=Paper=
{{Paper
|id=Vol-3762/469
|storemode=property
|title=A Risk-based Approach to Trustworthy AI Systems for Judicial Procedures
|pdfUrl=https://ceur-ws.org/Vol-3762/469.pdf
|volume=Vol-3762
|authors=Majid Mollaeefar,Eleonora Marchesini,Roberto Carbone,Silvio Ranise
|dblpUrl=https://dblp.org/rec/conf/ital-ia/MollaeefarMCR24
}}
==A Risk-based Approach to Trustworthy AI Systems for Judicial Procedures==
https://ceur-ws.org/Vol-3762/469.pdf
A Risk-based Approach to Trustworthy AI Systems for
Judicial Procedures
Majid Mollaeefar1,* , Eleonora Marchesini1 , Roberto Carbone1 and Silvio Ranise1,2
1
Fondazione Bruno Kessler, Center for Cybersecurity, Trento, Italy
2
Department of Mathematics, University of Trento, Italy
Abstract
In the rapidly evolving landscape of Artificial Intelligence (AI), ensuring the trustworthiness of AI tools deployed in sensitive
use cases, such as judicial or healthcare processes, is paramount. The management of AI risks in judicial systems necessitates
a holistic approach that includes various elements, such as technical, ethical considerations, and legal responsibilities. This
approach should not only involve the application of risk management frameworks and regulations but also focus on the
education and training of legal professionals. For this, we propose a risk-based approach designed to evaluate and mitigate
potential risks associated with AI applications in judicial settings. Our approach is a semi-automated process that integrates
both user (i.e., judge) feedback and technical insights to assess the AI tool’s alignment with Trustworthy AI principles.
Keywords
Judicial AI, Risk-aware, Trustworthy AI, Trustworthiness Risk Assessment.
1. Introduction countability and explainability of AI systems. As these
In recent years, the adoption of Artificial Intelligence (AI) systems become integral to decision-making processes, it
technologies has surged across various industries and is essential to comprehend how they reach their conclu-
domains. AI systems now play a pivotal role in making sions or recommendations. TAI increases transparency
critical decisions, automating tasks, and augmenting hu- and offers mechanisms for interpreting the rationale be-
man capabilities. However, with the expanding influence hind AI-generated decisions, allowing users and stake-
and complexity of AI, it is crucial to ensure the develop- holders to hold systems accountable. Cobianchi et al. [2]
ment and deployment of Trustworthy AI (TAI) systems. emphasize the importance of accountability, technical ro-
TAI encompasses the creation and implementation of AI bustness, and transparency in AI applications in surgery,
technologies adhering to a set of principles that promote which can be extended to other domains. Third, TAI
transparency, fairness, accountability, and robustness. By aids in mitigating risks associated with AI technologies.
designing TAI systems, the aim is to inspire trust among If developed or deployed irresponsibly, AI systems can
users, stakeholders, and society as a whole where these introduce numerous risks, including privacy breaches,
systems must operate reliably, ethically, and in a man- biased decision-making, safety concerns, and the perpet-
ner that respects fundamental rights and values. The uation of social inequalities. Addressing these risks is
significance of TAI cannot be overstated, as it has the vital to protect individuals, organizations, and society
potential to address pressing concerns that arise from from potential harm and adverse consequences.
increasing reliance on AI systems. Some notable rea- The AI Act draft proposal for a Regulation1 of the Euro-
sons why it is critical for AI systems to be designed with pean Parliament and of the Council laying down harmo-
trustworthiness in mind including the following three; nized rules on AI represents the first attempt to enact a
First, TAI cultivates user confidence and trust by ensur- horizontal AI regulation. This proposed legal framework,
ing that personal data is handled responsibly, decisions focusing specifically on the use of AI systems, advocates
made by AI systems are fair and unbiased, and privacy for a technology-neutral definition of AI systems in EU
is protected. This is critical for building user confidence legislation. It emphasizes a risk-based approach where
and trust in AI systems. The authors in [1] discuss the AI systems are classified with varying obligations pro-
theoretical framework of AI trustworthiness, including portional to their level of risk. The AI Act categorizes
aspects of privacy preservation and fairness, which are risks into four levels: minimal, limited, high, and unac-
key to fostering user trust. Second, TAI bolsters the ac- ceptable (i.e., the latter are not permitted to be sold on
the EU market). It focuses on high-risk AI applications
Ital-IA 2024: 4th National Conference on Artificial Intelligence, orga- (HRAI) by setting specific requirements and obligations
nized by CINI, May 29-30, 2024, Naples, Italy for both users and providers of these applications. This
*
Corresponding author.
includes a conformity assessment before market place-
$ mmollaeefar@fbk.eu (M. Mollaeefar); emarchesini@fbk.eu
(E. Marchesini); carbone@fbk.eu (R. Carbone); ranise@fbk.eu
(S. Ranise) 1
https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0). CELEX:52021PC0206
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
ment or service commencement, enforcement measures developing techniques for better human understanding
post-market placement, and a governance structure at of ML-generated algorithms. The choice between tra-
both European and national levels. The aim is to ensure ditional and modern methods depends on the specific
that obligations are aligned with the associated risk level application’s needs, including considerations of security
of each AI system. and trustworthiness. An effective risk analysis is crucial
One of the areas where AI holds a sensible impact is in in determining the suitability of an AI-produced algo-
the legal context, where for instance judges can benefit rithm for a given scenario.
from the presence of automated decision-making in ju-
dicial proceedings [3, 4], potentially reducing the effort
2.2. Trustworthy AI
Trustworthiness is a prerequisite for people and soci-
required to search through documents, seek out relevant
eties to develop, deploy and use AI systems. Without
legal provisions, or support them in complex cases where
AI systems—and the human beings behind them—being
the human capacity to detect patterns is limited [5]. AI
demonstrably worthy of trust, unwanted consequences
tools like ChatGPT, while useful, present several limi-
may ensue, and their uptake might be hindered, pre-
tations in legal contexts. They may produce inaccurate
venting the realization of the potentially vast social and
information, as demonstrated in cases like Roberto Mata
economic benefits that they can bring [6]. In the past
vs Avianca2 , where reliance on ChatGPT led to legal
few decades, the success of ML has primarily been evalu-
issues due to the citation of non-existent cases. This
ated based on its quantitative accuracy, which has made
stresses the necessity for legal professionals, particularly
training AI models much more manageable. Predictive
judges, to be acutely aware of the risks associated with
accuracy has also become the standard measure for de-
their use of HRAI Systems. In this paper, we introduce a
termining the superiority of an AI product. However,
risk-based approach designed to evaluate and mitigate
with the widespread use of AI, the limitations of using
potential risks associated with the trustworthiness of AI
accuracy as the sole measurement have become apparent,
applications in judicial settings.
as new challenges have arisen, such as malicious attacks
2. Background and the misuse of AI. To address these challenges, the AI
community has recognized that factors beyond accuracy
Below we introduce background information to better need to be considered and improved when building an
perceive the approach. AI system. Recently, a number of enterprises, academia,
2.1. AI Algorithms public sectors, and organizations have identified princi-
In the realm of AI, the development of algorithms falls ples of AI trustworthiness that go beyond accuracy-based
into two primary views: traditional and modern. The measurements [7]. According to [8], the current degree
traditional approach involves human-created models for of trustworthiness of an AI system is dependent on how
specific problems or computations, where a limited set the user perceives its technical characteristics. Various
of features and a fixed sequence of instructions are em- organizations, including the G20, the EU Parliament, the
ployed. This method, exemplified by classical planning General Partnership on AI (GPAI), and the Organisation
in autonomous systems, relies on symbolic representa- for Economic Co-operation and Development3 (OECD)
tions and a predefined set of rules, necessitating heuris- have proposed different principles for ensuring trustwor-
tics to navigate the vast potential state spaces. Despite thiness in AI systems [9]. The OECD, for instance, has
its rigidity, this approach allows for the construction of put forward a set of five principles aimed at promoting
algorithms that are easily understood and verified by hu- TAI: (i) inclusive growth, sustainable development and
mans. Conversely, the modern perspective, dominated well-being, (ii) human-centered values and fairness, (iii)
by Machine Learning (ML), leverages large datasets to transparency and explainability, (iv) robustness, security
generate rules for problem-solving. Through processes and safety, and (v) accountability. The use of AI is in-
like training and deployment, algorithms are formulated tended to promote human good and well-being, and as
to classify or interpret data, such as classifying images such, it should not cause any harm. AI systems must be
of dogs and cats. The ML-based methods benefit from characterized by fairness, accuracy, and reliability, and
the ability to tackle complex problems without extensive should not be discriminatory. To be considered trustwor-
human ingenuity, employing powerful optimization tech- thy, AI systems must be transparent and explainable,
niques. However, it faces challenges such as potential meaning they should have the necessary capabilities,
imprecision, bias in training data, and the complexity of functions, and features to achieve user goals, with their
the resulting algorithms making them difficult for hu- algorithms being easily understood by users. Addition-
mans to comprehend. Strategies to mitigate these issues ally, AI systems must be resilient to threats that may
include performance monitoring, dataset filtering, and try to exploit their normal behaviors and turn them into
harmful ones. In the literature, additional principles have
2
https://law.justia.com/cases/federal/district-courts/new-york/
3
nysdce/1:2022cv01461/575368/54/ https://oecd.ai/
Unbiasedness
been proposed such as accuracy [10], acceptance [11], Non-discrimination Fairness
predictability and performance [12]. The AI HLEG [6], Diversity
has focused on the concept of TAI, offering guidance Compliance
in the form of a framework and identifying seven key Auditability Accountability
Ethical
ethical and technical requirements. Traceability
Transparency
3. Our View on Trustworthiness Trust Explainability
Interpretability
In our analysis of the literature on finding principles of
trustworthiness in AI, the commonly agreed-upon prin-
Confidentiality
Privacy
Anonymity
Technical
ciples are accuracy, robustness, privacy, explainability,
accountability, and fairness. While these six principles Security
are widely acknowledged in the literature, there are ad-
Safety Robustness
Resiliency
ditional considerations that can be incorporated within Integrity
them. For instance, the concept of “human in the loop” Reliability Accuracy
can be viewed as an aspect of fairness. We differentiate be- Data Validity
tween properties and principles. While both concepts are
related and work together to ensure the overall trustwor- Figure 1: TAI principles and properties relationship.
thiness of AI systems, they represent different aspects
of the trustworthiness framework. Properties refer to
specific characteristics or attributes of an AI system that rithms [13]. Different AI models exhibit variability in
contribute to ensure a principle. For instance, integrity, how they align with TAI principles. This variation stems
reliability, and data validity can be considered as prop- from the inherent differences in model structures, train-
erties relevant to the accuracy principle; Integrity refers ing methods, data used, and their intended applications.
to the quality of an AI system being honest, consistent, For example, a model designed for healthcare decision
and maintaining the integrity of the data and algorithms support may prioritize accuracy and privacy, while one
it operates on. It ensures that the AI system is resistant for autonomous vehicles might focus more on safety and
to unauthorized modifications or tampering. Reliability, robustness. The data used to train AI models significantly
focuses on the consistency and dependability of an AI affects their trustworthiness. A model trained on limited
system’s performance. A reliable AI system consistently or biased data may exhibit lower trustworthiness due to
produces accurate results over time and under different its potential to generate skewed or unfair results. Addi-
conditions. Data validity refers to the quality and correct- tionally, the type of algorithm—whether it is rule-based
ness of the data used by an AI system to generate outputs. or learning-based—plays a crucial role in determining
Valid data ensures that the information processed by the the model’s reliability, fairness, and transparency [13].
AI system is accurate, relevant, and representative of
the problem domain. On the other hand, principles rep- 3.2. Algorithm-based Trustworthiness
resent high-level guidelines or concepts that guide the The relationship between algorithms and TAI principles
development and deployment of TAI systems. The rela- is a critical aspect of responsible AI development and
tionship between properties and principles lies in how deployment. TAI principles serve as benchmarks against
which the performance and ethical considerations of al-
properties contribute to fulfilling the principles. Figure 1
depicts the relationship between properties and six essen- gorithms can be evaluated. Each algorithm has its own
tial principles for TAI, categorized into either technical,set of advantages and limitations that align or conflict
ethical, or both. Accuracy and robustness serve as tech- with these principles, making it essential to investigate
nical principles, whereas fairness and accountability fall their compatibility in specific use cases. Since each al-
within the ethical domain. Located in the center of the gorithm has a distinct set of characteristics, their com-
figure, privacy, and explainability are unique principles patibility with TAI principles can differ significantly; in
that encompass both the technical and ethical facets. other words, they have different compliance levels. To
define Algorithm-based Trustworthiness (ABT) levels, it
3.1. AI Algorithms & Trustworthiness is essential to consider both the inherent characteristics
Trustworthiness in AI is a multifaceted concept, often of each algorithm and the specific attributes related to
seen as a relationship between two entities—the AI sys- each AI principle. We define the following qualitative
tem and its user. The trustworthiness of an AI system is levels for this assessment; High: The algorithm inher-
largely dependent on how it is perceived by the user in ently aligns with the AI principle in question, requiring
terms of its technical characteristics. This perception is minimal or no additional measures to ensure compliance.
influenced by various factors, including the type of AI Moderate: While the algorithm generally aligns with
model, its application context, and the underlying algo- the principle, additional safeguards or contextual consid-
erations may be necessary. Low: The algorithm poses achieve the same level of accuracy in complex scenar-
challenges or risks that make it difficult to align with ios as their more sophisticated counterparts. On the
the AI principle, and significant adjustments or limita- other hand, SVMs and neural networks, especially in
tions would be required for compliance. To conduct a their advanced forms, are capable of handling complex,
comparison between rule-based and ML-based AI algo- high-dimensional data with greater accuracy but often
rithms, we need to consider some assumptions such as sacrifice explainability, presenting a challenge in under-
consistency of environment (i.e., static or dynamic), the standing the rationale behind their decisions. When it
complexity of problems, availability and quality of data, comes to robustness, SVMs are distinguished by their
risk of bias, need for transparency, and explainability. high resilience, particularly against adversarial attacks,
With these considerations, in our judicial case, we take thanks to their strong generalization capabilities. NNs,
these assumptions; (i) the operational environment for despite their adeptness at complex pattern recognition,
the AI system is dynamic, (ii) the complexity of the prob- exhibit moderate to low robustness and are vulnerable to
lem can be considered as high, (iii) the high quality of adversarial examples, requiring specialized methods like
datasets are available, free of bias and sensitive personal adversarial training to enhance their robustness. DTs
information, and (iv) the explanation of the decisions is offer a moderate level of robustness, valued more for
required. With these considerations, in the following, their interpretability than their resistance to adversarial
we qualitatively evaluate the compatibility of the two examples, while LR models are less robust, particularly
distinct types of algorithms with TAI principles. in complex datasets and adversarial environments. In
3.2.1. Rule-based AI terms of accountability, LR models excel due to their
These AI systems are perfectly suited to applications that straightforward and transparent nature, which makes
require small amounts of data and simple, straightfor- tracing decisions back to specific data points relatively
ward rules. These algorithms exhibit high accuracy due easy. DTs also score highly in this regard, due to their
to deterministic outcomes from well-defined rules. How- clear decision-making paths. SVMs, particularly with
ever, since the assumption of the operational environ- non-linear kernels, present a more complex picture, offer-
ment is dynamic and the problem is complex, we consider ing moderate to low accountability due to the intricacies
a moderate level for the accuracy principle. These algo- involved in their decision-making processes. NNs are at
rithms can be very robust if the rules are well-crafted the lower end of the spectrum in terms of accountability,
to handle various edge cases. But they may falter in often described as “black boxes” due to their complex, lay-
scenarios not covered by the existing rules, therefore, ered structures, although efforts like layer-wise relevance
their robustness can also be considered moderate. These propagation (LRP) and SHAP4 values are employed to
algorithms stand out for their high explainability and enhance their interpretability. The aspects of fairness and
accountability, as their rule-based nature makes them privacy are also pivotal in evaluating the TAI alignment
transparent and easy to understand, even for non-experts. of ML algorithms. The fairness of algorithms such as
LR, DTs, SVMs, and NNs is predominantly governed by
3.2.2. ML-based AI the nature of their training data. Since these algorithms
These AI systems, particularly suited for environments inherently lack bias, any unfairness in decision-making
with abundant data, vary in their alignment with TAI largely stems from biases present in the training data.
principles. For the sake of simplicity, we focus only on This reality highlights the importance of precise data
four key supervised ML models; Linear Regression (LR), collection and processing, ensuring that the data is rep-
Decision Trees (DT), Support Vector Machines (SVM), resentative and free of biases to maintain fairness in the
and Neural Networks (NNs). LR is chosen for its fun- outcomes. Alongside fairness, privacy considerations in
damental approach to data modeling. DTs offer a more these algorithms are crucial, yet they are not intrinsic
intricate decision-making structure. SVMs are known to the algorithms themselves. Instead, privacy risks are
for their efficiency in high-dimensional spaces, while closely tied to how the data is handled. Ensuring the pri-
NNs, especially in deep learning, handle complex tasks vacy and security of data, especially sensitive personal
like image and language processing. These models col- information, is vital, regardless of the algorithm in use.
lectively represent the diverse capabilities of ML and Effective data handling practices, including anonymiza-
provide insights into their trustworthiness in dynamic, tion and secure storage, play a critical role in mitigating
data-intensive scenarios. For accuracy and explainability privacy risks in machine learning applications. There-
principles, there is a notable trade-off observed across fore, in both fairness and privacy, the emphasis shifts
the algorithms. In the literature [14, 15], there has been from the algorithmic design to the careful management
a comprehensive comparison of different ML models in of the data they process. In Table 1, we summarized the
terms of their accuracy and explainability level. The ABT levels for rule-based and ML-based algorithms. This
LR and DT algorithms, while offering high levels of ex-
plainability due to their transparent nature, may not 4
https://github.com/shap/shap
Table 1 sesses the potential consequences of the principle being
Qualitative comparison between the algorithms and their compromised within the context of the tool’s application.
alignment with TAI principles. Legend; Low, Moderate, High Figure 2 illustrates the proposed approach is organized
TAI Principles Rule-based ML-based (Supervised) sequentially into four steps: Data Collection, Data Model-
Accuracy M
LR
L
DT
H
SVM
H
NNs
H
ing & Analyzing, Risk Evaluation, and Suggestion which
Robustness M L H M M operates in two modes: user-only (M1) or user-plus devel-
Accountability
Explainability
H
H
H
H
M
M
L
L
L
L
oper (M2). The figure employs a color-coded system to
Privacy Depends on data handling, not inherent to the model. differentiate between the specific actions and processes
Fairness Depends on the data pipeline. associated with each mode: elements highlighted in blue
pertain to the User, those in green correspond to the
Developer, and the components in black apply to both
comparison, which provides a framework to gauge how modes. Below, we explain each step concisely.
various algorithms align with TAI principles, supports Data Collection. The data collection process is going to
the risk assessment process effectively. In the next sec- be performed by having comprehensive questionnaires
tion, we will propose a risk-based approach, where these that cover multiple factors regarding the development of
comparative insights become a vital factor in evaluating AI tools. Depending on the involvement of the AI devel-
AI trustworthiness and assessing risk levels. oper, three different questionnaires are provided—i.e., Q1-
4. The Risk-based Approach TAI Implementation, Q2-Criticality, and Q3-Algorithmic.
Data Modeling & Analysis. The results obtained from
The primary goal of this approach is to support judges the questionnaires in the previous step flow into this
and legal practitioners with a set of best practices when step as essential inputs. Based on the scenario mode, out
utilizing AI tools in their judicial work. This includes pro- of this step, two models can be generated; (i) the Basic
viding them with a clear understanding of the potential model, which considers M1 mode, and (ii) the Advanced
risks associated with these tools and offering actionable model, which is enriched with the involvement of both
suggestions to mitigate these risks, ensuring responsible the AI developer and the user. The Advanced model
and informed use of AI in legal settings. The approach extends beyond user feedback by integrating technical
is a semi-automated process that requires user interac- insights, allowing for a more intricate analysis of the AI
tion at the beginning of the approach to collect useful tool’s alignment with TAI principles. There are different
information about the AI tool. This approach assesses automated processes in this step that are connected to
risks associated with the use of AI tools, focusing on their each obtained response for the questionnaires, namely,
alignment with TAI principles and their role in legal con- CE Assessment (P1), ABT Assessment (P2), Algorithmic
texts. Before diving into the approach, we consider some Estimation (P3), and Criticality Analysis (P4). Below, we
assumptions; (i) the user has some experience using the provide a brief description of each process; P1. This pro-
AI tool, (ii) the user does not know anything about the cess analyses responses to Q1, determining CE levels for
technical details behind the AI tool, (iii) the user knows each TAI principle. For each principle, specific properties
only about the required input and output. Typically risk are identified (as depicted in Figure 1), with each property
defines as a function of two values Likelihood and Impact being assessed through a series of targeted questions. P2.
(i.e., Risk =𝑓 (L,I)). Similarly, we formulate the likelihood To conduct this analysis, preliminary we need to identify
as function of two values which are ABT and Control the algorithm used in the AI system. In M2 mode, this
effectiveness (CE), where the ABT refers to the degree identification is straightforward as the developer spec-
to which the AI tool’s algorithm aligns with TAI prin- ifies the algorithm. In M1 mode, two scenarios arise: if
ciples. It assesses whether the algorithmic design and the tool’s documentation is available and the user can
functionality inherently support or conflict with these specify its algorithm; if not or the user is unable to spec-
principles. For instance, the tool utilized with deep neural ify the algorithm, the user is prompted to complete Q3,
networks has a high level of accuracy in prediction while which is part of the subsequent P3 process. P3. This
their “black-box” nature makes them less explainable (see process performs in the case of M1 mode, which helps us
Table 1). Instead, the CE represents the effectiveness of uncover the algorithm through responding to Q3. The
implemented controls in mitigating risks associated with responses obtained from Q3 determine if the algorithm
the AI tool. For example, strict access controls and log- is rule-based or ML-based. P4. For this analysis, the
ging mechanisms increase confidentiality mitigate the user’s responses to Q2. We made a correlation between
risk to the privacy principle. The combination of these each question in Q2 and TAI principles (they are constant
two values produces the Likelihood level which collec- in our approach), which aids in assessing the extent to
tively evaluates the probability of a TAI principle being which the principles of TAI may be affected in light of
compromised. The Impact measures the criticality of the the specific use-case scenarios provided by the user.
use-case scenario in terms of each TAI principle. It as- Risk Evaluation. In this step, we conduct likelihood
Step 1 Step 2 Step 3 Step 4
Data Modeling &
Data Collection Risk Evaluation Suggestion
Analysis
Legend
Specify mitigation controls CE levels
CE Assessment
Questionnaire
Likelihood Likelihood levels
ABT levels Assessment Actor action
Specify the algorithm
ABT Assessment Process output
Yes
Developer
Uncover the algorithm Risk Risk levels Risk Profile Automated
Assessment Translation process
Is the tool's No Algorithmic
document available? Estimation
Respond
Report
Suggestion Report
Specify the use-case criticality Impact scores Impact Impact levels Scenario Mode
Criticality Analysis
Assessment User-only
User User-plus Developer
Figure 2: The proposed risk-aware approach.
and impact assessments based on the previous step out- G. R. Marseglia, M. Massaro, et al., Artificial in-
put. Depending on the mode, the risk assessment yields telligence and surgery: ethical dilemmas and open
varying risk levels. In fact, the difference between these issues, Journal of the American College of Surgeons
models lies in the input they provide for assessing likeli- 235 (2022) 268–275.
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a lack of developer involvement, overlooking both (i) de- to a panopticon: the use and misuse of technology
tailed algorithmic insights, where it might be possible the in the regulation of judges, Hastings LJ 71 (2019).
document of the tool is not available or the user may be [4] L. Winmill, Technology in the judiciary: One
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mation, and (ii) CE levels. Instead, the advanced model between judge and machine to reduce legal uncer-
integrates insights from both actors, providing a compre- tainty in disputes concerning ex aequo et bono com-
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is translating the risk profiles into concrete suggestions. gence, 2019.
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in AI usage within legal frameworks.
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Acknowledgments [13] L. N. Tidjon, F. Khomh, Never trust, always verify:
This work was partially supported by the JuLIA project, a roadmap for trustworthy ai?, arXiv:2206.11981
funded by the Justice Programme of the European Union (2022).
— JuLIA (101046631), JUST – 2021 JTRA. [14] G. Yang, Q. Ye, J. Xia, Unbox the black-box for the
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