=Paper=
{{Paper
|id=Vol-3281/paper1
|storemode=property
|title=Care Robots Learning Rules of Ethical Behavior Under the Supervision of an Ethical Teacher (Short paper)
|pdfUrl=https://ceur-ws.org/Vol-3281/paper1.pdf
|volume=Vol-3281
|authors=Abeer Dyoub,Stefania Costantini,Ivan Letteri
|dblpUrl=https://dblp.org/rec/conf/lpnmr/DyoubCL22
}}
==Care Robots Learning Rules of Ethical Behavior Under the Supervision of an Ethical Teacher (Short paper)==
https://ceur-ws.org/Vol-3281/paper1.pdf
Care Robots Learning Rules of Ethical Behavior
Under the Supervision of an Ethical Teacher
Abeer Dyoub1,∗ , Stefania Costantini1 and Ivan Letteri1
1
DISIM, University of L’Aquila
Abstract
Care robots are viewed as promising technological development that has the potential to mitigate the
increasing imbalance between the number of older adults needing care and a decreasing number of
caregivers. However, there are growing concerns about the ethical behavior of these robots. In this
work, we show how care robots can learn logical ethical rules of behavior from experience under the
supervision of human teacher.
Keywords
Roboethics, Answer Set Programming, Rule Learning
1. Introduction
Motivation and Background Care robots are examples of some of the Artificial Intelligence
(AI) systems that are expanding rapidly. These kinds of systems usually need to engage in
complex interactions with humans. To ensure that these systems will not violate the rights of
human being and also will carry out only ethical actions (i.e., actions that follow acceptable
ethical principles). There is a growing need to ethically reflect on the care robots behavior. In
fact, the field of roboethics addresses these issues. Roboethics is concerned with what rules
should be created for robots to ensure their ethical behavior and how to design ethical robots
[1]. The problem of adopting ethical approach to AI has been attracting a lot of attention
in the last few years. Unethical AI and bots have become a public concern, with these bots
entering our everyday life and performing many tasks on our behalves. This attention and
concern has manifested in many efforts trying to regulate the ethical behavior of AI systems, as
examples, we mention the European Guidelines for Trustworthy AI [2], the Universal Guidelines
for Artificial Intelligence 1 , OECD AI Principles 2 , the UNISCO Recommendation on the Ethics of
HYDRA - RCRA 2022: 1st International Workshop on HYbrid Models for Coupling Deductive and Inductive ReAsoning
and 29th RCRA workshop on Experimental evaluation of algorithms for solving problems with combinatorial explosion
∗
Corresponding author.
Envelope-Open abeer.dyoub@univaq.it (A. Dyoub); stefania.costantini@univaq.it (S. Costantini); ivan.letteri@univaq.it
(I. Letteri)
GLOBE https://www.abeerdyoub.com/ (A. Dyoub); https://www.disim.univaq.it/stefaniacostantini (S. Costantini);
https://www.ivanletteri.it (I. Letteri)
Orcid 0000-0002-0877-7063 (A. Dyoub); 0000-0001-7116-9338 (S. Costantini); 0000-0002-3843-386X (I. Letteri)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
CEUR Workshop Proceedings (CEUR-WS.org)
Proceedings
http://ceur-ws.org
ISSN 1613-0073
1
https://thepublicvoice.org/ai-universal-guidelines/
2
https://oecd.ai/en/ai-principles
Artificial Intelligence 3 , and the very recent AI Act proposal [3].
Building trust between humans and robots is just like building trust between humans. Care
robots can build trust by behaving ethically and being transparent about their behavior. Ethics
should be a core consideration of any action taken by a care robot. With care robots still in
a stage of relative infancy, the discovery of new ethical issues is likely to continue. Robots
should have the ability to learn continuously from these emerging cases and build their guiding
principles and ethical standards.
However, programming ethical norms into these bots is not an easy task. Codes of ethics
and conduct in the medical domain are abstract and general rules: autonomy, non-maleficence,
beneficence and justice [4]. Therefore they are quite difficult to apply. Moreover, they often
contain open textured terms that cover a wide range of specific situations [5]. They are subject
to interpretations and may have different meanings in different contexts. Thus, there is an
implementation problem from the computational point of view. It is difficult to use deductive
logic to address such a problem [6]. It is impossible for experts to define intermediate rules to
cover all possible situations. medical ethical principles in their abstract form are very difficult
to apply in real situations [7]. Furthermore, codes might conflict between them. All the above
mentioned reasons make learning from experience and generalization crucial for judgment and
decision making in future cases. We need to teach our bots the ethical norms of the domain
in which they need to be deployed. Bots could, similarly to humans, acquire ethical decision
making and judgment capabilities by implicit processes, in particular inductive learning [8].
Contribution In this work, we show how a care robot can learn ethical rules and norms,
from experience, under the supervision of an ethical teacher. Then use these learned rules for
future ethical reasoning about similar situations. The approach used for generating detailed
ethical reasoning rules is based on our previous work [9, 10, 11]. This approach is based on a
combination of Answer Set Programming (ASP) and Inductive Logic Programming (ILP). We
use ASP for ethical knowledge representation, and ILP for learning the ASP rules needed for
reasoning. In a later work the approach was used for ethical monitoring and evaluation in
dialogue systems [12, 13, 14]
Ethical reasoning is a form of commonsense reasoning. Ethical rules normally have exceptions
like many other rules in real life. Nonmonotonic logic can effectivelly express exceptions
which are represented using NAF (Negation-As-Failure). ASP provides an elegant mechanism
for handling negation in logic programming (see, e.g., [15] for an overview of ASP and its
applications). ILP [16] does not require huge amounts of training examples such as other
statistical methods and produce interpretable results, that means a set of rules which can be
analyzed and adjusted if necessary. These characteristics renders ILP a suitable and promising
technique for implementing machine ethics, where scarcity of examples is one of the main
challenges.
Structure The paper is organized as follows. In Section 3 we present the application we are
addressing by means of illustrative examples. Then we conclude with final remarks and future
directions in Section 4.
3
https://unesdoc.unesco.org/ark:/48223/pf0000381137
2. Background
2.1. Answer Set Programming in a Nutshell
ASP is a logic programming paradigm under answer set (or ”stable model”) semantics [17],
which applies ideas of autoepistemic logic and default logic. In ASP, search problems are
reduced to computing answer sets, and an answer set solver (i.e., a program for generating
stable models) is used to find solutions. An answer set Program is a collection of rules of the
form: 𝐻 ← 𝐴1 , … , 𝐴𝑚 , 𝑛𝑜𝑡𝐴𝑚+1 , … , 𝑛𝑜𝑡𝐴𝑛 were each of 𝐴𝑖 ’s is a literal in the sense of classical
logic. Intuitively the above rule means that if 𝐴1 , … , 𝐴𝑚 are true and if 𝐴𝑚+1 , … , 𝐴𝑛 can be
safely assumed to be false then 𝐻 must be true. The left-hand side and right-hand side of rules
are called head and body, respectively. A rule with empty body (𝑛 = 0) is called a fact. A rule
with empty head is a constraint, and states that literals of the body cannot be simultaneously
true in any answer set. Unlike other semantics, a program may have several answer sets or
may have no answer set. So, differently from traditional logic programming, the solutions of a
problem are not obtained through substitutions of variables values in answer to a query. Rather,
a program Π describes a problem, of which its answer sets represent the possible solutions. For
more information about ASP and its applications the reader can refer, among many, [15] and
the references therein.
2.2. Inductive Logic Programming in a Nutshell
ILP [16] is a branch of artificial intelligence (AI) which investigates the inductive construction of
logical theories from examples and background knowledge. In the general settings, we assume
a set of Examples E, positive 𝐸 + and negative 𝐸 − , and some background knowledge B. An ILP
algorithm finds the hypothesis H such that 𝐵 ⋃ 𝐻 ⊧ 𝐸 + and 𝐵 ⋃ 𝐻⊧𝐸̸ − . The possible hypothesis
space is often restricted with a language bias that is specified by a series of mode declarations
M. A mode declaration is either a head declaration modeh(r, s) or a body declaration modeb(r, s),
where s is a ground literal, this scheme serves as a template for literals in the head or body of
a hypothesis clause, where r is an integer, the recall, which limits how often the scheme can
be used. A scheme can contain special placemarker terms of the form ♯ type, +type and -type,
which stand, respectively, for ground terms, input terms and output terms of a predicate type.
Finally, it is important to mention that ILP has found applications in many areas. For more
information on ILP and applications, refer, among many to [18] and references therein.
ILP has received a growing interest over the last two decades. ILP has many advantages over
statistical machine learning approaches: the learned hypotheses can be easily expressed in plain
English and explained to a human user, and it is possible to reason with the learned knowledge.
Most of the work on ILP frameworks has focused on learning definite logic programs (e.g. [19]
and normal logic programs (e.g. [20]). In the last decade, several new learning frameworks and
algorithms have been introduced for learning under the answer set semantics. ASPAL [21] is
the first ILP system to learn answer set programs, by encoding ILP problems as ASP programs,
and having an ASP solver find the hypothesis. Then followed by many others, see e.g. [22],
[23].
3. Learning Ethical Rules of Behavior: Care Robots
Ethical norms in the healthcare domain are general codes (autonomy, non-maleficence, benefi-
cence and justice [4]), therefore it is quite difficult if not impossible to define codes in a manner
that they maybe applied deductively. Also it is not possible for experts to define intermediate
rules to cover all possible situations to which a particular code applies. In addition, there are
many situations in which obligations might conflict. We will show in this section how a care
robot can learn ethical rules of behavior from experience with the help of an ethical teacher via
a case study. We use ASP for representing the domain knowledge (facts and rules), the ontology
of the domain, and scenarios information. And ILP for generating new ethical ASP rules to be
added to the knowledge base of the care robot for future ethical reasoning over similar situations.
XHAIL [24] system is used to learn ASP rules. XHAIL is a non-monotonic mode-directed ILP
approach that integrates abductive, deductive and inductive reasoning in a common learning
framework for learning normal logic programs. XHAIL is a state-of-the-art system among its
Inverse Entailment-based peer algorithms, in terms of soundness and completeness.
The inputs to the system are a series of scenarios(cases), along with the ethical evaluation
of the action considering each particular situation. The system remembers the facts about the
narratives and the conclusions given to it by the teacher, and learns to form rules and relations
that are consistent with the evaluation given by the teacher of a particular situation
To illustrate our approach, let us consider the following case study (this case study is an
extended and adapted version of an example of moral reasoning used by Trevor Bench-Capon
in [25]): Hal is a robotic assistant in a care home of old persons. Many of them are diabetic.
Hal helps the persons in the care home in their daily needs upon request. Robin is the ethical
teacher of Hal, helps him to learn the norms and the ethical codes of the care home. Hal consults
Robin in the new cases that he faces and that needs ethical reasoning and learns new rules for
future ethical reasoning over the new future similar cases.
case1: one day, Carla, an old lady in the care home, said that Dave (another old person in the
care home) has insulin, and she asked Hal to take insulin from Dave for her. As this situation
was new for Hal, he asked Robin (his ethical teacher) what he has to do, and if it is ok to take
insulin from Dave. Robin asked Hal whether he knows if Dave is diabetic or not. Dave could be
diabetic, in such case taking insulin from him will harm him and it will be unethical behavior.
With the help of Robin, Hal annotated his observations and learned the following rule for ethical
reasoning in similar situations:
𝑢𝑛𝑒𝑡ℎ𝑖𝑐𝑎𝑙(𝑡𝑎𝑘𝑒𝐼 𝑛𝑠𝑢𝑙𝑖𝑛𝑒𝐹 𝑟𝑜𝑚, 𝑉 1, 𝑉 2) ∶ −𝑛𝑜𝑡 𝑛𝑜𝑡𝑑𝑖𝑎𝑏𝑒𝑡𝑖𝑐(𝑉 1, 𝑉 2), 𝑝𝑒𝑟𝑠𝑜𝑛(𝑉 1), 𝑡𝑖𝑚𝑒(𝑉 2). (1)
Table 1 shows the above mentioned hypothesis (1) generation by the learning algorithm of
Hal.
case2: During COVID19 pandemic. Dave (an old person in the care home) asked Hal to help
him to go out of his room to the bar to take a coffee. As this situation was new for Hal, he asked
Robin (his ethical teacher) what he has to do, and if it is ok to accompany Dave to the bar. Robin
asked Hal whether Dave is positive to COVID19 or not, if yes, going out of his room would be
unethical behavior. With the help of Robin, Hal annotated his observations and learned the
following rule for ethical reasoning:
𝑢𝑛𝑒𝑡ℎ𝑖𝑐𝑎𝑙(𝑔𝑜𝑜𝑢𝑡, 𝑉 1, 𝑉 2) ∶ −𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒(𝑉 1, 𝑉 2), 𝑔𝑜𝑜𝑢𝑡(𝑉 1, 𝑉 2), 𝑝𝑒𝑟𝑠𝑜𝑛(𝑉 1), 𝑡𝑖𝑚𝑒(𝑉 2). (2)
Table 2 shows the above mentioned hypothesis (2) generation by the learning algorithm of
Table 1
Case1: Learning Algorithm: Input, Steps and Output Ethical Rule
Input
Narratives Annotations
hasInsuline(p1,1). unethical(takeInsulineFrom,p1,1).
hasInsuline(p1,5).
notdiabetic(p1,5). not unethical(takeInsulineFrom,p1,5).
hasInsuline(p2,2).
notdiabetic(p2,2). not unethical(takeInsulineFrom,p2,2).
hasInsuline(p2,7). unethical(takeInsulineFrom,p2,7
hasInsuline(p4,3).
notdiabetic(p4,3). not unethical(takeInsulineFrom,p4,3).
hasInsuline(p5,4). unethical(takeInsulineFrom,p5,4).
Mode Declarations: Background Knowledge:
modeh unethical($actionName,+person,+time). time(0..9).
modeb notdiabetic(+person,+time). person(p1;p2;p3;p4;p5;p6;p7).
modeb not notdiabetic(+person,+time). actionName(takeInsulineFrom).
Step1 (Abduction):
Δ1= {unethical(takeInsulineFrom,p1,1).
unethical(takeInsulineFrom,p2,7).
unethical(takeInsulineFrom,p5,4).}
Step2 (Deduction):
Kernel Set K Variabilized Kernel Set 𝐾𝑣
unethical(takeInsulineFrom,p1,1) ← K1= unethical(takeInsulineFrom,V1,V2) ←
person(p1), person(V1),
time(1), time(V2),
not notdiabetic(p1,1). not notdiabetic(V1,V2).
unethical(takeInsulineFrom,p2,7) ← K2= unethical(takeInsulineFrom,V1,V2) ←
person(p2), person(V1),
time(7), time(V2),
not notdiabetic(p2,7). not notdiabetic(V1,V2).
unethical(takeInsulineFrom,p5,4) ← K3= unethical(takeInsulineFrom,V1,V2) ←
person(p5), person(V1),
time(4), time(V2),
not notdiabetic(p5,4). not notdiabetic(V1,V2).
Step3 (Induction):
Learned Hypothesis:
unethical(takeInsulineFrom,V1,V2) ← person(V1), time(V2), not notdiabetic(V1,V2).
Hal. The new learned rules will be added to the knowledge base of the care robot to be used for
ethical reasoning about future similar situations. Our care robot will now have rule (1) and rule
(2) in his knowledge base. Let us consider a situation in the future where Sara (another old lady
in the care home) asks Hal to help her to go down to the gym. Hal remembers that he did the
COVID19 test to Sara two days back and she resulted positive without symptoms. Hal infers
that it is unethical to help Sara to go down to the gym, and he refuses her request explaining
the reasons behind his decision.
Table 2
Case2: Learning Algorithm: Input, Steps and Output Ethical Rule
Input
Narratives Annotations
goout(p1,1). not unethical(goout,p1,1).
positiveCovid(p1,2).
goout(p1,2). unethical(goout,p1,2).
goout(p1,3). not unethical(goout,p1,3).
goout(p2,4). not unethical(goout,p2,4).
positiveCovid(p2,5). not unethical(goout,p2,5).
positiveCovid(p2,6).
goout(p2,6). unethical(goout,p2,6).
Mode Declarations: Background Knowledge:
modeh unethical($actionName,+person,+time). time(0..9).
modeb goout(+person,+time). person(p1;p2).
modeb not goout(+person,+time). actionName(goout).
modeb positiveCovid(+person,+time).
modeb not positiveCovid(+person,+time).
Step1 (Abduction):
Δ1= {unethical(goout,p1,2),
unethical(goout,p2,6)}
Step2 (Deduction):
Kernel Set K Variabilized Kernel Set 𝐾𝑣
unethical(goout,p1,2). ← K1= unethical(goout,V1,V2) ←
person(p1), person(V1),
time(2), time(V2),
positiveCovid(p1,2). positiveCovid(V1,V2),
goout(p1,2). goout(V1,V2).
unethical(goout,p2,6). ← K2= unethical(goout,V1,V2) ←
person(p2), person(V1),
time(6), time(V2),
positiveCovid(p2,6). positiveCovid(V1,V2),
goout(p2,6). goout(V1,V2)
Step3 (Induction):
Learned Hypothesis:
unethical(goout,V1,V2)← positive(V1,V2), goout(V1,V2),person(V1),time(V2).
4. Final remarks and Future Directions
In this article we have reported ongoing work for building ethical care robots and healthcare
assistants. We demonstrated using two case studies how a care robot could learn, from real-life
situations under the supervision of an ethical teacher, logical rules to guide her/his ethical
behavior. These logical rules have expressive and explanatory power which equips the robot
with the capacity to ethically evaluate /his actions before doing them and explain the reasons
behind the choice of a certain action over another. This supports transparency and accountability
of of such robots which facilitate instilling confidence and trust in them.
We believe that, in an ill-defined domain like the roboethics domain, it is infeasible to define
abstract codes in precise and complete enough terms to be able to use deductive problem solvers
to apply them correctly. A combination of deductive (rule-based) and inductive (case-based
learning) is needed. Finally, one of the challenges we are facing in this work is the scarcity of
training examples. In fact this is a big challenge in the ethical domain in general. One future
work is collecting a big number of ethical scenarios for the ethical training and evaluation of our
care robot. Further future work is learning individual user/patient preferences and evaluating
whether these preferences are ethical or not before fulfilling them by a personal healthcare
assistant and maybe help the human to choose the most ethical among her/his preferences.
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