=Paper=
{{Paper
|id=Vol-3741/paper31
|storemode=property
|title=POLARIS: A framework to guide the development of Trustworthy AI systems
|pdfUrl=https://ceur-ws.org/Vol-3741/paper31.pdf
|volume=Vol-3741
|authors=Maria Teresa Baldassarre,Danilo Caivano,Domenico Gigante,Azzurra Ragone
|dblpUrl=https://dblp.org/rec/conf/sebd/BaldassarreCGR24
}}
==POLARIS: A framework to guide the development of Trustworthy AI systems==
https://ceur-ws.org/Vol-3741/paper31.pdf
POLARIS: A framework to guide the development of
Trustworthy AI systems
Maria Teresa Baldassarre1 , Danilo Caivano1 , Domenico Gigante1,2,* and
Azzurra Ragone1,*
1
University of Bari "A. Moro", Bari, Italy
2
Ser&Practices Srl, Bari, Italy
Abstract
In the ever-expanding landscape of Artificial Intelligence (AI), where innovation thrives and
new products and services are continuously being delivered, ensuring that AI systems are
designed and developed responsibly throughout their entire lifecycle is crucial. To this end,
several AI ethics principles and guidelines have been issued to which AI systems should conform.
Nevertheless, relying solely on high-level AI ethics principles is far from sufficient to ensure the
responsible engineering of AI systems. In this field, AI professionals often navigate by sight.
Indeed, while recommendations promoting Trustworthy AI (TAI) exist, they are often high-level
statements difficult to translate into concrete implementation strategies. Currently, there is a
significant gap between high-level AI ethics principles and low-level concrete practices for AI
professionals. To address this challenge, in this discussion paper we describe the novel holistic
framework for Trustworthy AI we developed — designed to bridge the gap between theory
and practice. The framework builds up from the results of a systematic review of the state
of the practice as well as a survey and think-aloud interviews with 34 AI practitioners. The
framework, unlike most of the ones in the literature, is designed to provide actionable guidelines
and tools to support different types of stakeholders throughout the entire Software Development
Life Cycle (SDLC). Our goal is to empower AI professionals to confidently navigate the ethical
dimensions of TAI through practical insights, ensuring that the vast potential of AI is exploited
responsibly for the benefit of society as a whole.
Keywords
Artificial Intelligence, Software Engineering, Trustworthy AI, Knowledge Base, Framework
1. Introduction
In the dynamic realm of Artificial Intelligence (AI), marked by ceaseless innovation and
rapid advancements, the ethical, societal, and operational implications of AI technologies
have shifted to the forefront of discussions. As AI systems become deeply integrated into
our daily lives, from healthcare [1] to finance [2], and influence critical decision-making
SEBD 2024: 32nd Symposium on Advanced Database Systems, June 23-26, 2024, Villasimius, Sardinia,
Italy
*
Corresponding author.
$ mariateresa.baldassarre@uniba.it (M. T. Baldassarre); danilo.caivano@uniba.it (D. Caivano);
d.gigante@serandp.com (D. Gigante); azzurra.ragone@uniba.it (A. Ragone)
0000-0001-8589-2850 (M. T. Baldassarre); 0000-0001-5719-7447 (D. Caivano); 0000-0003-3589-6970
(D. Gigante); 0000-0002-3537-7663 (A. Ragone)
© 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
processes, the responsible development and deployment of AI has transitioned from an
academic discourse to an imperative in real-world applications.
In this complicated context, the concept of Trustworthy Artificial Intelligence (TAI)
[3] has grown relevance. These risks are even more pronounced with the recent advent of
Generative AI — e.g. ChatGPT — and how this impacts on various societal aspects [4].
Several public and private organizations have tried to address TAI by developing
different kinds of resources, just to cite a few: ethical requirements [5], principles [6],
guidelines [7], best practices [3], tools [8], and frameworks [9]. However, navigating the
intricacies of TAI has become increasingly complex due to what we might refer to as
"principle proliferation" [10]. This phenomenon encompasses the multitude of ethical
principles that have been devised, each one providing a specific definition, but also
contributing to a landscape that can overwhelm AI practitioners.
In response to the challenges posed by principle proliferation, our research follows the
work of Jobin et al. [10] and focuses on four foundational pillars of TAI: Privacy, Security,
Fairness, and Explainability. These pillars have been condensed in a practical, focused,
and adaptable framework, called POLARIS [11]. Its aim is to guide AI practitioners and
stakeholders in their quest to ensure the effective trustworthy development of AI-enabled
systems across the entire software Development LifeCycle (SDLC).
The main contributions of this work — detailed in [11] — can be summarized as
follows:
• A review of the state of the practice and identification of practitioner needs to
understand existing practices, challenges, and what practitioners currently lack in
developing Trustworthy AI applications.
• The proposal of a novel framework (POLARIS) that systematizes and organizes the
knowledge found in different sources. The objective of POLARIS is to make this
knowledge easily accessible to AI practitioners and to provide them with actionable
guidelines that can be applied in every phase of the SDLC.
The paper is organized as follows: Section 2 outlines the results of the systematic
review and the findings from the survey and interviews with AI professionals. Section 3
describes the POLARIS framework, its components and how to use it. Conclusions are
drawn in Section 4.
2. State of the practice
This discussion paper builds on previous research [12], in which we conducted a compre-
hensive study of the state of the practice of existing TAI frameworks. More precisely, we
investigated (i) the extent to which the analyzed frameworks addressed the principles
mentioned in Section 1 and (ii) whether and to what extent these frameworks covered
the stages of the Software Development Life Cycle (SDLC). Next, we carried out a
comparative analysis among the identified frameworks with respect to characteristics
such as best practices, guidelines, and tools, in order to assess if and how big a gap there
is between the proposed high-level AI ethics principles and low-level operational practices
for practitioners.
In our previous work [12] we analyzed 138 frameworks, both from white-literature and
grey-literature sources. The main findings are:
1) Most of the frameworks are proposed by No-profit Organisations, Public Entities
or Human Communities (50.7%); followed by private Companies (31.9%) and then
Universities (17.4%).
2) Most of the frameworks provide a set of TAI principles/values (46.1%); others
include actionable Guidelines (29.6%), but very few also provide Tools (9.2%).
3) In the majority of cases, the frameworks address all four TAI principles (45.1%)
even if there are frameworks that cover only one (15.5%) or two (15.5%) principles;
4) More than half of the frameworks (55.2%), provide support only for the Requirements
Elicitation phase. While all the SDLC phases are covered only in 5.7% of the frameworks;
5) In more than 80% of the cases there is no tool included in the framework and when it
is present, it is directed to Non-technical stakeholders (i.e. stakeholders who work in the
first two phases of the SDLC — e.g. commercial agents, functional analysts, architecture
designers, etc).
In summary, our analysis confirmed that most of the existing frameworks include
high-level best practices, checklists, or self-assessment questions, most suitable for non-
technical stakeholders, limitedly able to address technical stakeholder needs and close
the gap between high-level principles definition and practical recommendations for AI
practitioners covering all the SDLC phases. The findings from [12] have then guided our
subsequent research aimed at investigating the needs of AI practitioners, their current
practices and issues encountered in the design and implementation of trustworthy AI
systems.
2.1. Identification of Practitioner needs
As a premise for the design and proposal of our framework, we conducted an exploratory
survey [13] to collect practitioner insights and needs with respect to TAI principles. We
used convenience sampling and recruited practitioners from companies in our network
of collaborations. All participants were practitioners with experience in developing AI-
enabled systems who had addressed, to some extent, TAI in their projects. We contacted a
total of 45 professionals, of which 34 completed the survey. These participants represented
a diverse spectrum, ranging from small-medium companies (55.9%), to large companies
with more than 1000 employees (44.1%).
Apart from the demographics, the survey is organized into three main parts, each
focusing on the collection of specific data pertaining: a) existing practices, b) identification
of challenges, c) discovery of unmet needs. In the following, for each part, we provide a
brief explanation and highlight the main results.
A) Exploration of Existing Practices. The first part of the survey investigated the
operational procedures and methodologies employed by practitioners in the context of
implementing TAI. This exploration aimed to provide a detailed insight into the real-world
practices and strategies adopted.
Results. First, we observed that the TAI principle most frequently addressed by partici-
pants is Privacy (58.8%), and most of the participants address at least one TAI principle
during Design (64.7%) and Development (47.1%) SDLC phases. On the contrary, very
few participants declared to address at least one TAI principle during the Test (29.4%)
and Deploy (20.6%) phases. This may highlight the need for more support, in terms of
tools and guidelines in the last phases of the SDLC. Moreover, when participants faced
issues related to TAI, in half of the cases they did not even try to address or solve them
(50%). Probably because they did not know how to or they simply considered them
not worth solving. This is a point that deserves more investigation. Only 35% of the
participants declared to have directly addressed TAI issues, while a small percentage
stated that the issue resolution was demanded to a third party (15%).
B) Identification of Challenges. This section of the survey explored the challenges and
obstacles encountered by professionals while trying to integrate TAI into their systems.
By identifying these issues, we aimed to shed light on critical areas where AI professionals
may need more support.
Results. In cases where the respondents tried to address/fix TAI issues, we found that
the most voted impediments are: (i) "the issue solution required too much time to be
implemented" (58.3%) and (ii) "the issue solution was likely to decrease the performance of
the system (e.g., decreasing accuracy)" (50%). On the other hand, none of the participants
answered: "no one had idea on how to solve the issue", which is a positive result since it
indicates that practitioners are conscious of untrustworthiness problems and are able to
hypothesize solutions. Among the comments, one participant mentioned "[scarce] data
availability" as an impediment.
C) Discovery of Unmet Needs. The third part of the survey reveals the presence of
unaddressed needs within the practical landscape of TAI. Specifically, we uncovered a
range of requirements that have so far received limited attention within the existing
literature.
Results. Regarding the prevention of trustworthiness issues in AI, the participants rated
as the most valuable tool able to "[...] generate an explanation of a model after its
creation [...]" (with 82% of positive answers) while they rated as least useful (i) a tool to
help "deciding how much data you need for particular subgroups/subpopulations" and (ii)
a tool to "generate possible adversarial/malicious data points to test to use in testing the
system" (both with 19% of negative answers).
On the other side, to address untrustworthiness in AI, the participants rated as the most
valuable (i) "best practices that can actively guide your team through the model’s SDLC "
(92% positive answers), (ii) a tool able to "[...] help [...] monitoring the AI model after its
release to the public" (91% positive answers), and "a knowledge book in which are mapped
trustworthiness problems and [...] solutions" (70% positive answers). On the other hand,
they rated as least useful (i) a tool "[...] to help your team doing an ex-post TAI audit"
(18% of negative answers) and (ii) a tool able to [...] help your team deciding which AI
model best respects the TAI principles [...] (17% of negative answers).
Overall, these results confirmed the findings of our previous work [12]: a significant
majority of the respondents expressed the need for comprehensive knowledge bases and
pragmatic guidelines offering insights and recommendations for the seamless implemen-
tation of trustworthy AI system throughout the entire SDLC. Furthermore, they also
highlighted the lack of tools supporting them in the last stages of SDLC.
3. The POLARIS Framework
In response to the challenges and issues highlighted by the research results, with the
intent to fill the gap between theory and practice and to address stakeholder needs and
shortcomings (Section 2), we have developed a framework: POLARIS [11]POLARIS.
Indeed, POLARIS has been designed to provide actionable guidelines and tools in
order to support stakeholders in addressing TAI principles throughout the entire Software
Development Life Cycle (SDLC). POLARIS provides a significant amount of informa-
tion, organized and linked into a comprehensive knowledge base that is designed to be
expandable, with the possibility to easily add new knowledge.
In Section 3.1 we explain how we built the POLARIS knowledge base while in Section
3.2 we describe how to navigate it.
3.1. Defining POLARIS Knowledge Base
In this section, we describe how we assembled the POLARIS Knowledge Base and
the selection process used to choose the different knowledge sources representing the
foundation of POLARIS.
We started from the frameworks analyzed in [12], we complemented our analysis with
the results obtained from the survey, and then we identified among the existing knowledge
sources (i.e. frameworks) those that met both of the following criteria:
1. Have actionable guidelines (and not only a simple high-level principles list)
2. Address all SDLC phases.
Regarding the last criterion, since SDLC phases do not always map with the activities
required to develop an AI-enabled system, we have integrated each SDLC phase with
AI-enabled activities established by Zhengxin et al. [14].
After this first selection phase, we identified only three knowledge sources that meet
both criteria (1) and (2). Then, we mapped each identified knowledge source to the
corresponding TAI principle. For Explainability we selected Jin et al. - EUCA: the
Explainable AI Framework [15], for Fairness we chose Amsterdam Intelligence - The
Fairness Handbook [16]. For both Privacy and Security we selected ENISA - Securing
Machine Learning Algorithms [17].
Then, we refined this first selection by adding more knowledge sources that could
complement the information provided by the primary ones initially selected. We started
by selecting the frameworks that met at least one of the following criteria:
1. Have actionable guidelines (and not only a simple high-level principles list)
2. Address all SDLC phases.
We retrieved 10 additional knowledge sources that met at least one of the previous
criteria. The table with all the 10 knowledge sources identified can be found in the
online appendix [18]. Then, we performed a comparative analysis between each primary
knowledge source already selected ([15], [16], [17]) and the new ones retrieved in this
second iteration.
The results of the comparative analysis brought us to select four additional knowledge
sources that could complement and expand the information provided by the first ones
selected.
The additional frameworks selected were ICO’s "Guidance on AI and data protection"
[19], Tensorflow’s "Responsible AI in your ML workflow" [20], the guidelines in Microsoft’s
"Threat Modeling AI/ML Systems and Dependencies" [21] and CSIRO’s "Responsible
AI Pattern Catalogue" [22]. Therefore, we used these additional knowledge sources to
further extend the information provided by the primary ones. We ended up selecting 7
knowledge sources. In the online appendix [18] there is the mapping between each TAI
principle and the corresponding knowledge sources covering that principle.
3.2. Navigating POLARIS Knowledge Base
Having defined the POLARIS knowledge base, in this section we focus on how to navigate
it. The goal of the proposed framework is to support stakeholders throughout the SDLC
by suggesting concrete implementation strategies able to support and guide them in the
development of TAI applications.
When applying the framework, the users will ultimately receive an Action to implement,
that is, an actionable guideline that a stakeholder should consider and, if possible,
implement while developing the AI-enabled software system to ensure compliance with
the four TAI principles. The user can also choose to filter and apply only a subset of the
suggested guidelines.
As of now, the first version of POLARIS has been structured as a filterable Excel
array of sheets. There are four main knowledge components, one per each principle: (i)
Privacy; (ii) Security; (iii) Fairness and (iv) Explainability.
In proposing the structure of each Excel sheet, we were inspired by the ENISA
framework [17] and then customized it according to our needs.
The two sheets that contain the knowledge for "Privacy" and "Security" are composed
of the following six columns (Fig. 1 shows an excerpt of the security component).
Figure 1: Excerpt of the Security component navigation of the POLARIS framework.
(1) SDLC Phase. The SDLC phase that the Action column applies to.
(2) Threat. Contains the list of threats, i.e. possible attacks that can be conducted
against an AI-enabled system. Examples are Evasion and Poisoning attacks.
(3) Sub-Threat. In some specific cases, a threat can have a specific declination in a
sub-characteristic. For example, the Poisoning attack can be declined in Targeted Data
Poisoning and Indiscriminate Data Poisoning.
(4) Description. A textual description of the (Sub)Threat, which helps the stakeholder
obtain coarse-grained details about the threat and understand the attacker’s objective.
(5) Vulnerability (consequence). This is the immediate consequence of having a model
vulnerable to a specific threat.
(6) Action. The corresponding action, or decision, that should be adopted to address
a specific threat, based on the SDLC phase and threat selected, keeping in mind the
vulnerability.
For example, a developer in the Design SDLC phase who is trying to address the
vulnerabilities associated with Poisoning threat, may consult POLARIS and access the
Security Excel sheet, select the Poisoning threat — and corresponding sub-threat, i.e.
Label modification —, and obtain a description of the vulnerability associated to the
(sub)threat and the action to take in order to mitigate the vulnerability, i.e. ensure that
reliable sources are used (Fig. 1).
The sheet that contains the knowledge for "Fairness" is composed of 5 columns, all of
the above, except for Vulnerability (consequence) column which has been removed as the
concept of vulnerability in the context of fairness does not apply.
The sheet containing the knowledge for "Explainability" has a different set of columns
(see Fig. 2), because there are no real threats associated with the lack of explainability.
However, having a system that is not explainable, will lead users to use it with some
reluctance because of its opacity in making decisions, as it is not possible to derive any
clear logical relationship between the internal configuration and their external behaviour,
except for a few specific cases (e.g. decision trees) [23]. For Explainability, the columns
are the following:
Figure 2: Excerpt of the Explainability component navigation of the POLARIS framework
(1) SDLC Phase. The SDLC phase the Action column relates to.
(2) Data Type. The type of data used by the AI algorithm for which the action/guideline
applies. Examples are Tabular data or Image. When the action applies to all algorithms,
regardless of the type of data, the tag General is used.
(3) Local/Global Explanation. This column describes the type of explanation that can
be obtained by implementing the action. At the moment, the possible values are Global
and Local [24].
(4) Explanation Goal. This is the goal that can be achieved if the action/guideline
gets implemented. Examples are: to validate the algorithm outcome and to reveal bias.
(5) Action. The corresponding action, or decision, that should be taken to reach the
selected explanation goal. We point out that for each pair
there is at least a corresponding row in the framework.
For example, a user who is in the Requirement Elicitation SDLC phase and needs to
enquire on all the possible explanation approaches to explain the output of an algorithm,
could access the Explainability Excel sheet and select the General data type and retrieve a
set of actions that pertain explainability requirements i.e. elicit explainability requirements
(Fig. 2).
When navigating POLARIS, each stakeholder can use different filters and subfilters,
based on specific needs, as for instance: Knowledge Component (i.e. TAI principle) to
address, Threat (or Sub-Threat), Vulnerability, SDLC Phase, Data type, and Local/Global
Explanation. One of the most significant filters is SDLC phase, which makes POLARIS
flexible and allows stakeholders to use it either on ongoing/closed projects — where it is
possible to address, for example, only the deployment or monitoring phase — or at the
early stage of a project, since in the latter case it can cover all SDLC phases.
4. Conclusion
In this work, we described POLARIS, the framework we designed to fill the gaps
highlighted in the review of the state of the practice and to provide AI practitioners with
actionable guidelines specific to each phase of the SDLC.
POLARIS has four pillars (or components), which are Explainability, Fairness, Security,
and Privacy. These principles have been chosen as they are the most recurrent TAI
principles found in the current literature [10]. Each component provides practical
guidelines and tools to support different kinds of stakeholders across the entire SDLC.
Its added value is that it provides knowledge already freely accessible online but in an
organized and systematized way.
As detailed in [11], we identified several improvements. From a usability point of view,
we are planning to (i) provide a more usable UI, like the one of the VIS-Prise tool [25].
Then, we plan to (ii) validate POLARIS on a growing number of case studies.
Moreover, if further validations confirm us the stakeholders are interested and plan to
use POLARIS, in the next versions we plan to integrate more TAI principles.
POLARIS is a preliminary attempt to organize and make knowledge on TAI principles
easily accessible and available to different kinds of stakeholders. It is a pioneering
prototype whose goal is to make AI professionals, policymakers, and stakeholders able to
navigate the ethical dimensions of TAI with confidence, ensuring that the vast potential
of AI is harnessed responsibly for the benefit of society.
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