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Who is “random man”?
COMMENTARY
Who is "random man"? JS BUCKLETON, KAJ WALSH
Chemistry Division, Department of Scientific and Industrial Research, PO Box 2224, Auckland, New Zealand and
Home Ofice Central Research and Support Establishment, Aldermaston, Reading, Berkshire, United Kingdom RG 7 4PN Survey data are essential in evaluating forensic evidence. The choice of survey is discussed for simple forensic situations. Key Words: Forensic interpretation; Bayes' theorem; Surveys. Journal of the Forensic Science Society 1991; 31: 463-468 Received 27 March 1990; accepted 10 December 1990
Introduction The complete interpretation of forensic evidence requires a synergy of theory and application. In many ways, the theory, or at least a theoretical framework, is provided if Bayesian inference is accepted as a means of assessing evidence. Bayes' theorem directs us to consider the ratio of two probabilities: the conditional probability of the evidence calculated under the assumption that the suspect did commit the crime divided by the conditional probability of the evidence calculated under the assumption that the suspect did not commit the crime. It is the challenge of applying these ideas to real situations that the interpretation researchers in forensic science must now accept. It is necessary to survey people unassociated with the crime to understand the probability of the evidence under the assumption that the suspect did not commit it. Equally, transfer and persistence experiments or their equivalent are required, to understand the probability of the evidence under the assumption that the suspect did, in fact, commit the crime. In a good many instances, data of this type are already available. Examples of differing types of population are the survey of persons attending a gymnasium or changing into uniform, performed at the Northern Ireland Forensic Science Laboratory in Belfast (McQuillan J, personal communication); the survey by Pearson, May and Dabbs [I] of suits submitted to a dry cleaners; and the survey of non-matching glass on the clothing of crime suspects reported by Harrison, Lambert and Zoro [2]. JFSS 1991; 31(4): 463-468
463
In some instances, clear shortcomings in the existing survey data can be identified and future experimental effort must be directed into these areas. Typically, the denominator of the likelihood ratio is modelled as the probability of this evidence for a person selected at random from the population; but to what population should we refer? Should it be persons completely unconnected with crime or should it be persons of the type likely to come to the attention of the police? Which are the correct data? The answer is often not clear-cut, but it is certain that any data are valuable. Such surveys as the three above can be compared, and much valuable information can be gained to aid in the assessment of forensic evidence.
Discussion It is the exact form of the conditional probability in the denominator of the likelihood ratio that defines which population should be used as a model. Typically we are seeking the probability of the evidence ( E ) in the light of the background ( I ) and given that the suspect did not commit the crime (c). This can be written p ( E I C, I ) , and to demonstrate the choice of survey data, the following two situations will be considered. Transfer from the offender to the scene (suspect details irrelevant under C ) There is a deceased victim and a second bloodstain at the scene that is different from the blood from the victim. From the circumstances it is possible to assume that this stain is blood from the offender (I). Evett [3] has considered this situation and states "If the suspect had not committed the crime, then clearly some other person must have been responsible. The [background] information gives us no reason to confine our attention to any particular group in the community." What effect could the nature or lifestyle of the suspect have if he was not the person who left the blood at the scene ( C ) ? In this case, under C, we require the probability that the offender was of a particular blood type. Clearly, under C, the suspect is not the offender and, therefore, this possibility must not condition our thinking. We need to survey all possible offenders. If, under Evett's assumption, there is no reason in the background information to direct us to any sector of the community, then the best that we can do is to model blood types in the total population. However, the population to be surveyed can be modified if there is some information which would cause us to reconsider our choice of population. An example would be if there was an eyewitness to the crime who reliably reported that the offender was of a particular race, an unlikely possibility. We therefore require a survey representative of all possible offenders. However, from the general population in the area of the crime, some individuals will be more likely to be offenders and some will be less likely. Young children and women may be excluded as possible offenders in a rape 464
JFSS 1991; 31(4): 463-468
case, for instance. It will seldom be possible to define the population of possible offenders accurately in all its aspects but this is probably not necessary. If, for instance, we require a frequency for a particular blood group from the population of possible offenders, as we do in the hypothetical case given above, then it is known that age and sex are not factors but race is. We must concentrate on the racial composition of the population of possible offenders. If there is no cause in the background information concerning the case to direct us wholly or partially towards a race or races, then the best that can be done is to model the general population. In this example, the race, nature, or lifestyle of the suspect in no way influences our thinking on which population to choose. Therefore, in our consideration of C, the suspect details were irrelevant. Transfer from the scene to the suspect (suspect details important under C) In some cases the suspect may be all important under C. Consider the instance where there is a deceased victim stabbed numerous times, and a suspect who has a history of violence has been apprehended with a heavy bloodstain on his jacket. The stain is not the suspect's own blood. In a real case, this would be only part of the evidence. There would, in most cases, also be blood typing evidence. It will, however, suffice here to consider merely the evidential value of a heavy non-self bloodstain on a suspect's clothing.
What is the probability of this evidence assuming that the suspect did not commit the crime ( C ) ? We do not know if the suspect committed the crime (C) or not (C). That is the issue that the court is considering. We need to consider the probability of a heavy non-self stain on the clothing of the suspect if he did not commit this crime, and this can be influenced in two ways. The suspect can offer an alternative explanation as to how a heavy non-self stain came to be on his clothing. The jury could then assess the veracity of his explanation and assign a probability to the occurrence of the evidence. If the story is totally substantiated then the probability is 1. Anything less than 1 acknowledges that there is some doubt as to the veracity of the alternative explanation. However, the suspect is under no obligation to offer an alternative explanation and often chooses not to do so; the onus of proof is on the prosecution. The only option open to the forensic scientist is to model the suspect using a survey of others like him. Ideally we require identical twins of the suspect, all leading the same lifestyle as the suspect. This is never possible. The best compromise must therefore be taken. JFSS 1991; 31(4): 463-468
465
A survey should be used of persons as alike as possible to the suspect in whatever are the key features of his behaviour or lifestyle. Even though the "perfect" survey is never available, the scientist should not despair. It is better to have some data available than none at all, and by using good judgement it will often be possible to modify existing survey data to compensate for the case variables.
In this second hypothetical case, the key feature of the suspect might be his violent background. It is plausible that persons of violent background have more non-self blood on their clothing than do most people. We therefore require a survey of persons of violent background. This might be adequately provided by the work done by Briggs [4]. In a large homicide investigation, 122 suspects who were largely vagrants, alcoholics, and violent sex deviants, were studied. This was the data used by Evett and Buckleton [5] when they considered the likelihood ratio for the transfer situation discussed in this hypothetical case of transfer to the suspect. It might be argued that the data used were flawed by having been skewed towards persons of violent background. Rather than being flawed, it can be argued that it is more appropriate whenever the suspect belongs to a group that could be expected to have an elevated incidence of non-self blood on clothing. Other instances quoted of such groups are mothers, sportspersons, security personnel, and ambulance drivers. It might be thought that the supposition of a violent background should not be beneficial to the defendant's case and that the likelihood ratio should not be lower for persons with violent backgrounds. It must be remembered that the prosecution must prove this particular charge, and the admission of involvement in separate acts of violence, either as an innocent or a guilty party, is a viable, although unlikely, defence. It is even possible that the defence to the charge of assaulting person A could be to admit assaulting person B, therefore explaining the presence of non-self blood. Taking this to its final conclusion, the finding of non-self blood on an office worker would result in a larger likelihood ratio than the same finding on a gang member. Equally, but not strictly the scientist's domain, the prior odds on the office worker would be lower than on the gang member. Certainly no one could doubt that the result of using the Briggs data would be conservative if anything, in that any error is in the favour of the suspect. In this second hypothetical example, it was the nature and lifestyle of the suspect that determined the type of population to survey and we were considering the probability of this evidence on the suspect, assuming that he did not commit the crime Therefore in our consideration of the suspect details are important.
(c).
466
c,
JFSS 1991; 31(4): 463-468
With the current state of forensic surveying, it is difficult to come up with a great number of examples but in the field of glass transfer there are a number of surveys that differ in the populations that have been surveyed. The surveys conducted by the Belfast laboratory (MacQuillan J, personal communication) and Pearson et a1 [I] largely represent people with no known predisposition to having glass on their clothing. This can be contrasted with the survey of Harrison et a1 [2] of non-matching glass on suspects in glass cases. This latter survey represents a population that could be expected to show an elevated incidence of spurious glass, although the number of people with no glass (or no non-matching glass) on them is reasonably similar between the surveys (ranging from 0.37 for the drycleaners survey to 0.40 for the Belfast work). There are a number of characteristics of a suspect that could lead us to believe that he would belong to a group that would have elevated levels of glass on his clothing. The most commonly quoted is that he is a glazier. It is, however, possible that there are a large number of other factors that could be significant. Amongst these could be a job in the refuse or demolition industries, or a lifestyle that exposed the person to breaking glass such as frequenting rough pubs. It is perfectly possible that a suspect in a glass case has more in common with suspects in other glass cases than he has with a random selection from the population. For these reasons, the work of Harrison et a1 [2] should not be discounted. Another instance of a survey of suspects is the work of Fong and Inami [6], in which clothing items from suspects predominantly in offences against the person were searched exhaustively for fibres that were subsequently grouped and identified. There may be many instances where data of this type is valuable, particularly to demonstrate the number and type of fibres expected on suspects in offences against the person.
Conclusion There is no doubt that survey data are essential in the application of Bayesian inference. It is necessary to consider what type of data is required when assessing the denominator of the likelihood ratio. This assessment is best done by considering the form of the conditional probability required for the denominator. Therefore, data developed from previous cases should not be dismissed out of hand and may indeed be very useful. If the transfer was to the suspect, then data from previous cases may well be appropriate. Our thinking in such a case will be conditioned by the nature and lifestyle of the suspect. If, however, the transfer was to the scene, then information about the suspect is irrelevant in calculating the probability under C. JFSS 1991; 31 (4): 463-468
467
References 1. Pearson EF, May RW and Dabbs MGD. Glass and paint fragments found in men's outer clothing-report of a survey. Journal of Forensic Sciences 1971; 16: 283-300. 2. Harrison PH, Lambert J A and Zoro JA. A survey of glass fragments recovered from clothing of persons suspected of involvement in crime. Forensic Science International 1985; 27: 171-187. 3. Evett IW. What is the probability that this blood came from that person? A meaningful question? Journal of the Forensic Science Society 1983; 23: 35-39. 4. Briggs TJ. The probative value of bloodstains on clothing. Medicine, Science and the Law 1978; 18: 79-83. 5. Evett IW and Buckleton JS. Some aspects of the Bayesian approach to evidence evaluation. Journal of the Forensic Science Society 1989; 29: 317-324. 6. Fong W and Inami SH. Results of a study to determine the probability of chance match occurrences between fibres known to be from different sources. Journal of Forensic Sciences 1986; 31: 65-72.
JFSS 1991; 31(4): 463-468
Who is "random man"? JS BUCKLETON, KAJ WALSH
Chemistry Division, Department of Scientific and Industrial Research, PO Box 2224, Auckland, New Zealand and
Home Ofice Central Research and Support Establishment, Aldermaston, Reading, Berkshire, United Kingdom RG 7 4PN Survey data are essential in evaluating forensic evidence. The choice of survey is discussed for simple forensic situations. Key Words: Forensic interpretation; Bayes' theorem; Surveys. Journal of the Forensic Science Society 1991; 31: 463-468 Received 27 March 1990; accepted 10 December 1990
Introduction The complete interpretation of forensic evidence requires a synergy of theory and application. In many ways, the theory, or at least a theoretical framework, is provided if Bayesian inference is accepted as a means of assessing evidence. Bayes' theorem directs us to consider the ratio of two probabilities: the conditional probability of the evidence calculated under the assumption that the suspect did commit the crime divided by the conditional probability of the evidence calculated under the assumption that the suspect did not commit the crime. It is the challenge of applying these ideas to real situations that the interpretation researchers in forensic science must now accept. It is necessary to survey people unassociated with the crime to understand the probability of the evidence under the assumption that the suspect did not commit it. Equally, transfer and persistence experiments or their equivalent are required, to understand the probability of the evidence under the assumption that the suspect did, in fact, commit the crime. In a good many instances, data of this type are already available. Examples of differing types of population are the survey of persons attending a gymnasium or changing into uniform, performed at the Northern Ireland Forensic Science Laboratory in Belfast (McQuillan J, personal communication); the survey by Pearson, May and Dabbs [I] of suits submitted to a dry cleaners; and the survey of non-matching glass on the clothing of crime suspects reported by Harrison, Lambert and Zoro [2]. JFSS 1991; 31(4): 463-468
463
In some instances, clear shortcomings in the existing survey data can be identified and future experimental effort must be directed into these areas. Typically, the denominator of the likelihood ratio is modelled as the probability of this evidence for a person selected at random from the population; but to what population should we refer? Should it be persons completely unconnected with crime or should it be persons of the type likely to come to the attention of the police? Which are the correct data? The answer is often not clear-cut, but it is certain that any data are valuable. Such surveys as the three above can be compared, and much valuable information can be gained to aid in the assessment of forensic evidence.
Discussion It is the exact form of the conditional probability in the denominator of the likelihood ratio that defines which population should be used as a model. Typically we are seeking the probability of the evidence ( E ) in the light of the background ( I ) and given that the suspect did not commit the crime (c). This can be written p ( E I C, I ) , and to demonstrate the choice of survey data, the following two situations will be considered. Transfer from the offender to the scene (suspect details irrelevant under C ) There is a deceased victim and a second bloodstain at the scene that is different from the blood from the victim. From the circumstances it is possible to assume that this stain is blood from the offender (I). Evett [3] has considered this situation and states "If the suspect had not committed the crime, then clearly some other person must have been responsible. The [background] information gives us no reason to confine our attention to any particular group in the community." What effect could the nature or lifestyle of the suspect have if he was not the person who left the blood at the scene ( C ) ? In this case, under C, we require the probability that the offender was of a particular blood type. Clearly, under C, the suspect is not the offender and, therefore, this possibility must not condition our thinking. We need to survey all possible offenders. If, under Evett's assumption, there is no reason in the background information to direct us to any sector of the community, then the best that we can do is to model blood types in the total population. However, the population to be surveyed can be modified if there is some information which would cause us to reconsider our choice of population. An example would be if there was an eyewitness to the crime who reliably reported that the offender was of a particular race, an unlikely possibility. We therefore require a survey representative of all possible offenders. However, from the general population in the area of the crime, some individuals will be more likely to be offenders and some will be less likely. Young children and women may be excluded as possible offenders in a rape 464
JFSS 1991; 31(4): 463-468
case, for instance. It will seldom be possible to define the population of possible offenders accurately in all its aspects but this is probably not necessary. If, for instance, we require a frequency for a particular blood group from the population of possible offenders, as we do in the hypothetical case given above, then it is known that age and sex are not factors but race is. We must concentrate on the racial composition of the population of possible offenders. If there is no cause in the background information concerning the case to direct us wholly or partially towards a race or races, then the best that can be done is to model the general population. In this example, the race, nature, or lifestyle of the suspect in no way influences our thinking on which population to choose. Therefore, in our consideration of C, the suspect details were irrelevant. Transfer from the scene to the suspect (suspect details important under C) In some cases the suspect may be all important under C. Consider the instance where there is a deceased victim stabbed numerous times, and a suspect who has a history of violence has been apprehended with a heavy bloodstain on his jacket. The stain is not the suspect's own blood. In a real case, this would be only part of the evidence. There would, in most cases, also be blood typing evidence. It will, however, suffice here to consider merely the evidential value of a heavy non-self bloodstain on a suspect's clothing.
What is the probability of this evidence assuming that the suspect did not commit the crime ( C ) ? We do not know if the suspect committed the crime (C) or not (C). That is the issue that the court is considering. We need to consider the probability of a heavy non-self stain on the clothing of the suspect if he did not commit this crime, and this can be influenced in two ways. The suspect can offer an alternative explanation as to how a heavy non-self stain came to be on his clothing. The jury could then assess the veracity of his explanation and assign a probability to the occurrence of the evidence. If the story is totally substantiated then the probability is 1. Anything less than 1 acknowledges that there is some doubt as to the veracity of the alternative explanation. However, the suspect is under no obligation to offer an alternative explanation and often chooses not to do so; the onus of proof is on the prosecution. The only option open to the forensic scientist is to model the suspect using a survey of others like him. Ideally we require identical twins of the suspect, all leading the same lifestyle as the suspect. This is never possible. The best compromise must therefore be taken. JFSS 1991; 31(4): 463-468
465
A survey should be used of persons as alike as possible to the suspect in whatever are the key features of his behaviour or lifestyle. Even though the "perfect" survey is never available, the scientist should not despair. It is better to have some data available than none at all, and by using good judgement it will often be possible to modify existing survey data to compensate for the case variables.
In this second hypothetical case, the key feature of the suspect might be his violent background. It is plausible that persons of violent background have more non-self blood on their clothing than do most people. We therefore require a survey of persons of violent background. This might be adequately provided by the work done by Briggs [4]. In a large homicide investigation, 122 suspects who were largely vagrants, alcoholics, and violent sex deviants, were studied. This was the data used by Evett and Buckleton [5] when they considered the likelihood ratio for the transfer situation discussed in this hypothetical case of transfer to the suspect. It might be argued that the data used were flawed by having been skewed towards persons of violent background. Rather than being flawed, it can be argued that it is more appropriate whenever the suspect belongs to a group that could be expected to have an elevated incidence of non-self blood on clothing. Other instances quoted of such groups are mothers, sportspersons, security personnel, and ambulance drivers. It might be thought that the supposition of a violent background should not be beneficial to the defendant's case and that the likelihood ratio should not be lower for persons with violent backgrounds. It must be remembered that the prosecution must prove this particular charge, and the admission of involvement in separate acts of violence, either as an innocent or a guilty party, is a viable, although unlikely, defence. It is even possible that the defence to the charge of assaulting person A could be to admit assaulting person B, therefore explaining the presence of non-self blood. Taking this to its final conclusion, the finding of non-self blood on an office worker would result in a larger likelihood ratio than the same finding on a gang member. Equally, but not strictly the scientist's domain, the prior odds on the office worker would be lower than on the gang member. Certainly no one could doubt that the result of using the Briggs data would be conservative if anything, in that any error is in the favour of the suspect. In this second hypothetical example, it was the nature and lifestyle of the suspect that determined the type of population to survey and we were considering the probability of this evidence on the suspect, assuming that he did not commit the crime Therefore in our consideration of the suspect details are important.
(c).
466
c,
JFSS 1991; 31(4): 463-468
With the current state of forensic surveying, it is difficult to come up with a great number of examples but in the field of glass transfer there are a number of surveys that differ in the populations that have been surveyed. The surveys conducted by the Belfast laboratory (MacQuillan J, personal communication) and Pearson et a1 [I] largely represent people with no known predisposition to having glass on their clothing. This can be contrasted with the survey of Harrison et a1 [2] of non-matching glass on suspects in glass cases. This latter survey represents a population that could be expected to show an elevated incidence of spurious glass, although the number of people with no glass (or no non-matching glass) on them is reasonably similar between the surveys (ranging from 0.37 for the drycleaners survey to 0.40 for the Belfast work). There are a number of characteristics of a suspect that could lead us to believe that he would belong to a group that would have elevated levels of glass on his clothing. The most commonly quoted is that he is a glazier. It is, however, possible that there are a large number of other factors that could be significant. Amongst these could be a job in the refuse or demolition industries, or a lifestyle that exposed the person to breaking glass such as frequenting rough pubs. It is perfectly possible that a suspect in a glass case has more in common with suspects in other glass cases than he has with a random selection from the population. For these reasons, the work of Harrison et a1 [2] should not be discounted. Another instance of a survey of suspects is the work of Fong and Inami [6], in which clothing items from suspects predominantly in offences against the person were searched exhaustively for fibres that were subsequently grouped and identified. There may be many instances where data of this type is valuable, particularly to demonstrate the number and type of fibres expected on suspects in offences against the person.
Conclusion There is no doubt that survey data are essential in the application of Bayesian inference. It is necessary to consider what type of data is required when assessing the denominator of the likelihood ratio. This assessment is best done by considering the form of the conditional probability required for the denominator. Therefore, data developed from previous cases should not be dismissed out of hand and may indeed be very useful. If the transfer was to the suspect, then data from previous cases may well be appropriate. Our thinking in such a case will be conditioned by the nature and lifestyle of the suspect. If, however, the transfer was to the scene, then information about the suspect is irrelevant in calculating the probability under C. JFSS 1991; 31 (4): 463-468
467
References 1. Pearson EF, May RW and Dabbs MGD. Glass and paint fragments found in men's outer clothing-report of a survey. Journal of Forensic Sciences 1971; 16: 283-300. 2. Harrison PH, Lambert J A and Zoro JA. A survey of glass fragments recovered from clothing of persons suspected of involvement in crime. Forensic Science International 1985; 27: 171-187. 3. Evett IW. What is the probability that this blood came from that person? A meaningful question? Journal of the Forensic Science Society 1983; 23: 35-39. 4. Briggs TJ. The probative value of bloodstains on clothing. Medicine, Science and the Law 1978; 18: 79-83. 5. Evett IW and Buckleton JS. Some aspects of the Bayesian approach to evidence evaluation. Journal of the Forensic Science Society 1989; 29: 317-324. 6. Fong W and Inami SH. Results of a study to determine the probability of chance match occurrences between fibres known to be from different sources. Journal of Forensic Sciences 1986; 31: 65-72.
JFSS 1991; 31(4): 463-468