=Paper=
{{Paper
|id=Vol-3936/iStar24_keynote
|storemode=property
|title=Ontology-Based Requirements Engineering: The Case of Ethicality Requirements
|pdfUrl=https://ceur-ws.org/Vol-3936/iStar24_keynote.pdf
|volume=Vol-3936
|authors=Renata Guizzardi,Giancarlo Guizzardi
|dblpUrl=https://dblp.org/rec/conf/istar/GuizzardiG24
}}
==Ontology-Based Requirements Engineering: The Case of Ethicality Requirements==
https://ceur-ws.org/Vol-3936/iStar24_keynote.pdf
Ontology-Based Requirements Engineering: The Case of
Ethicality Requirements
Renata Guizzardi and Giancarlo Guizzardi
University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands
Abstract
In this paper, we summarize the content of our keynote speech at iStar’24, in which we discussed an
ontology-based requirements engineering method to elicit and analyze ethicality requirements for the
development of trustworthy AI systems.
Keywords
Ethical AI, Trustworthy AI, Requirements Engineering Method 1
Concerned by the growing impact of information systems in people’s lives, especially motivated
by the recent AI developments, ethicists and AI researchers have been recently studying the
interplay of ethics and AI systems [1,2]. Moreover, governments and private organizations have
been engaged in producing regulations and guidelines for the development of trustworthy
systems [3,4]. Although we agree that the theoretical debate, along with regulations and
guidelines are important, we believe that it is essential to embed ethics into the system
engineering practices. For being concerned with stakeholders’ needs and wants, Requirements
Engineering has a fundamental role in the development of ethical systems. If we provide the
means for requirements analysts to capture and analyze ethicality requirements, we will be
contributing for ethics to be a core concern since the start of the system development lifecycle.
Moreover, ethicality requirements may be monitored and assessed not only while the system is
under development, but also after it is deployed.
This extended abstract summarizes the content of our keynote speech at iStar 2024, where
we presented an ontology-based requirements engineering method [5, 6]. The proposed method,
known and Ontology-based Requirements Engineering (OBRE) started with an ontological
analysis of ethicality requirements as non-functional requirements. As a result, we created an
ethicality requirements ontology. Then we instantiated this ontology, identifying guidelines for
the elicitation of ethicality requirements. With the help of these guidelines, the requirements
analyst may use an existing Requirements Engineering approach of her choice (e.g.,
requirements table, i*, user stories) to specify and analyze ethicality requirements.
The definition of ethicality requirements is based on the ontological analysis of four principles
conceived as part of an ethical framework to guide the development and adoption of AI systems
[7]: Beneficence, Nonmaleficence, Autonomy and Explicability. As a result of our ontological
analysis, these principles have been understood as more concrete concepts that are easier to
grasp, thus supporting requirements elicitation and analysis. To make our analysis clear, let us
describe how we define these principles. To illustrate the types of requirements, we use a
driverless car example.
Beneficence and Nonmaleficence are analyzed together. Beneficence is roughly understood
as ‘do good’ while Nonmaleficence means ‘do no harm’ [7]. With the help of the Common
Ontology of Value and Risk [8], we used the concepts of “value” and “risk” to analyze these
iStar’24: The 17th International i* Workshop, October 28-31, 2024, Pittsburgh, US
r.guizzardi @utwente.nl (R. Guizzardi); g.guizzardi @utwente.nl (G. Guizzardi)
0000-0002-5804-5741 (R. Guizzardi); 0000-0002-3452-553X (G. Guizzardi)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
respective principles. Beneficence requirements are those that allow the system to create gain
events, i.e., events that positively impact the stakeholder’s intentions. On the other hand,
nonmaleficence requirements are those that lead the system to prevent loss events, i.e., events
that negatively impact the stakeholder’s intentions. For instance, for a driverless car, “the car
shall choose the quicker route towards destination” and “the car shall stop before a crosswalk
every time there is a pedestrian waiting to cross it” are examples of beneficence requirements,
while “the car shall make enough distance while overtaking a car” and “the car shall adopt a
defensive driving behavior” are examples of nonmaleficence requirements.
Autonomy means striking a balance between the decision-making power retained by the
stakeholder and that which is delegated to the system [7]. To understand this kind of
requirement, we need to focus on the concept of delegation. The stakeholder delegates decisions
to the system and as part of this delegation, social positions are created to regulate the content
of such relationship [9]. For example, autonomy requirements may define duties, permissions
and powers from the system towards the stakeholders. For a driverless car, “the car has the duty
to follow traffic laws” and “the car does not have permission to change destination without the
passenger’s explicit request” are autonomy requirements examples.
Explicability is understood as making the decision-making process transparent, intelligible
and accountable [7]. Explicability requirements aim at keeping track of the system’s decision-
making process. According to the Decision-Making Ontology [10], for each decision, the system
conducts valuations of different options, and such valuations are based on different criteria. For
each decision, an explicability requirement aims at making explicit the available options, which
option was chosen, and which criteria were applied in this choice. Requirements such as “the car
shall explain why it decides (not) to overtake other vehicles” and “the car shall explain the
reasons why a particular route is chosen” are examples of explicability requirements.
Focusing on ethics since the Requirements Engineering activity is paramount to guarantee
the development of trustworthy systems. Our work is a first attempt in this direction. We hope
that in the future, we are able to evaluate it by its application on real cases, and improve it based
on this practical application. We also intend to complete the ontological analysis of the ethical
dimensions proposed in [7] by tackling the notion of Justice.
References
[1] L. Floridi. The Ethics of Artificial Intelligence: Principles, challenges, and opportunities.
Oxford University Press (2023).
[2] Handbook of Research on Technoethics. IGIGlobal (2009).
[3] EU Artificial Intelligence Act, accessed 27-01-2025 at https://artificialintelligenceact.eu/
[4] 7000-2021 - IEEE Standard Model Process for Addressing Ethical Concerns during System
Design, accessed 27-01-2025 at https://ieeexplore.ieee.org/document/9536679
[5] R. Guizzardi, G. Amaral, G. Guizzardi and J. Mylopoulos. An ontology-based approach to
engineer ethicality requirements. Softw. Syst. Model. 22 (2023): 1897-1923.
[6] R. Guizzardi, G. Amaral, G. Guizzardi and J. Mylopoulos. Using i* to Analyze Ethicality
Requirements. In X. Franch, J.C. Leite, G. Mussbacher, J. Mylopoulos and A. Perini (Eds.) Social
Modeling Using the i* Framework: Essays in Honour of Eric Yu. Springer, (2024): 183-204.
[7] L. Floridi, et al. Ai4people – An ethical framework for a good AI society: Opportunities, risks,
principles and recommendations. Minds & Machines 28 (2018): 689-707.
[8] T. Sales et al. The Common Ontology of Value and Risk. In: Proc. of 37th International
Conference on Conceptual Modeling (ER). Springer, LNCS v. 11157 (2019): 121–135.
[9] Griffo, C., Almeida, J.P.A., Guizzardi, G., Nardi, J.C., R. Guizzardi, B. Carneiro, D. Porello and G.
Guizzardi. A core ontology on decision making. Information Systems, V. 101, Nove. 2021.
[10] R. Guizzardi, B. Carneiro, D. Porello and G. Guizzardi. A core ontology on decision making.
In: Proc. of the 13th Seminar on Ontology Research in Brazil, CEUR v. 2728 (2020): 9-21.