Risk	assessment	tools	in	criminal	justice	and	forensic psychiatry:	the	need	for	better	data [This	is	a	pre-publication	version.	The	final	version	is	forthcoming	in	European	Psychiatry	©	<2016>.	This manuscript	version	is	made	available	under	the	CC-BY	4.0	license] Thomas	Douglas1,	Jonathan	Pugh1,	Ilina	Singh1,2,	Julian	Savulescu1,	Seena	Fazel2,3 1. Oxford	Uehiro	Centre	for	Practical	Ethics,	Faculty	of	Philosophy,	University	of	Oxford,	Suite	8, Littlegate	House,	St	Ebbes	Street,	Oxford	OX1	1PT,	United	Kingdom 2. Department	of	Psychiatry,	University	of	Oxford,	Warneford	Hospital,	Oxford	OX3	7JX,	United Kingdom 3. Oxford	Health	NHS	Foundation	Trust,	Warneford	Hospital,	Oxford	OX3	7JX,	United	Kingdom Summary.	Violence	risk	assessment	tools	are	increasingly	used	within	criminal	justice	and	forensic psychiatry,	however	there	is	little	relevant,	reliable	and	unbiased	data	regarding	their	predictive accuracy.	We	argue	that	such	data	are	needed	to	(i)	prevent	excessive	reliance	on	risk	assessment scores,	(ii)	allow	matching	of	different	risk	assessment	tools	to	different	contexts	of	application,	(iii) protect	against	problematic	forms	of	discrimination	and	stigmatisation,	and	(iv)	ensure	that contentious	demographic	variables	are	not	prematurely	removed	from	risk	assessment	tools. Declaration	of	Interest.	SF	has	published	research	on	risk	assessment,	including	as	part	of	a	team that	has	derived	and	validated	one	tool	for	prisoners	with	psychiatric	disorders. Keywords:	Violence;	Forensic	psychiatry;	Ethics	and	human	rights;	Risk	assessment;	Crime prediction;	Racial	profiling;	Discrimination. There	are	currently	more	than	200	structured	tools	available	for	assessing	risk	of	violence	in	forensic psychiatry	and	criminal	justice.[1]	These	are	widely	deployed	to	inform	initial	sentencing,	parole decisions,	and	decisions	regarding	post-release	monitoring	and	rehabilitation.	In	some	jurisdictions, including	Canada,	New	Zealand,	and	until	2012	the	United	Kingdom,	risk	assessment	tools	are	or	were also	used	to	justify	indeterminate	post-sentence	detention.	In	addition,	violence	risk	assessment	tools are	used	to	inform	decisions	regarding	detention,	discharge,	and	patient	management	in	forensic	and, increasingly,	general	psychiatry. This	article	highlights	some	potential	ethical	problems	posed	by	risk	assessment	tools	and	argues	that better	data	on	predictive	accuracy	are	needed	to	mitigate	these.	It	focuses	on	the	use	of	risk	assessment tools	in	forensic	psychiatric	and	criminal	justice	settings. Professional	obligations	and	competing	values In	the	psychiatric	literature,	criticism	of	risk	assessment	has	focused	on	the	possibility	that,	in	deploying risk	assessment	tools,	mental	health	professionals	may	fail	to	fulfil	their	professional	obligations	to	their patients.[2,3]	Health	professionals	are	expected	to	make	the	care	of	their	patients	their	first	concern,	to build	trust	and	to	respect	patient	preferences,	and	this	is	reflected	in	professional	guidelines.[4]	Some argue	that	the	use	of	risk	assessment	tools	is	unjustified	when	it	is	intended	to	realise	other	values,	such as	justice	or	public	protection,	and	does	not	benefit	the	assessed	individual.[5,6,7,8]	Buchanan	and Grounds	hold	that	"it	is	inappropriate	to	comment	on	a	defendant's	risk	unless	psychiatric	intervention is	proposed	or	other	benefit	will	result".[9]	Similarly,	Mullen	claims	that	"[r]isk	assessments	.	. are	the proper	concern	of	health	professionals	to	the	extent	that	they	initiate	remedial	interventions	that directly	or	indirectly	benefit	the	person	assessed".[10] The	use	of	risk	assessment	tools	is	perhaps	most	clearly	at	odds	with	the	interests	of	the	assessed individual	where	the	tool	is	used	to	inform	decisions	regarding	post-sentence	detention.	In	this	context, the	default	position	is	that	the	person	will	be	released;	however,	if	the	tool	indicates	a	high	risk	of violence,	detention	may	be	extended.	It	could	be	argued	that	deploying	the	tool	thus	runs	against	the individual's	interest	in	being	released	as	soon	as	possible. In	other	cases,	however,	the	application	of	a	risk	assessment	tool	will	benefit	the	individual.	There	are	at least three ways in which it could confer such a benefit. First, the risk assessment may be used to identify	beneficial	treatments.	Second,	the	use	of	a	risk	assessment	tool	may	facilitate	an	earlier	release or	discharge.	Suppose	an	individual	is	being	considered	for	parole	or	discharge	from	a	secure	psychiatric institution,	but	this	is	likely	to	be	refused	on	the	basis	that	there	is	insufficient	evidence	for	a	low	risk	of violence. In	this	situation,	application	of	a	risk	assessment	tool	may	provide	the	evidence	necessary	to secure	an	end to	detention. Third, even	when	a risk assessment results in further	detention, it	might nevertheless	confer	a	benefit	because	extended	detention	is	itself	in	the	individual's	best	interests.	For example,	it	may	prevent	re-offending	and	an	even	longer	period	of	detention	in	the	future. Moreover, even when mental health professionals administer risk assessments that are against the assessed	individual's	best	interests,	it	is	not	clear	they	thereby	violate	a	professional	obligation,	for	the view	that	medical	professionals	ought	never	to	compromise	a	patient's	best	interests	can	be	contested. In	the	setting	of	infectious	disease	control	it	would	be	widely	accepted	that	physicians	may	sometimes compromise a patient's best interests in order to promote other values, such as the health of family members	and	the	wider	public.[11,12]	Similarly,	many	would	hold	that	an	obstetrician	may	sometimes act to	protect a future child, even if this comes at some cost to the	patient-that is, the	prospective mother.[13]	It	can	be	argued	that	a	parallel	point	holds	in	relation	to	forensic	psychiatry:	professionals	in this	field	may	sometimes	give	precedence	to	values	besides	the	welfare	of	their	own	patients.[14]	Those who	hold that risk assessment tools should be	used	only	when they	benefit the patient	may thus be overstating	the	ethical	difficulties	created	by	such	tools. Nevertheless, the presence of competing values in risk assessment does create a potential ethical problem: it is possible that some values will be unjustifiably sacrificed for the sake of others. For example, there is a risk that the interests of individual patients or prisoners will be unjustifiably compromised	in	the	name	of	public	protection,	or	the	reverse.	We	will	argue	that	a	lack	of	high	quality data	on	predictive	accuracy	compounds	this	ethical	risk. Predictive	accuracy Existing data suggest that most risk assessment tools have poor to moderate accuracy in most applications.	Typically,	more	than	half	of	individuals	classified	by	tools	as	high	risk	are	false	positives- they	will	not	go	on	to	offend.[15]	These	persons	may	be	detained	unnecessarily.	False	positives	may	be especially	common	in	minority	ethnic	groups.[16,17] Rates of false negatives are usually much lower. Nevertheless, in typical cases around 9% of those classed	as	low	risk	will	go	on	to	offend.[18]	These	individuals	may	be	released	or	discharged	too	early, posing	excessive risk to the	public. Such failures	of	negative	prediction	are frequently	associated	with significant	controversy	and	outrage,	as	reactions	to	recent	high	profile	cases	demonstrate.[19] The prevalence of prediction errors does not entirely undermine the rationale for deploying risk assessment tools. To balance risk to the public against the interests of the assessed individual, some method	for	assessing	risk	is	required,	and	risk	assessment	tools,	even	if	limited	in	accuracy,	may	be	the best	option	available.	However,	to	mitigate	the	risk	of	inadequate	or	excessive	detention,	the	limitations of risk assessment tools need to be well understood and factored into clinical and criminal justice responses. Unfortunately,	published	validation	findings for	the	most	widely	used	tools,	which	allow	for	predictive accuracy	to	be	estimated	in	advance,	frequently	present	a	misleading	picture.[20]	First,	though	there	are exceptions,	most	tools	have	not	been	externally	validated	outside	of	their	derivation	sample.[21,22]	Of particular	concern,	few	validation	studies	have	been	conducted	in	women,	ethnic	minority	populations, and	individuals	motivated	by	religious	or	political	extremism.	[23,24,25]	Consequently,	it	is	unclear	how far reported accuracy findings can be extrapolated to new settings and populations.[26,27] Second, there	is	strong	evidence	that	conflicts	of	interest	are	often	not	disclosed	in	this	field,	and	some	evidence of publication and authorship bias.[28] (Authorship bias occurs when research on tools tends to be published	by	the	authors	of	those	tools,	who	typically	find	better	performance.)	Third,	published	studies frequently	present	only	a	small	number	of	performance	measures	that	do	not	provide	a	full	picture	of predictive	accuracy.	[29] Thus, not only is the predictive accuracy of risk assessment tools imperfect, it is also imperfectly presented in the literature.	The limited	and	skewed	evidence	base	creates	a risk that	decision	makers will	rely	more	heavily	on	risk	assessment	scores	than	their	accuracy	warrants.	To	mitigate	this	risk,	there is	a	need	for	better	quality	data	covering	more	subpopulations.	Validation	studies	should	include	more than just	one	or two	performance statistics, and	data	on the	numbers	of true	and false	positives	and negatives	should	be	clearly	presented.	Conflict	of	interests	need	to	be	disclosed,	and	reviews	by	authors with	financial	conflict	of	interests	should	be	treated	with	caution. In addition to risking over-reliance on risk assessment scores, deficiencies in the evidence base also generate at least three	more specific problems,	which	we explain below: they (i) thwart attempts to match risk assessment tools to different contexts of application, (ii) complicate efforts to determine whether risk assessment tools are unjustifiably discriminatory or stigmatising, and thereby (iii) contribute to a risk that contentious demographic variables will be prematurely eliminated from assessment	tools. The	right	tool	for	the	context Selecting the optimal risk assessment tool for a given application requires trade-offs to be made between false negatives and false positives; attempts to reduce the number of false positives will increase the number of false negatives.[30] Tools with a low rate of false negatives (due to high sensitivity)	will	be	most	effective	at	protecting	the	public,	and	may	garner	most	political	support,	while tools	with	a	low	rate	of	false	positives	(due	to	high	specificity)	will	best	protect	the	rights	and	interests of	prisoners	and	psychiatric	patients. The	optimal	balance	between	false	positives	and	false	negatives is	an	ethical	decision	and	will	depend on	the	social	and	political	context in	which	the	tool is to	be	used.[31]	For	example,	avoidance	of false positives may be more important in jurisdictions with less humane detention practices than in jurisdictions with more humane practices, since the less humane the conditions of detention, the greater	the	harm	false	positives	will	tend	to	impose	on	the	assessed	individual.[32] The	appropriate	balance	between	false	positives	and	false	negatives	will	also	depend	on	the	stage	in	the criminal justice	process	or patient pathway at	which the tool	will be	deployed. For instance, suppose that	a	risk	assessment	tool	is	used	to	inform	decisions	about	post-sentence	detention	in	a	setting	where an	individual's	initial	sentence	is	designed	to	be	proportionate	to	their	degree	of	responsibility	and	the seriousness of the crime. In this case, detaining the individual beyond the end	of the initial sentence involves	imposing	a	disproportionately	long	period	of	detention.	In	this	context,	special	care	should	be taken	to	avoid	false	positives,	and	there	may	be	grounds	to	prefer	a	tool	with	a	very	low	false	positive rate	to	one	that	is	overall	more	accurate. However,	the	situation	is	different	when	a	tool	is	used	to	inform	parole	decisions.	In	this	context,	false positives	may	lead	to	refusal	of	parole	and	an	unnecessarily	long	period	of	incarceration	from	the	point of	view	of	public	protection.	Yet if	we	assume	that	the	initial	sentences	are	themselves	proportionate, then	the	overall	period	of	detention	for	'false	positive'	individuals	will	remain	within	the	limits	required by	proportionality.	In	this	context	it	may	be	more	important	to	avoid	false	negatives. Matching risk assessment tools to different contexts of application thus requires trade-offs between positive	and	negative	predictive	accuracy.	For	each	context,	we	must	first	decide	which	type	of	accuracy to prioritise to which degree, and then select a tool that reflects this priority. Unfortunately, in the absence	of reliable	data, it is	not	possible to	make the latter	decision	confidently.	There is	a	need for studies	using	representative	samples	for	relevant	subpopulations,	avoiding	highly	selected	samples,	and presenting performance measures that allow false negative and false positive rates to be reliably estimated	for	a	particular	application. Discrimination	and	stigmatisation Some	argue	that	singling	out	individuals	for	unfavourable	treatment	on	the	basis	of	their	demographic characteristics	amounts	to	unjustified	discrimination.	This	criticism	is	often	levelled	at	racial	profiling	by police	and	airport security.[33]	A similar concern	might	be raised regarding risk	assessment tools that take	into	account	an	individual's	demographic	characteristics	such	as	ethnicity,	age,	immigration	status and gender. It has been suggested that risk assessment tools should employ only 'individualised' information,	such	as	information	about	declared	plans	and	desires	based	on	face	to	face	interviews,[34, 35] though, even then, judgments may be subject to implicit biases based on the demographic characteristics	of	the	individual	being	assessed.[36] However, the requirement to utilize only individualised information is overly restrictive. Some	would argue that	demographic	profiling is	discriminatory,	or	problematically so,	only	when the	demographic variables used are recognised social groups (such as ethnic or gender groups),[37] or certain kinds	of recognised social groups, for instance, those whose membership is unchosen,[38] or that have historically been subject to oppression.[39] Risk assessment tools could theoretically exclude such variables. In	reply,	it	might	be	argued	that	exclusion	of	such	variables	is	insufficient	to	avoid	moral	concerns.	First, even if the problematic demographic variables are formally excluded from the analysis, they may continue to exert an influence; there remains the potential for implicit bias in the application of risk assessment tools and interpretation of risk scores.[40,41,42] Second, even if the problematic demographic	variables	are	formally	excluded	from	the	analysis	and	there	is	no	implicit	bias	in	applying the	tools,	there	may	still	be	a	correlation	between	membership	of	certain	demographic	groups	and	risk score.	For	example,	members	of	a	particular	ethnic	group	may	be	more likely than	average	to	receive high	risk	scores.	Some	may	hold	that	such	a	correlation	is	itself	problematic,	especially	if	it	is	due	to	past wrongdoing	against	members	of	the	demographic	group	in	question	(e.g.,	members	of	the	ethnic	group are indeed	more likely to offend, but only because they are victims of unjust social exclusion), if the correlation does not reflect a true difference in risk (e.g., false positives occur	more frequently than average	in	the	minority	ethnic	group),	or	if	the	correlation	is	likely	to	lead	to	stigmatisation	of	the	group deemed	to	be	higher	risk. However,	even	if	the	use	of	risk	assessment	tools	does	involve	a	problematic	form	of	discrimination	or stigmatisation,	it	could	nevertheless	be	justified	if	the	case	in	favour	of	using	the	information	is	powerful enough.	The	parallel	with racial	profiling in	airport screening is instructive	here.	Airport screening is	a limited resource and there are reasons to deploy it to detect the maximum number of would-be terrorists. If profiling enables a far greater number of terrorist attacks to be prevented with the resources available than any other policy, and if the cost to those profiled is low, then it is arguably justified	even if somewhat	problematic, for	example,	because	discriminatory	or	stigmatising.	Similarly, the	resources	available	for	the	prevention	of	violence	are	limited,	and	if	deploying	a	risk	assessment	tool prevents	far	more	violence	than	could	otherwise	be	prevented	with	the	resources	available,	it	might	be justified	even	if	it	does	raise	some	concerns	about	discrimination	and	stigmatisation. Nevertheless,	it	is	important	that	risk	assessment	tools	deploy	the	most	specific	predictive	information available.	Arguably,	what	is	most	objectionable	about	some	forms	of	racial	profiling	is	that	they	deploy racial	appearance	as	a	predictor	when	more	specific	predictors	of	security	threat	are	available	and,	were these predictors used, racial appearance would add no further predictive value.[43,44] In such circumstances,	use	of	racial	appearance	seems	unnecessary. Similarly, it may be problematic to use demographic predictors in risk assessment tools when	more specific predictors of future offending are available and these predictors would render the use of demographic	categories	redundant. Unfortunately, the lack of good evidence on accuracy	makes it difficult to ascertain	whether existing tools do use the	most specific predictors available. To determine this, we	would need to be able to compare	the	accuracy	of	more	specific	and	less	specific	tools	using	relevant,	reliable	and	unbiased	data on accuracy. Currently deployed tools frequently do use demographic factors such as age and immigration status as predictors, and although recent evidence suggests that including such demographic	factors	improves	predictive	accuracy,[45,46]	further	data	are	needed	to	confirm	this. In the	absence	of these	data, there	are two risks.	On the	one	hand,	mental health	professionals	may continue to employ coarse demographic variables that result in unnecessary discrimination or stigmatisation.	On	the	other,	given	growing	public	concern	regarding the	use	of such	variables,[47,48] professionals or policy	makers	may	prematurely remove them from risk assessment tools.[49] Before variables are removed because they are potentially contentious, high quality research that uses transparent	methods	and	presents	all relevant	outcomes	should investigate	whether	the	demographic factors	included	in	current	tools	add	incremental	validity	to	tool	performance. Thomas Douglas, Senior Research Fellow, Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy,	University	of	Oxford	and	Brasenose	College,	Oxford;	Jonathan	Pugh,	Postdoctoral	Research Fellow, Oxford Uehiro Centre for Practical Ethics; Ilina Singh, Professor of Neuroscience and Society, Department	of	Psychiatry,	University	of	Oxford,	Oxford	Uehiro	Centre	for	Practical	Ethics,	Oxford	Ethox Centre;	Julian	Savulescu,	Uehiro	Professor	of	Practical	Ethics,	Oxford	Uehiro	Centre	for	Practical	Ethics, Faculty of Philosophy, University of Oxford; Seena Fazel, Wellcome Trust Senior Research Fellow, Department	of	Psychiatry,	University	of	Oxford. Correspondence: Seena Fazel, Department of Psychiatry, Medical Sciences Division, University of Oxford,	Warneford	Hospital,	Oxford,	OX3	7JX,	United	Kingdom.	Email:	seena.fazel@psych.ox.ac.uk Funding: This work was supported by grants from the Wellcome Trust [100705/Z/12/Z], (WT086041/Z/08/Z,	#095806,	WT104848/Z/14/Z),	and	the	Uehiro	Foundation	on	Ethics	and	Education. References 1	Singh	JP,	Desmarais	SL,	Hurducas	C,	Arbach-Lucioni	K,	Condemarin	C,	Dean	K,	et	al.,	"International Perspectives	on	the	Practical	Application	of	Violence	Risk	Assessment:	A	Global	Survey	of	44	Countries". 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