IMPACT OF EVIDENCE By BAYES, this is carried by the LIKELIHOOD RATIO Appropriate subject of expert testimony? Instruct jury on how to combine LR with prior odds?
IMPACT OF A LR OF 100 PRIOR.001.01.1.3.5.7.9 POSTERIOR.09.5.92.98.99.996.999 Probability of Guilt
IDENTIFICATION EVIDENCE M = DNA match B = other background evidence Assume – “match probability” MP
PROSECUTOR’S ARGUMENT The probability of a match having arisen by innocent means is 1/10 million. So= 1/10 million – i.e.is overwhelmingly close to 1. – CONVICT
DEFENCE ARGUMENT Absent other evidence, there are 30 million potential culprits 1 is GUILTY (and matches) ~3 are INNOCENT and match Knowing only that the suspect matches, he could be any one of these 4 individuals So –ACQUIT
BAYES POSTERIOR ODDS = (10 MILLION) “PRIOR” ODDS PROSECUTOR’S argument OK if Only BAYES allows for explicit incorporation of B DEFENCE argument OK if
DENIS ADAMS –Match probability = 1/200 million 1/20 million 1/2 million Doesn’t fit description Victim: “not him” Unshaken alibi No other evidence to link to crime Sexual assault DNA match
Court presented with LR for match Instruction in Bayes’s theorem Suggested LR’s for defence evidence Suggested priors before any evidence
PRIOR 150,000 males 18-60 in local area DEFENCE EVIDENCE B=D&A D: Doesn’t fit description/victim does not recognise A: Alibi
POSTERIOR Match probability1/200m1/20m1/2m Posterior.98.85.35
Trial –Appeal – Retrial – Appeal “usurps function of jury” “jury must apply its common sense” BAYES rejected – HOW? SALVAGE? 1.Use “Defence argument” 2.Apply other evidence
DATABASE SEARCH Rape, DNA sample No suspect Search police database, size 10,000 Find single “match”, arrest Match probability 1/1 million EFFECT OF SEARCH??
DEFENCE – (significantly) weakens impact of evidence PROSECUTION We have eliminated 9,999 potential culprits – (slightly) strengthens impact of evidence
BAYES Prosecutor correct 1.Suspect is guilty 2.Some one in database is guilty Defence switches hypotheses – equivalent AFTER search – but NOT BEFORE Different priorsDifferent likelihood ratio – EFFECTS CANCEL!
CONCLUSIONS Interpretation of evidence raises deep and subtle logical issues STATISTICS and PROBABILITY can address these BAYES’S THEOREM is the cornerstone Need much greater interaction between lawyers and statisticians