=Paper=
{{Paper
|id=Vol-2669/paper10
|storemode=property
|title=Explaining Legal Decisions Using IRAC
|pdfUrl=https://ceur-ws.org/Vol-2669/paper10.pdf
|volume=Vol-2669
|authors=Trevor Bench-Capon
|dblpUrl=https://dblp.org/rec/conf/comma/Bench-Capon20
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==Explaining Legal Decisions Using IRAC==
https://ceur-ws.org/Vol-2669/paper10.pdf
Explaining Legal Decisions Using IRAC
Trevor Bench-Capon
Deoartment of Computer Science, University of Liverpool, Liverpool, UK.
tbc@csc.liv.ac.uk
Abstract. We suggest that the Issue, Rule, Application, Conclusion
(IRAC) method can be used to produce a natural explanation of legal
case outcomes. We show how a current methodology for representing
knowledge of legal cases can be used to provide such explanations.
Keywords: Reasoning with cases · Explanation · Legal Reasoning
1 Introduction
Explanations given by AI and Law systems are often rather stilted and formulaic.
In order to try to produce more natural explanations of legal cases we turn for
inspiration to how lawyers are taught to discuss cases. IRAC stands for Issue,
Rule, Application and Conclusion and is a methodology for legal analysis widely
taught in US law schools1 as a way of answering hypothetical questions posed
during teaching by the Socratic method and in exams.
– The issue is the legal question to be answered. This is couched in terms
specific to the particular case rather than in general terms. Thus can infor-
mation communicated to specific employees be considered a Trade Secret?
rather than the generic was the information a Trade Secret? 2 .
– The rule, or rules, are the rules that are used to answer the question in the
issue. For example a rule might be information disclosed to employees is only
regarded as confidential if covered by a specific non disclosure agreement 3 .
– The rule must then be applied to the facts of the particular case being
considered. For example: The defendant was an employee of the plaintiff,
and signed a general non disclosure agreement, but the particular information
was not specifically mentioned.
– The conclusion is the answer to the legal question. In our example: the
information is not regarded as Trade Secret since it was not covered by a
specific non disclosure agreement.
1
For example, City University of New York (https://www.law.cuny.edu/legal-
writing/students/irac-crracc/irac-crracc-1/) and Elazabeth Haub School of Law at
Pace University (https://academicsupport.blogs.pace.edu/2012/10/26/the-case-of-
the-missing-a-in-law-school-you-cant-get-an-a-without-an-a/).
2
My examples are from the US Trade Secrets domain widely used in AI and Law [7].
3
Legal information such as this is for illustration only, and may not be an accurate
reflection of the law.
CMNA’20 - F. Grasso, N. Green, J. Schneider, S. Wells (Eds) - 8th September 2020
Copyright c 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
Explaining Legal Decisions Using IRAC 75
Sometimes additional elements are included, such as Rule Proof. Rule Proof
is a justification of the rule, citing the statute or case on which it is based.
Thus in our example, one could cite MBL (USA) Corp. v. Diekman which was
found for the plaintiff on the grounds that although a general employer-employee
confidentiality agreement had been signed, “the court found that defendant and
other employees were not told what, if anything, plaintiff considered confidential”
(President Justice Downing). AI systems addressing the question of reasoning
with legal cases have always attempted to explain their reasoning. Indeed the
ability to provide explanations is considered (e.g. [8]) to be a major advantage
of such systems over systems based on machine learning algorithms such as [13].
The key point about IRAC is that it is specifically tailored to the case at
hand: it indicates what is important and different about the particular case under
discussion, and uses the specific facts of the case to apply the general rule. This
is different from the standard forms of explanation of case outcomes found in AI
and Law, which go through every potential issue in the domain, obscuring the
key point, and bottom out in generally applicable factors rather than specific
facts. We suggest that IRAC is a more natural form of explanatory argument,
just as arguments are more natural than watertight logical proofs since they can
use enthymemes to suppress trivial and generally known premises and focus on
the real point. In this paper we will consider how current representations can be
adapted to provide IRAC style explanations.
2 Background: the US Trade Secrets Domain
US Trade Secrets has been widely used as a domain in AI and Law since its
introduction in the HYPO system [15]. We will use as our primary example
the US Trade Secrets case of The Boeing Company v. Sierracin Corporation, as
modelled for the CATO system [4] and used in [1].
Fig. 1. Top level of Trade Secrets Domain from [11]
The top level of the US Trade Secrets domain is shown in Figure 1, taken
from [11]. These are called issues in [4] and [11]. Below the issues are a number of
factors, stereotypical patterns of fact that are legally significant and favour one
or other side of the argument. In CATO [4] and many subsequent approaches,
76 Trevor Bench-Capon
including IBP [11] and ANGELIC [1], issues and factors are organised in a
hierarchy, descending through abstract factors recognising what Aleven terms
“intermediate concerns” until the base level factors, the legal facts of the case,
are reached. Figure 2 shows the branch of the hierarchy relating to whether the
information is a Trade Secret, the left hand branch of Figure 1. CATO’s issues
are a way of structuring the case, but are rather more generic than the issues
which form the ‘I’ of IRAC, which are supposed to indicate what is particular to
the case under consideration. Below we will refer to IRAC issues as case-issues.
Fig. 2. Factor Hierarchy for branches relating to Info-Trade-Secret, taken from [4]
We will use a slightly simplified version of Boeing 4 , with six factors, five
pro-plaintiff and one pro-defendant. Boeing was found for the plaintiff.
The factors, the side favoured and their associated CATO issues are:
– F4 NonDisclosureAgreement (plaintiff); Maintain-Secrecy, ConfidentialRela-
tionship. This also appears in the branch not shown in Fig 2.
– F6 SecurityMeasures (plaintiff): Maintain-Secrecy
– F10 SecretsDisclosedOutsiders (defendant): Maintain-Secrecy
– F12 OutsiderDisclosuresRestricted (plaintiff): Maintain-Secrecy
– F14 RestrictedMaterialsUsed (plaintiff): Improper-Means (not in Fig 2)
– F21 KnewInfoConfidential (plaintiff): Confidential-Relationship
4
The simplification is that we do not use F1, Disclosure-in Negotiations, since we
consider it subsumed by F10, since the defendant is an outsider.
Explaining Legal Decisions Using IRAC 77
The F numbers are the identifiers given in [4], shown in Figure 2, and used
in subsequent discussions such as [1] and the Tables below.
Here it should be noted that since improper (although not criminal) means
were used to obtain the information, by improperly exploiting restricted ma-
terials, there is no question but that the information should be regarded as
misappropriated. So even though there is some support for the other arm, Con-
fidentialRelationship, it is not needed: ImproperMeans alone is enough for mis-
appropriation So the issue is whether information disclosed to outsiders can be
considered a trade secret. Here the answer is that it can be, since the disclosures
were subject to restrictions, showing that the information was considered secret
by the plaintiff, and adequate efforts had been taken to maintain the secrecy.
The value of the information is not disputed, and so can be assumed by default.
3 Standard explanation in AI and Law
One approach to building an AI and Law system is to formalise the relevant legal
knowledge and to elicit the facts of the particular case by querying the user. This
is the approach of the classic [17], which formalised the British Nationality Act.
The explanation was the standard how? typical of expert systems of the time.
This approach remains relevant today, as shown by the ANGELIC methodology
[1], which has recently been used to build a fielded system [3]. In [1] the Boeing
case was used as an example. The proof trace of the Prolog program was post-
processed to give the following explanation (note that the ANGELIC program
uses defaults to resolve issues for which there are no base level factors):
We find for plaintiff. The information was a trade secret: efforts were
taken to maintain secrecy, since disclosures to outsiders were restricted
and the defendant entered into a non-disclosure agreement and other se-
curity measures were applied. The information was unique. It is accepted
that the information was valuable and it is accepted that the informa-
tion was neither known nor available. A trade secret was misappropri-
ated: there was a confidential relationship since the defendant entered
into a non-disclosure agreement and it is accepted that the information
was used. Moreover improper means were used since the defendant used
restricted materials.
This follows the usual pattern of a rule based how? explanation: each com-
ponent of the and/or tree of Figure 1 is considered, and the result justified by
citing lower level rules until the base level factors (given or accepted by default)
are reached. The explanation has a conclusion and a rule (here implicit, although
older programs such as [17] cite the rule explicitly). What is missing here is the
focus provided by the case-issue: the explanation covers all the elements without
any effort to identify the important point, or to say why the factors are present.
Case based reasoning in AI and Law also typically considers the case as a
whole. What would happen in HYPO is that the case base would be searched
until the closest match in terms of shared factors was found. A possible match,
78 Trevor Bench-Capon
taken from the limited set of publicly available cases listed in [12], is Trandes
Corp. v. Guy F. Atkinson Co. Trandes had just three factors, all shared with
Boeing: AgreedNotToDisclose, SecurityMeasures and SecretsDisclosedOutsiders
(again we diregard F1 as subsumed by F10). It was found for the plaintiff. A case
based explanation (based on the explanation of a different case in [16]) would
cite the factors in common:
Where the plaintiff had taken security measures, the defendant had
agreed not to disclose the information and information had been dis-
closed to outsiders, Plaintiff should win claim. Cite Trandes.
The defendant might now reply: In Trades the disclosures were of a lesser extent
than Boeing. This distinction cannot be made with Boolean factors as used in
CATO, IBP and [1], but is available in [9], where factors can have magnitudes.
For rebuttal the plaintiff can say that in Boeing the disclosures to outsiders
were restricted: this is not so in Trandes, which makes Boeing significantly
stronger than Trandes. This style of factor based explanation remains relevant,
and is currently advocated as a means of explaining systems based on Machine
Learning techniques [10]
It might now be asked why Trandes was found in favour of the plaintiff
when the information had been disclosed to outsiders without restriction. From
a reading of the decision it can be seen that these disclosures were held to be
too limited to be of significance: “Although Trandes did give WMATA a single
demonstration disk in contemplation of a licensing agreement, it did so only in
confidence. Such limited disclosure does not destroy secrecy” (opinion delivered
by Williams, Circuit Judge). This may cast doubt on whether this factor should
have been ascribed to Trandes at all. The issue in Trandes was really whether
the disclosures had been sufficient to compromise the secret, whereas in Boeing
they clearly were. Because the case based explanation uses the whole case it may
rely on similarities which were not relevant to the crucial issue. Again, what is
missing is the focus the case-issue provides.
A key point about these explanations is that they consider the case as a whole.
Thus the rule based explanation goes through all the CATO issues without
distinguishing on which of them the case turns. The case based explanation
considers all factors in common with the precedent. Neither focus on a specific
issue, particular to the case, which is what we want for our IRAC case-issue.
A second point is that we can see two types of case-issue. In Boeing we
have two factors favouring different sides in the same branch of the tree: Se-
cretsDisclosedOutsiders and OutsiderDisclosuresRestricted, bringing into ques-
tion whether or not their parent factor, EffortsMadeToMaintainSecrecyVis-a-
VisOutsiders, is present. If it is not, then, as can be seen in Figure 2, we can
decide that the efforts to maintain secrecy were not adequate and so the infor-
mation cannot be regarded as a trade secret. We term this a conflict-issue. In
Trandes, in contrast, the issue is whether or not we consider that information
was disclosed to outsiders to a sufficient extent: that is whether we should con-
sider this factor to be meaningfully present in the case. We will term this an
ascription-issue.
Explaining Legal Decisions Using IRAC 79
4 Issues in AI and Law
As mentioned above, CATO [4] used issue to describe the top levels of its abstract
factor hierarchy. This practice was also used in Issue Based Prediction (IBP) [11],
[5] and the ANGELIC methodology [1], [2]. CATO was concerned with helping
law students to distinguish a case, and so does not give an explanation of the
outcome. It does, however, use issues to organise its advice. IBP, however, was
directed towards predicting an outcome. It first identified the relevant issues,
those with at least one factor present in the case in its sub-tree, and where there
were opposing factors relevant to an issue, used matching precedents to choose
a resolution. The explanation provided, like that from ANGELIC given above,
proceeds on an issue by issue basis, rather than identifying and focussing on the
crucial case-issue, but at least uncontested issues are ignored.
In the remainder of the paper I will discuss how an IRAC explanation can
be produced from a system constructed using the ANGELIC methodology.
5 CATO in ANGELIC
The ANGELIC methodology produces an ADF corresponding to the factor hi-
erarchy of [4], part of which is shown in Fig 2. Each node of the ADF can have
positive or negative children. The ADF for the issues are shown in Table 1. The
ADF for the abstract factors are shown in Table 2. Factors present or inferred
in Boeing are in bold, those in Trandes in italics. Base Level factors are given
using CATO F numbers [4] and Fig 2. The value and use of in the information
was not contested, and so no related factors were mentioned in either case.
CATO ID Name Positive Children Negative Children
F200 TradeSecretMisappropriation F201 , F203 F124
F203 InfoTradeSecret F102 , F104
F104 InfoValuable F8, F15 F105
F102 EffortstoMaintainSecrecy F6 , F122, F123 F19, F23, F27
F201 InfoMisappropriated F110, F112, F114
F112 InfoUsed F7, F8, F18 F17
F114 ConfidentialRelationship F115, F121
F110 ImproperMeans F111 F120
Table 1. IBP Logical Model as an ADF taken from [1]. Factors present in
Boeing are in bold. Factors in Trandes are in italics
6 Modelling IRAC with ANGELIC
As we saw above, issues can be of two types: conflict based and ascription based.
We will consider each in turn.
80 Trevor Bench-Capon
CATO ID Name Positive Children Negative Children
F105 InfoKnownOrAvailable F106, F108
F106 InfoKnown F20, F27 F15, F123
F108 InfoAvailableElsewhere F16, F24
F111 QuestionableMeans F2, F14, F22, F26 F1, F17, F25
F115 NoticeOfConfidentiality F4, F13, F14, F21 F23
F120 LegitimatelyObtainable F105 F111
F121 ConfidentialityAgreement F4 F5, F23
F122 MaintainSecrecyDefendant F4 F1
F123 MaintainSecrecyOutsiders F12 F10
F124 DefendantOwnershipRights F3
Table 2. CATO factors as ADF taken from [1]. Factors present in Boeing
are in bold. Factors in Trandes are in italics
6.1 Conflict Issues
Examination of Table 1 shows that there are no case-issues at the CATO issue
level, because there are no negative children. Two leaf issues in Fig 1 are relevant,
EffortstoMaintainSecrecy and InfoMisappropriated (ConfidentialRelationship is
only relevant when InfoUsed is also relevant and InfoValuabe is uncontested),
but since both have only positive factors, the case is clearly decidable for the
plaintiff at this level. So, to find the case-issue we must delve deeper down the
tree, and examine Table 2.
In Table 2 we see that there are is one factor with conflicting base level factors,
MaintainSecrecyOutsiders. This conflict will thus be our case-issue. MaintainSe-
crecyOutsiders, if resolved differently, could have destroyed the plaintiff’s claim
by removing EffortstoMaintainSecrecy, and hence showing the information not
to be regarded as a Trade Secret. This then is the case-issue on which the case
turns. Since any case-issue requires a factor with a positive child and a negative
child, we can now state a case-issue using a template of the form Can the plaintiff
be considered to Parent when Negative Child given that Positive Child? :
– Can the plaintiff be considered to MaintainSecrecyOutsiders when Secrets-
DisclosedOutsiders given that OutsiderDisclosuresRestricted?
We now examine the acceptance conditions for the node F123 at which the
conflict occurs. We take the acceptance conditions from [1]:
ACCEPT IF OutsiderDisclosuresRestricted
REJECT IF SecretsDisclosedOutsiders
ACCEPT
The default ACCEPT indicates that the burden of proof for this factor is on
the defendant, and the order of the conditions indicate the priority of the two
rules. The acceptance conditions can be annotated with the case or cases which
led to their inclusion. These can then be used as the Rule Proof, if that element
is required. The rule of the case is thus
Explaining Legal Decisions Using IRAC 81
– MaintainSecrecyOutsiders if OutsiderDisclosuresRestricted
The application is that OutsiderDisclosuresRestricted is present in the case,
and so the antecedent is satisfied. This could be elaborated using specific facts
taken from the decision “Boeing did not lose its secrets through confidential
disclosure of drawings to Libbey. The secrets were preserved by first Libbey’s and
then Sierracin’s promise to keep the information confidential.” (opinion delivered
by Justice Dore). We suggest an extension of the ANGELIC methodology so that
that when a case is analysed into factors, the extract from the opinion on which
the ascription was based is recorded, so that it can be used in the explanation.
The conclusion is that Boeing did MaintainSecrecyOutsiders, and hence made
adequate efforts to maintain secrecy, and that they had a Trade Secret which
was misappropriated through the use of restricted materials. We now have all
the elements of IRAC, which can be used as a natural explanation, focussing on
what is important in the particular case.
In Boeing the issue is whether the plaintiff can be considered to have
maintained secrecy with respect to outsiders when the plaintiff disclosed
the information to outsiders when these outsider disclosures were re-
stricted? We apply the rule: if outsider disclosures are restricted then
secrecy with respect to outsiders is maintained. In Boeing, the secrets
were preserved by first Libbey’s and then Sierracin’s promise to keep
the information confidential. Thus secrecy with respect to outsiders was
maintained, and the information can be regarded as a trade secret.
6.2 Ascription Based Issues
We now consider Trandes. In Trandes there were no confidentially restrictions
on the disclosures to outsiders, and so it would seem a straightforward case in
which the information is not a trade secret because it was in the Public Domain.
This, however, is only the case if we insist that factors are Boolean. This is
true of CATO, but not of HYPO, which used dimensions indicating the extent
to which a particular feature favoured a party. Recently, the recognition that
factors are present to differing extents is increasingly recognised (e.g. [14]). The
ANGELIC methodology has been extended to accommodate factors present to
differing extents as reported in [9]. In [9] this was applied to CATO, and one of
the factors treated as non-Boolean was SecretsDisclosedOutsiders. In Williams’
decision in Trandes we can find the form of the alleged disclosure:
The advertisement described in general terms the capabilities of the Tun-
nel System and offered a demonstration disk for $100. The demonstration
disk did in fact contain an object-code version of the Tunnel System, but
was configured to operate in demonstration mode only.
Note that the users did not have open access to the source code constituting
the Trade Secret. So the disclosure would seem to be minimal: moreover the
advertisement attracted very few enquiries. Thus for Trandes, we may include
82 Trevor Bench-Capon
SecretsDisclosedOutsiders (as argued by the defendant), but the extent will be
small and so may fail to meet the threshold needed to defeat MaintainSecrecy-
Outsiders. Thus the information can continue to be seen as a trade secret.
The issue here is thus whether distribution of a demonstration disk containing
object level code configured to operate in demonstration mode only means that
inadequate security measures were taken. The rule that was applied by Williams
was “limited disclosure does not destroy secrecy”, with Space Aero v Darling
cited as the precedent. We now apply the rule by stating that the disclosure in
Trades was sufficiently limited.
Thus the ascription issue can be identified by looking for a non boolean
factor, in particular one below or close to the threshold at which it will influence
the acceptability of its parent. In Trandes the factor SecurityMeasures also has
magnitude, but these were quite extensive, and so the presence of this factor is
not in dispute. The conclusion is that Trandes did MaintainSecrecyOutsiders,
and hence made adequate efforts to maintain secrecy, and that they had a Trade
Secret which was misappropriated through the use of information disclosed to
the defendant in a relation of confidence.
In Trandes the application of the rule that can be extracted from the rep-
resentation is simply the claim that the disclosures were too limited to destroy
secrecy, backed up the quotation showing the nature of the disclosure. We may,
however, encounter rather more extensive discussion of whether the factor should
be applied or not. This may require true analogical reasoning, as discussed in
[18]. Her example was a hypothetical based on Dillon v. Legg in which the issue
is whether a kindergarten teacher who witnessed an accident involving one of her
pupils can be considered sufficiently analogous to a mother (the relationship in
Dillon) to receive damages for emotional distress. On the one hand both are in a
close caring relationship with the child, but on the other there is no blood rela-
tionship: the precedent in question simply states that “how closely the plaintiff
was related to the victim” is what needs to be considered. Here we have a HYPO
style dimension and need to decide where to fix the point at which it switches
from pro-plaintiff to pro-defendant [14]. In [6] it was suggested that such reason-
ing requires too much common sense knowledge of the world to be achievable in
current AI and Law systems. A rich ontology might be produced for past cases
and so used for teaching, but not one broad enough to cover arbitrary future
cases, as would be required for prediction.
Thus, for ascription issues, we must be content with the limited notion of
application provided by thresholds. If a good discussion of application is, as the
second url in footnote 1 suggests, required for an A grade, we must perhaps be
content with a B minus.
7 Conclusion
In this paper we have proposed the IRAC method as a natural way of explaining
legal cases, which focusses on the central point on which the case turns. We
showed how this might be produced from a factor based representation such is
Explaining Legal Decisions Using IRAC 83
produced by the ANGELIC methodology [1]. We further identified two types
of issue: conflict issues, which turn on opposing factors relating to a common
parent, and ascription issues, turning on whether a factor is present to a sufficient
extent. The representation allows us to deal satisfactorily with conflict issues,
but even if we can represent the extent to which factors are present in a case
our explanation of the application of rules relating to ascription issues will be
limited in cases where true analogical reasoning is needed.
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