Dr. Rosenberger argues that Dr. Campion cannot reject the null hypothesis and report evidence of disparate impact. 10/
Another way to apply the Bonferroni correction is to change the p-value. That is, when M independent comparisons have been conducted, the Bonferroni correction is either to set “the critical significance level . . . at 0.05/M” (as Professor Rosenberger recommended) or “to inflate all the calculated P values by a factor of M before considering against the conventional critical P value (for example, 0.05).” 11/

The Court of Appeals was not so sure that this conservative adjustment was essential to the admissibility of the p-values or assertions of statistical significance. It held that the district court erred in excluding the subgroup analysis and granting summary judgment. It remanded “for further Daubert proceedings regarding plaintiffs' statistical evidence.” 12/ Further proceedings were said to be necessary partly because the district court had applied “an incorrectly rigorous standard for reliability.” 13/ The lower court had set “a higher bar than what Rule 702 demands” 14/ because “it applied a bright-line exclusionary rule” for all studies with multiple comparisons that have no Bonferroni correction. 15/

But the district court did not clearly articulate such a rule. It wrote that “Dr. Campion does not apply any of the generally accepted statistical procedures (i.e., the Bonferroni procedure) to correct his results for the likelihood of a false indication of significance.” 16/ The sentence is grammatically defective (and hence confusing). On the one hand, it refers to "generally accepted statistical procedures." On the other hand, the parenthetical phrase suggests that only one "procedure" exists. Had the district court written “e.g.” instead of “i.e.,” it would have been clear that it was not promulgating a dubious rule that only the Bonferroni adjustment to p-values or significance levels would satisfy Daubert. To borrow from Mark Twain, "the difference between the almost right word and the right word is really a large matter—'tis the difference between the lightning-bug and the lightning." 17/

Understanding the district court to be demanding a Bonferroni correction in all cases of multiple testing, the court of appeals essentially directed it to reconsider its exclusionary ruling in light of the fact that other procedures could be superior. Indeed, there are many adjustment methods in common use, of which Bonferroni’s is merely the simplest. 18/ However, plaintiff’s expert apparently had no other method to offer, which makes it hard to see why the possibility of some alternative adjustment, suggested by neither expert in the case, made the district court's decision to exclude Dr. Campion's proposed testimony an abuse of discretion.

A rule insisting on a suitable response to the multiple-comparison problem does not seem “incorrectly rigorous.” To the contrary, statisticians usually agree that “the proper use of P values requires that they be ... appropriately adjusted for multiple testing when present.” 19/ It is widely understood that when multiple comparisons are made, reported p-values will exaggerate the significance of the test statistic. 20/ The court of appeal’s statement that “[i]n certain cases, failure to perform a statistical adjustment may simply diminish the weight of an expert's finding.” 21/ is therefore slightly misleading. In virtually all cases, multiple comparisons degrade the meaning of a p-value. Unless the statistical tests are all perfectly correlated, multiple comparisons always make the true probability of the disparity (or a larger one) under the model of pure chance greater than the nominal value. 22/

Even so, whether the fact that an unadjusted p-value exaggerates the weight of evidence invariably makes unadjusted p-values or reports of significance inadmissible under Daubert is a more delicate question. If no reasonable adjustment can be devised for the type of analysis used and no better analysis can be done, then the nominal p-values might be presented along with a cautionary statement about selection bias. In addition, in extreme cases, the adjustment will be small and the degree of exaggeration will not be so formidable as to render the unadjusted p-value inadmissible. For instance, if the nominal p-value were 0.001, the fact that the corrected figure is 0.005 would not be a fatal flaw. The disparity would be highly statistically significant even with the correction. But that was not the situation in Karlo. In this case, statistical significance was not apparent. It was undisputed that as soon as one considered the number of tests performed, not a single subgroup difference was significant at the 0.05 level. 23/

Consequently, the rejection of the district court’s conclusion that the particular statistical analysis in the expert’s report was unsound seems harsh. It should be within the trial court’s discretion to prevent an expert from testifying to the statistical significance of disparities (or their p-values) unless the expert avoids multiple comparisons that would seriously degrade the claims of significance or modifies those claims to reflect the negative impact of the repeated tests on the strength of the statistical evidence. 24/ The logic of Daubert does not allow an expert to dismiss the problem of selection bias on the theory -- advanced by plaintiffs in Karlo -- that “adjusting the required significance level [is only] required [when the analyst performs] ‘a huge number of analyses of all possibilities to try to find something significant.'’’ 25/ The threat to the correct interpretation of a significance probability does not necessarily disappear when the number of comparisons is moderate rather than “huge.” Given the lack of highly significant results here (even nominally), it is not statistically acceptable to ignore the threat. 26/ Although the Third Circuit was correct to observe that not all statistical imperfections render studies invalid within the meaning of Daubert, the reasoning offered in support of the claim of significant disparities in Karlo was not statistically acceptable. 27/

l. 29 U.S.C. §§ 621–634.
2. 849 F.3d 61 (3d Cir. 2017).
3. Id. at 76.
4. Id.
5. He testified that he did not compute a z-score (a way to analyze the difference between two proportions when the sample sizes are large) for the 60-and-over group “because ‘[t]here are only 14 terminations, which means the statistical power to detect a significant effect is very low.’” Karlo, 849 F.2d at 82 n.15.
6. Karlo v. Pittsburgh Glass Works, LLC, 2015 WL 4232600, at *11, No. 2:10–cv–1283 (W.D. Penn. July 13, 2015), vacated, 849 F.3d 61 (3d Cir. 2017).
7. Id. at *11 n.13. "A P value measures a sample's compatibility with a hypothesis, not the truth of the hypothesis." Naomi Altman & Martin Krzywinski, Points of Significance: Interpreting P values, 14 Nature Methods 213, 213 (2017).
8. Id. at *12.
9. Id. at *13.
10. 849 F.3d at 82 (notes omitted).
11. Pak C. Sham & Shaun M. Purcell, Statistical Power and Significance Testing in Large-scale Genetic Studies, 15 Nature Reviews Genetics 335 (2014) (Box 3).
12. Id. at 80 (note omitted).
13. Id. at 82.
14. Id at 83.
15. Id. (internal quotation marks and ellipsis deleted).
16. Karlo, 2015 WL 4232600, at *1.
17. George Bainton, The Art of Authorship 87–88 (1890.
18. Martin Krzywinski & Naomi Altman, Points of Significance: Comparing Samples — Part II, 11 Nature Methods 355, 355 (2014)
19. Naomi Altman & Martin Krzywinski, Points of Significance: Interpreting P values, 14 Nature Methods 213, 214 (2017)
20. Krzywinski & Altman, supra note 18
21. Id. at 83 (emphasis added).
22. Because each age group included some of the same older workers, the tests here were not completely independent. But neither were they completely dependent.
23. However, that three out of five groups exhibited significant associations between age and terminations is surprising under the null hypothesis that those variables are uncorrelated. If each test were independent, then the probability of a significant result in each group would be 0.05. The probability of one or more significant results in five tests would be 0.226; that of two or more would be 0.0226; of three or more, 0.00116.
24. Joseph Gastwirth, Case Comment: An Expert's Report Criticizing Plaintiff's Failure to Account for Multiple Comparisons Is Deemed Admissible in EEOC v. Autozone, 7 Law, Probability & Risk 61, 62 (2008).
25. Karlo, 849 F.3d at 82.
26. Dr. Campion also believed that “his method [was] analogous to ‘cross-validating the relationship between age and termination at different cut-offs,’ or ‘replication with different samples.’” Id. at 83. Although the court of appeals seemed to take these assertions at face value, cross-validation involves applying the same statistical model to different data sets (or distinct subsets of one larger data set). For instance, a equation that predicts law school grades as a function of such variables as undergraduate grades and LSAT test scores might be derived from one data set, then checked to ensure that it performs well in an independent data set. Findings in one large data set of statistically significant associations between particular genetic loci and a disease could be checked to see if the associations were present in an independent data set. No such validation or replication was performed in this case.
27. The Karlo opinion suggested that the state of statistical knowledge or practice might be different in social science than in the broader statistical community. The court pointed to a statement (in a footnote on regression coefficients) in a treatise on statistical evidence in discrimination cases that “the Bonferroni adjustment [is] ‘good statistical practice,’ but ‘not widely or consistently adopted’ in the behavioral and social sciences.” Id. (quoting Ramona L. Paetzold & Steve L. Willborn, The Statistics of Discrimination: Using Statistical Evidence in Discrimination Cases § 6:7, at 308 n.2 (2016 Update)). The treatise writers were referring to an unreported case in which the district court found itself unable to resolve the apparent conflict between the generally recognized problem of multiple comparisons and an EEOC expert’s insistence that labor economists do not make such corrections and courts do not require them. E.E.O.C. v. Autozone, Inc., No. 00-2923, 2006 WL 2524093, at *4 (W.D. Tenn. Aug. 29, 2006). In the face of these divergent perceptions, the district judge decided not to grant summary judgment just because of this problem. Id. (“[T]he Court does not have a sufficient basis to find that ... the non-utilization [of the Bonferroni adjustment] makes [the expert's] results unreliable.”). The notion that multiple comparisons generally can be ignored in labor economics or employment discrimination cases is false, Gastwirth, supra note 23, at 62 (“In fact, combination methods and other procedures that reduce the number of individual tests used to analyse data in equal employment cases are basic statistical procedures that have been used to analyse data in discrimination cases.”), and any tendency to overlook multiple comparisons in “behavioral and social science” more generally is statistically indefensible.
That said, the outcome on appeal in Karlo might be defended as a pragmatic response to the lower court's misunderstanding of the meaning of the ADEA. The court excluded the unadjusted findings of significance for several reasons. In addition to criticizing Professor Campion's refusal to make any adjustment for his series of hypothesis tests across age groups, Judge McVerry noted that "the subgrouping analysis would only be helpful to the factfinder if this Court held that Plaintiffs could maintain an over-fifty disparate impact claim." Karlo, 2015 WL 4232600, at *13 n.16. He sided with "the majority view amongst the circuits that have considered this issue ... that a disparate impact analysis must compare employees aged 40 and over with those 39 and younger ... ." Id. (Petruska v. Reckitt Benckiser, LLC, No. CIV.A. 14–03663 CCC, 2015 WL 1421908, at *6 (D.N.J. Mar.26, 2015)). The Third Circuit decisively rejected this construction of the ADEA, pulling this rug out from under the district court. Having held that the district court erred in interpreting the ADEA, requiring the district court to re-examine the statistical showing under the ADEA, correctly understood, might seem appropriate.
Of course, ordinarily an evidentiary ruling that can be supported on several independent grounds will be upheld on appeal as long as at least one of the independent grounds is valid. Here, the ADEA argument was literally a footnote to the independent ground that the failure to adjust for multiple comparisons invalidated the expert's claim of significant disparities. Nevertheless, the independent-grounds rule normally applies after a trial. It avoids retrials when the trial judge would or could rule the same way on retrial. Because Karlo is a summary judgment case, there is less reason to sustain the evidentiary ruling. But even so, the court of appeals did not have to vacate the judgment. Instead, it could have followed the usual independent-grounds rule to affirm the summary judgment while noting that district court could reconsider its Daubert ruling in light of the court of appeals' explanation of the proper reach of the ADEA and the range of statistically valid responses to the problem of multiple hypothesis tests. As a practical matter, however, there may be little difference between having counsel address the issue in the context of a motion to reconsider and a renewed motion for summary judgment.

Friday, June 30, 2017

Judge Spotlights PCAST Report

When the District of Columbia Court of Appeals (the District's "supreme court") overruled Frye v. United States and replaced the general acceptance standard for scientific evidence with one based on the Daubert line of cases, 1/ the court admonished trial judges to use "a delicate touch" in regulating the flow of expert testimony. 2/ One judge offered more guidance. Judge Catharine Friend Easterly penned a concurring opinion proposing that
trial courts will be called upon to scrutinize an array of forensic expert testimony under new, more scientifically demanding standards. As the opinion of the court states, “[t]here is no ‘grandfathering’ provision in Rule 702,” and, under the new rule we adopt, courts may not “reflexively admit expert testimony because it has become accustomed to doing so under the Dyas/Frye test. 3/
Daubert does not necessarily erect a more demanding standard than Frye. It leaves plenty of wiggle room for undiscriminating or lenient rulings. Moreover, under Frye, counsel can challenge scientific evidence that is generally accepted in the forensic-science community (predominantly forensic-science practitioners) but whose scientific foundations are seen as weak in the broader scientific community. Both Frye and Daubert enable -- indeed, both require -- courts to depart from reflexively admitting expert testimony just because they are accustomed to it. The legal difference between the two approaches is that Daubert creates the theoretical possibility of rejecting a method that is still clearly generally accepted but that a small minority of scientists has come to regard -- on the basis of sound (but not yet generally accepted) scientific arguments -- as unfounded. This is merely the flip side of evidence that is not yet generally accepted but that is scientifically sound. Frye keeps such evidence out; Daubert does not. In sum, the standards are formally different, but, as written, one is not more demanding than the other.

But regardless of whether Daubert is more demanding than what the Supreme Court called the "austere" standard of Frye, the remainder of Judge Easterly's opinion is worthy of general notice. The opinion urges the judiciary to heed the findings of the 2009 NRC Report on forensic science and the 2016 PCAST report on particular methods. It observes that
Fortunately, in assessing the admissibility of forensic expert testimony, courts will have the aid of landmark reports that examine the scientific underpinnings of certain forensic disciplines routinely admitted under Dyas/Frye, most prominently, the National Research Council's congressionally-mandated 2009 report Strengthening Forensic Science in the United States: A Path Forward, and the President's Council of Advisors on Science and Technology's (PCAST) 2016 report Forensic Science in the Criminal Courts: Ensuring Scientific Validity of Feature-Comparison Methods [hereinafter PCAST Report]. These reports provide information about best practices for scientific testing, an objective yardstick against which proffered forensic evidence can be measured, as well as critiques of particular types of forensic evidence. In addition, the PCAST Report contains recommendations for trial judges performing their gatekeeping role under Rule 702:
(A) When deciding the admissibility of [forensic] expert testimony, ... judges should take into account the appropriate scientific criteria for assessing scientific validity including: (i) foundational validity,  with respect to the requirement under Rule 702(c) that testimony is the product of reliable principles and methods; and (ii) validity as applied, with respect to [the] requirement under Rule 702(d) that an expert has reliably applied the principles and methods to the facts of the case.
(B) ... [J]udges, when permitting an expert to testify about a foundationally valid feature-comparison method, should ensure that testimony about the accuracy of the method and the probative value of proposed identifications is scientifically valid in that it is limited to what the empirical evidence supports. Statements suggesting or implying greater certainty are not scientifically valid and should not be permitted. In particular, courts should never permit scientifically indefensible claims such as: “zero,” “vanishingly small,” “essentially zero,” “negligible,” “minimal,” or “microscopic” error rates; “100 percent certainty” or proof “to a reasonable degree of scientific certainty;” identification “to the exclusion of all other sources;” or a chance of error so remote as to be a “practical impossibility.”
PCAST Report, supra, at 19; see also id. at 142–45; Gardner v. United States, 140 A.3d 1172, 1184 (D.C. 2016) (imposing limits on experts' statements of certainty). 4/
  1. Motorola v. Murray, 147 A.3d 751 (D.C. 2016) (en banc); Frye Dies at Home at 93, June 30, 2017,
  2. 147 A.3d at 757.
  3. Id. at 759 (emphasis added).
  4. Id. at 759-60 (notes omitted).

Frye Dies at Home at 93

The general-scientific-acceptance standard for scientific evidence originated in the District of Columbia, when the federal circuit court for the District upheld the exclusion of a blood-pressure test for deception in Frye v. United States, 93 F. 1013  (D.C. Cir. 1923). In October of 2016, the District of Columbia's highest court ended the standard's 93-year life there.The D.C. Court of Appeals unanimously overruled Frye and replaced it with the more open-ended Federal Rule of Evidence 702.

It did so in Motorola v. Murray, 147 A.3d 751 (D.C. 2016) (en banc), at the request of the trial court,  which felt that Frye required the admission of expert testimony that cell phones cause brain tumors. That view was mistaken. It is quite possible to exclude, as not based on a generally accepted method, opinions of general causation from expert witnesses when the scientific consensus is that the pertinent scientific studies do not support those opinions. See Cell Phones, Brain Cancer, and Scientific Outliers Are Not the Best Reasons to Abandon Frye v. United States, Nov. 26, 2015.

Elsewhere, I have argued that the choice between the Daubert line of cases codified in Rule 702 and the earlier Frye standard is less important than is the rigor with which the courts apply either standard. The Court of Appeals in Murray remarked that "[p]roperly performing the gatekeeping function will require a delicate touch." Id. at 757. It noted that trial courts have "discretion (informed by careful inquiry) to exclude some expert testimony." Id. In the end, "[t]he trial court still will need to determine whether the opinion 'is the product of reliable principles and methods[,] ... reliably applied.'" Id. at 758 (quoting Fed. R. Evid. 702 (c), (d)).

Wednesday, May 31, 2017

A Few Statistical and Legal Ideas About the Weight of Evidence

The expression “weight of evidence” has become popular among theorists of forensic science, where it is used to indicate the extent to which findings support the claim that two similar traces originated from the same source as opposed to the claim that they originated from different sources. Speaking more broadly, the idea is that the degree of corroboration a body of evidence provides for a theory or hypothesis depends on the probability of the evidence given that hypothesis compared to the probability of the evidence given other hypotheses. This notion has a rich intellectual history in philosophy, law, and statistics.A recent book review* discusses ways to quantify this measure of corroboration and the motivations for them. Some excerpts follow:

The “likelihood ratio” is a concept that pervades statistics. 31/ As [Richard] Lempert argued, it can be used to define whether an item of evidence is [logically] relevant. For example, in the 1990s researchers developed a prostate cancer test based on the level of prostate-specific antigen (“PSA”). The test, they said, was far from definitive but still had diagnostic value. Should anyone have believed them? A straightforward method for validation is to run the test on subjects known to have the disease and on other subjects known to be disease-free. The PSA test was shown to give a positive result (to indicate that the cancer was present) about 70% of the time when the cancer was, in fact, present, and about 10% of the time when the cancer was not actually present. Thus, the test has diagnostic value. The doctor and patient can understand that positive results arise more often among patients with the disease than among those without it.

But why should we say that the greater probability of the evidence (a positive test result) among cancer patients than among cancer-free patients makes the test diagnostic of prostate cancer? There are three answers. One is that if we use it to sort patients into the two categories, we will (in the long run) do a better job than if we use some totally bogus procedure (such as flipping a coin). This is a “frequentist” interpretation of diagnostic value.

A second justification takes the notion of “support” for a hypothesis as fundamental. 37/ Results that are more probable under a hypothesis H1 about the true state of affairs are stronger evidence for H1 than for any alternative (H2) under which they are less probable. If the evidence were to occur with equal probability under both states, however, the evidence would lend equal support to both possibilities. In this example, such evidence would provide no basis for distinguishing between cancer-free and cancer-afflicted patients. It would have no diagnostic value, 38/ and the test should be kept off the market. The coin-flipping test is like this. A head is no more or less probable when the cancer is present than when it is absent.

A difference between the “frequentist,” long-run justification and the “likelihoodist,” support-based understanding is that the latter applies even when we do not perform or imagine a long series of tests. If it really is more probable to observe the data under one state of affairs than another, it would seem perverse to conclude that the data somehow support the latter over the former. The data are “more consistent” with the state of affairs that makes their appearance on a single occasion more probable (even without the possibility of replication).

The same thing is true of circumstantial evidence in law. Circumstantial evidence E that is just as probable when one party’s account is true as it is when that account is false has no value as proof that the account is true or false. It supports both states of nature equally and is logically irrelevant. To condense these observations into a formula, we can write:
E is irrelevant (to choosing between H1 and H2) if P(E|H1) = P(E|H2),
where P(E|H1) and P(E|H2) are the probabilities of the evidence conditional on (“given the truth of,” or just “given”) the hypotheses. The conditional probabilities (or quantities that are directly proportional to them) have a special name: likelihoods. So a mathematically equivalent statement is that
E is irrelevant if the likelihood ratio L = P(E|H1) / P(E|H2) = 1.
A fancier way to express it is that E is irrelevant if the logarithm of L is 0. Such evidence E has zero “weight” when placed on a metaphorical balance scale that aggregates the weight of the evidence in favor of one hypothesis or the other. 39/ In this [prostate cancer] case, the likelihood ratio for a positive test result is 70% ÷ 10% = 7, which is greater than 1. Thus, the test is relevant evidence in deciding whether the patient has cancer. ...

Nothing that I have said so far involves Bayes’s rule. “Likelihood” and “support” are the primitive concepts. Lempert argued for a likelihood ratio of 1 as the defining characteristic of relevance by relying on a third justification—the Bayesian model of learning. How does this work? Think of probability as a pile of poker chips. Being 100% certain that a particular hypothesis about the world is correct means that all of the chips sit on top of that hypothesis. Twenty-five percent certainty means that 25% of the chips sit on the same hypothesis, and the remaining 75% are allocated to the other hypotheses. 42/ To keep things as simple as possible, let’s assume there are only two hypotheses that could be true. To be concrete, let’s say that H1 asserts that the individual has cancer and that H2 asserts that he does not. Assume that doctors know that men with this patient’s symptoms have a 25% probability of having prostate cancer. We start with 25% of the chips on hypothesis 1 (H1: cancer) and 75% on the alternative (H2: some other cause of the symptoms). Learning that the PSA test is positive for cancer requires us to take some of the chips from H2 and put them on H1. Bayes’s rule dictates just how many chips we transfer. The exact amount generally depends on two things: the percentage of chips that were on H1 (the prior probability) and the likelihood ratio L in this simple situation. ... [T]he very simple structure of Bayes’s rule in this case [is]
Odds(H1) · L = Odds(H1|E).
The rule requires updating the “prior odds” (on the left-hand side) by multiplying by the Bayes factor (which also is the likelihood ratio L) to arrive at the “posterior odds” (on the right-hand side). ...

The crucial point is that multiplication by L = 1 never changes the prior odds. Evidence that is equally probable under each hypothesis produces no change in the allocation of the chips—no matter what the initial distribution. Prior odds of 1:3 become posterior odds of 1:3. Prior odds of 10,000:1 become posterior odds of 10,000:1. The evidence is never worth considering. Again, we can get fancy and place the odds and the likelihood ratio on a logarithmic scale. Then the posterior log odds are the prior log odds plus the weight of the evidence (WOE = log-L):
New LO = Prior LO + WOE. 44/
Evidence that has zero weight (L = 1, log-L = 0) leaves us where we started. Evidence E that does not change the odds (and, hence, the corresponding probability) is uninformative—it is irrelevant. Inversely, evidence that does change the probability is relevant—as [Federal] Rule [of Evidence] 401 states in near-identical terms. This, in a nutshell, is the Bayesian explanation of the rule as it applies to circumstantial evidence. It tracks the text of the rule better than the likelihoodist, support-based analysis, but both lead to the conclusion that relevance vel non turns on whether the likelihood ratio departs from 1. ...

... The simple likelihood ratio is the basic measure that dominates the forensic science literature on evaluative conclusions. However, most writers in this area construe the likelihood ratio as the ratio of posterior odds to prior odds and base its use on that purely Bayesian interpretation. Greater clarity would come from using the related term “Bayes factor” when this is the motivation for the ratio. 51/ [Note 51: The choice of words is not merely a labeling issue. In simple situations, the Bayes factor and the likelihood ratio are numerically equivalent, but more generally, there are conceptual and operational differences. For instance, simple likelihood ratios can be used to gauge relative support within any pair of hypotheses, even when the pair is not exhaustive. But when there are many hypotheses, the Bayes factor is not so simple. See [Peter M. Lee, Bayesian Statistics 140 (4th ed. 2012)], at 141–42. It becomes the usual numerator divided by a weighted sum of the likelihoods for each hypothesis. The weights are the probabilities (conditional on the falsity of the hypothesis in the numerator). For an example, see Tacha Hicks et al., A Framework for Interpreting Evidence, in Forensic DNA Evidence Interpretation 37, 63 (John S. Buckleton et al. eds., 2d ed. 2016). Furthermore, there is disagreement over the use of a likelihood ratio for highly multidimensional data (such as fingerprint patterns and bullet striations) and whether and how to express uncertainty with respect to the likelihood ratio itself. Compare Franco Taroni et al., Dismissal of the Illusion of Uncertainty in the Assessment of a Likelihood Ratio, 15 Law, Probability & Risk 1, 2 (2016), with M.J. Sjerps et al., Uncertainty and LR: To Integrate or Not to Integrate, That’s the Question, 15 Law, Probability & Risk 23, 23–26 (2016). ... ] ...

The obvious Bayesian measure of probative value is the Bayes factor (B). In the examples used here, B is equal to the likelihood ratio L, and therefore the statisticians’ “weight of evidence” is WOE = log-B = log-L. 58/ The value of L in these cases tells us just how much more the evidence supports one theory than another and hence—this is the Bayesian part—just how much we should adjust our belief (expressed as odds) for any starting point. For the PSA test for cancer, L = 7 is “the change in odds favoring disease.” A test with greater diagnostic value would have a larger likelihood ratio and induce a stronger shift toward that conclusion. ... [T]he likelihood-ratio measure (or variations on it), which keeps prior probabilities out of the picture, is more typically used to describe the value of test results as evidence of disease or other conditions in medicine and psychology. Using the same measure in law has significant advantages. ...

* David H. Kaye, Digging into the Foundations of Evidence Law, 115 Mich. L. Rev. 915 (2017)  (reviewing The Michael J. Saks & Barbara A. Spellman, Psychological Foundations of Evidence Law (2016)).

31. Vic Barnett, Comparative Statistical Inference 306 (3d ed. 1999) (“The principles of maximum likelihood and of likelihood ratio tests occupy a central place in statistical methodology.”); see, e.g., id. at 178–80 (describing likelihood ratio tests in frequentist hypothesis testing); N. Reid, Likelihood, in Statistics in the 21st Century 419 (Adrian E. Raftery et al. eds., 2002).

37. A “support function” can be required to have several appealing, formal properties, such as transitivity and additivity. E.g., A.W.F. Edwards, Likelihood 28–32 (Johns Hopkins Univ. Press, expanded ed. 1992) (1972). It also can be derived, in simple cases, from other, arguably more fundamental, principles. E.g., Barnett, supra note 31, at 310–11.

39. See generally I. J. Good, Weight of Evidence and the Bayesian Likelihood Ratio, in the Use of Statistics in Forensic Science 85 (C.G.G. Aitken & D.A. Stoney eds., 1991); I. J. Good, Weight of Evidence: A Brief Survey, in 2 Bayesian Statistics 249 (J.M. Bernardo et al. eds., 1985) (providing background information regarding the use of Bayesian statistics in evaluating weight of evidence). ...

42. If the individual were to keep some of the chips in reserve, the analogy between the fraction of them on a hypothesis and the kind of probability that pertains to random events such as games of chance would break down.

44. A deeper motivation for using logarithms may lie in information theory, but, if so, it is not important here. See Solomon Kullback, Information Theory and Statistics (1959).

58. The logarithm of B has been called “weight of evidence” since 1878. I.J. Good, A. M. Turing’s Statistical Work in World War II, 66 Biometrika 393, 393 (1979) ... . While working in the town of Banbury to decipher German codes, Alan Turing famously (in cryptanalysis and statistics, at least) coined the term “ban” to designate a power of 10 for this metaphorical weight. Good, supra, at 394. Thus, a B of 10 is 1 ban, 100 is 2 ban, and so on.

Saturday, May 20, 2017

Science Friday and Contrived Statistics for Hair Comparisons

On May 19th, Public Radio International's Science Friday show had a segment entitled "There’s Less Science In Forensic Science Than You Think." The general theme — that some practices have not been validated by rigorous scientific testing — is a fair (and disturbing) indictment. But listeners may have come away with the impression that the FBI has determined that hair examiners make up statistics from personal experience 95% of the time to help out prosecutors.

Ira Flato, the show's host, opened with the observation that "The FBI even admitted in 2015, after decades, investigators had overstated the accuracy of hair sample matches over 95% of the time in ways that benefited the prosecution." He returned to this statistic when he asked Betty Layne DesPortes, a lawyer and the current President of the American Academy of Forensic Sciences, the following question:
Dr. DesPortes, I want to go back to that FBI admission in 2015 that for decades investigators had overstated the accuracy of their hair samples, and I mean 95% of the time in a way that benefited the prosecution. Is this a form of cognitive bias coming into the picture?
Ms. DesPortes replied that
It is, and ... you would have overstatement along the lines of, "Well, I’ve never seen in my X years of experience that two hairs would be this similar, so it must be a match," and then they would just start making statistics up based on, "Well, I’ve had a hundred cases in my practice, and there have been a thousand cases in my lab, and nobody else has ever reported similar hairs like this," so let’s just start throwing in one in a hundred thousand as a statistic — "one in a hundred thousand" — and that’s where the misstatement came in.
But neither Ms. DesPortes nor anyone else knows how often FBI examiners cited statistics like "one in a hundred thousand" based on either their recollections of their own casework or their impression of the collective experience of all hair examiners. 1/

To be sure, such testimony would have been flagged as erroneous in the FBI-DOJ Microscopy Hair Comparison Review. But so would a much more scientifically defensible statement such as
The hair removed from the towel exhibited the same microscopic characteristics as the known hair sample, and I concluded it was consistent with having originated from him. However, hair comparison is not like fingerprints, for example. It’s not a positive identification. I can’t make that statement." 2/
The Hair Comparison Review was not designed to produce a meaningful estimate of an error rate for hair comparisons. It produced no statistics on the different categories of problematic testimony. The data and the results have not been recorded (at least, not publicly) so as to allow independent researchers to ascertain the extent to which FBI examiners overstated their findings in various ways. See David H. Kaye, Ultracrepidarianism in Forensic Science: The Hair Evidence Debacle, 72 Wash. & Lee L. Rev. Online 227 (2015).

The interim results from the Hair Comparison Review prompted the Department of Justice to plan a retrospective study of FBI testimony involving other identification methods as well. In July 2016, it asked a group of statisticians how best to conduct the new "Forensic Science Disciplines Review." The informal recommendations that emerged in this "Statisticians' Roundtable" included creating a database of testimony that would permit more rigorous, social science research. But this may never happen. A new President appointed a new Attorney General, who promptly suspended the expanded study.

  1. Ms. DesPortes may not have meant to imply that all the instances of exaggerated testimony were of the type she identified.
  2. That statements like these may be scientifically defensible does not render them admissible or optimal.
(For related postings, click on the label "hair.")

Tuesday, May 16, 2017

The Reappearing Rapid DNA Act

With bipartisan sponsorship, the Rapid DNA Act of 2017 (H.R.510 and S. 139) is sailing through Congress. The Senate bill made it to the legislative calendar on May 11, 2017, without amendment and without a written report from the Judiciary Committee.  The Committee Chairman, Senator Grassley, wrote this about the bill:
Turning to legislation, the first bill is S.139, the Rapid DNA Act of 2017. It is sponsored by Senator Hatch. The Committee reported this bill and the Senate passed it in the last Congress. The bill would establish standards for a new category of DNA samples that can be taken more quickly and then uploaded to our national DNA index. 1/
This characterization is misleading. The bill itself contains no standards for producing profiles to upload to the national database. It orders the FBI to “issue standards.” Specifically, the part of the bill entitled “standards” adds to the DNA Identification Act of 1994, 42 U.S.C. § 14131(a), a new Section 5, which reads as follows:
(A) ... the Director of the Federal Bureau of Investigation shall issue standards and procedures for the use of Rapid DNA instruments and resulting DNA analyses.
(B) In this Act, the term ‘Rapid DNA instruments’ means instrumentation that carries out a fully automated process to derive a DNA analysis from a DNA sample. 2/
But the FBI does not need new authorization to devise standards for “Rapid DNA instruments.” The “resulting DNA analyses” are not a new category of “samples,” and some such profiles already may be in the National DNA Index System (NDIS). In fact, the FBI issued standards for “rapid” profiles years ago. One need only peek at the FBI's forthright answers to “Frequently Asked Questions on Rapid DNA Analysis.” There, the FBI explained that
Based upon recommendations from the Scientific Working Group on DNA Analysis Methods (SWGDAM), the FBI Director approved and issued The Addendum to the Quality Assurance Standards for DNA Databasing Laboratories performing Rapid DNA Analysis and Modified Rapid DNA Analysis Using a Rapid DNA Instrument (or “Rapid QAS Addendum”). The Addendum contains the quality assurance standards specific to the use of a Rapid DNA instrument by an accredited laboratory; it took effect December 1, 2014.
The FBI added that “[a]n accredited laboratory participating in NDIS may use CODIS to upload authorized known reference DNA profiles developed with a Rapid DNA instrument performing Modified Rapid DNA Analysis to NDIS if [certain] requirements are satisfied” and that “DNA records generated by an NDIS-approved Rapid DNA system performing Rapid DNA analysis in an NDIS participating laboratory are eligible for NDIS.” 3/

But if the FBI does not need the bill to develop standards or to incorporate rapid-DNA results into NDIS, what is the real purpose of the bill? The answer is simple. The bill clears the way for these results to come, not from accredited laboratories, 4/ but from police stations, jails, or prisons. The House Judiciary Committee was explicit in its brief report on the bill:
Currently, booking stations have to send their DNA samples off to state labs and wait weeks for the results. This has created a backlog that impacts all criminal investigations using forensics, not just forensics used for identification purposes. H.R. 510 would modify the current law regarding DNA testing and access to CODIS. The short turnaround time resulting from increased use of Rapid DNA technology would help to quickly eliminate potential suspects, capture those who have committed a previous crime and left DNA evidence, as well as free up current DNA profilers to do advanced forensic DNA analysis, such as crime scene analysis and rape-kits. 5/
The FBI was more succinct when it referred to “the goal of using Rapid DNA systems in the booking environment” and reported that “legislation will be needed in order for DNA records that are generated by Rapid DNA systems outside an accredited laboratory to be uploaded to NDIS.6/

Is the migration of DNA profiling from the laboratory to the police station — and potentially to the officer on the street — a good idea? The efficiency argument from the House Committee has some force. We do not demand that only accredited laboratories conduct breath alcohol testing of drivers who seem to be intoxicated. Police using properly maintained portable instruments can do the job. 7/

How is DNA different? In one respect, it is less problematic than roadside alcohol testing. Rapid DNA analysis is not for crime-scene samples. (At least, not yet.) It is for samples from arrestees or convicted offenders whose profiles can be uploaded to a database. The police have an incentive to avoid uploading inaccurate profiles. Such profiles will degrade the effectiveness of the database. Any cold hits that they might produce will be shown to be false when a later DNA test from the suspect fails to replicate the incorrect profile. In contrast, incriminating output of a faulty alcohol test usually enables a conviction and will not be shown to be in error.

But there is more to the matter than efficiently generating and uploading profiles. It could be argued that DNA information is more private that a breath alcohol measurement and that having CODIS profiles known to local police is more dangerous than having it known only to laboratory personnel. Considering the limited kind of information that is present in a CODIS profile, however, this argument does not strike me as compelling.


The Rapid DNA Act of 2017 met no opposition as the Senate and House passed the bills. S. 139 generated unanimous consent (and no discussion) on May 16. 8/ Its counterpart, H.R. 510, passed after receiving praise from two of its sponsors and the observation from Representative Goodlatte (R-VA) that "this is a good bill. It is a bipartisan bill. I thank Members on both sides of the aisle for their contributions to this effort." 9/

  1. Prepared Statement by Senator Chuck Grassley of Iowa, Chairman, Senate Judiciary Committee Executive Business Meeting, May 11, 2017,, viewed May 16, 2017.
  2. Rapid DNA Act of 2017, S. 139 § 2(a).
  3. The difference between “Rapid DNA Analysis” and “Modified Rapid DNA Analysis” is that the former is “a “swab in – profile out” process ... of automated extraction, amplification, separation, detection, and allele calling without human intervention,” whereas the latter uses “human interpretation and technical review” for ascertaining the alleles in a profile. FBI, Frequently Asked Questions on Rapid DNA Analysis,, Nos. 1 &2, viewed May 17, 2017.
  4. The DNA Identification Act of 1994, 42 U.S.C. § 14131, which the Rapid DNA Act amends, requires the FBI to create and consider the recommendations of "an advisory board on DNA quality assurance methods." § 14131(a)(1)(A).  The members of the board must come from "nominations proposed by the head of the National Academy of Sciences and professional societies of crime laboratory officials." Id. They "shall develop, and if appropriate, periodically revise, recommended standards for quality assurance, including standards for testing the proficiency of forensic laboratories, and forensic analysts, in conducting analyses of DNA." § 14131(a)(1)(C). As the name indicates, the board is purely advisory. The Act only demands that
    The Director of the Federal Bureau of Investigation, after taking into consideration such recommended standards, shall issue (and revise from time to time) standards for quality assurance, including standards for testing the proficiency of forensic laboratories, and forensic analysts, in conducting analyses of DNA.
    § 14131(a)(2).
    The advisory board was a half-a-loaf response to the recommendation of a National  Academy of Sciences committee for "a National Committee on Forensic DNA Typing (NCFDT) under the auspices of an appropriate government agency, such as NIH or NIST, to provide expert advice primarily on scientific and technical issues concerning forensic DNA typing." NRC Committee on DNA Technology in Forensic Science, DNA Technology in Forensic Science 72-73 (1992). Now that NIST has established an Organization of Scientific Area Committees for Forensic Science to develop science-based standards for DNA testing and other forensic science methods, Congress should reconsider the need for the overlapping FBI board.
  5. On May 11, 2017, the House Committee on the Judiciary recommended adoption of H.R. 510 without holding hearings. The Judiciary Committee saw no need to consult independent scientists. It was satisfied with the fact that
    the Judiciary Committee’s Subcommittee on Crime, Terrorism, Homeland Security and Investigations held a hearing on a virtually identical bill, H.R. 320, on June 18, 2015, [at which] testimony was received from: Ms. Amy Hess, Executive Assistant Director of Science and Technology, Federal Bureau of Investigation; Ms. Jody Wolf, Assistant Crime Laboratory Administrator, Phoenix Police Department Crime Laboratory, President, American Society of Criminal Laboratory Directors; and Ms. Natasha Alexenko, Founder, Natasha’s Justice Project.
    Report to accompany H.R. 510, May 11, 2017,
  6. FBI Answers, No. 13,, viewed May 17, 2017 (emphasis added).
  7. “As of January 1, 2017, there is no Rapid DNA system that is approved for use by an accredited forensic laboratory for performing Rapid DNA Analysis.” Several systems had been approved but they do “not contain the 20 CODIS Core Loci required as of January 1, 2017.” FBI Answers, No. 6,, viewed May 16, 2017. 
  8. 163 Cong. Rec. S2954-2955, 115th Cong., 1st Sess., May 16, 2017.
  9. Id. at H4205.