Metrics for ethics – Evaluating and Integrating Ethics, Legal and Societal (ELS) values in advanced systems

This macro-project aims to address the integration of Ethics, Legal, and Societal
(ELS) values in the design, development, and utilisation of novel AI approaches,
including generative AI, Large Language Models (LLMs), and hybrid human-AI
systems. The project underscores the necessity of a multidisciplinary approach,
combining formal, empirical, and prototyping methods to reflect AI’s diverse and
evolving field. The focus is on creating a comprehensive framework encapsulating
ELS values in AI, emphasising the need for ongoing adaptation and refinement as AI
technologies evolve.

The macro-project is structured as a growing combination of building blocks, each
contributing towards a holistic understanding and practical application of ELS
principles in AI. The primary focus is on developing metrics for ethics that provide
tangible, actionable insights into the ethical dimensions of AI systems.

Topic 1: Methods And Tools For Evaluating Ai Impact

This topic explores the development of methods and tools that evaluate the impact of
AI systems from a holistic and context-specific perspective. The goal is to measure
various ELS aspects, such as fairness, bias, privacy, robustness, and transparency,
in an integrated manner. A key deliverable is the design of an integrated prototype dashboard. This dashboard will feature an interface with real-time metrics, visualisations, and contextual information to monitor multiple dimensions of AI ethics. The dashboard will enable setting thresholds and conducting risk assessments, ensuring data security and scalability.

Topic 2: Critical Multidisciplinary Studies

This topic focuses on the critical and comprehensive understanding of the ELS
implications of evolving AI systems, especially Large Multimodal Models (LMMs) and hybrid human-AI models. It includes the evaluation of international governance
approaches from the perspective of suitable implementations and understanding the
environmental impact of AI infrastructure. Another significant aspect is the
exploration of approaches for developing sustainable AI practices, considering the
long-term implications of AI technologies on society and the environment. Last but
not least, a practical instantiation of these principles and methods on an AI-based
software product (Misinformation Detection) enables practitioners to maintain strong relevance and actionability of the recommendations.

This macro-project emphasises the importance of an adaptive, continually evolving
approach to integrating ELS values in AI. It acknowledges that as AI technologies
develop, so too must our methods and understanding of their ethical, legal, and
societal impacts. This project is poised to contribute significantly to the field of AI
ethics, offering practical tools and deep insights into the responsible development
and use of AI technologies.

The call for contributions for this macro-project had a deadline set for the end of
November 2023, including several key events and milestones. Firstly, there has been
an Integration Workshop for Topic 1 in April 2024 in Umeå. This workshop primarily
focused on developing a prototype dashboard integrating various metrics. The
culmination of the efforts in the macro-project was presented at a final workshop at
HHAI 2024, at the end of June at INESC TEC (Porto). During this meeting,
participants shared insights and challenges of their work on macro-project 4 –
“Metrics for Ethics”, and included a roundtable that brought together members from
the European project HumanE-AI and invited companies such as NOS and Feedzai.
The “Trustworthy Assessment for Companies” questionnaire, integrated into the
dashboard, was debated in the roundtable. From the discussion emerged
suggestions for improvements for the dashboard, to the questionnaire and inspiring
reflections for companies regarding access and development, the need for incentives
to motivate better practices, and the consequences for the brand if they do not adopt
an ethical approach in their system.

Dashboard For Ethical And Societal Principles

A main component of the WP5 macro-project is the dashboard for monitoring and
analysing ethical and societal principles relevant to practical applications. This
resulted in an integrated framework for evaluating the ethics of Artificial Intelligence
(AI) systems, with a specific focus on high-risk applications such as creditworthiness
assessment. The research addresses the critical ethical dimensions of AI, including
explainability, fairness, robustness, and transparency, which are essential for
ensuring that AI systems operate in a manner that is both effective and ethically
sound.

The ethical evaluation of AI systems is crucial, particularly in domains where
decisions can significantly impact individuals’ lives, such as finance. Explainability is
highlighted as a key aspect, referring to the ability of AI systems to make their
operations and outcomes understandable to humans. This is especially important in
financial applications, where AI-driven decisions can affect creditworthiness and
access to financial resources. The study also emphasises the importance of fairness
in AI, particularly in avoiding biases that could lead to discriminatory outcomes.
Another domain with potential individual and societal impact is misinformation, a topic on which we address possible information bias and consequences for the
trustworthiness of information sources using the RUWA dataset.

Methodology

The research uses the German Credit Dataset (GCD) as a case study to evaluate the
ethical dimensions of AI models. This dataset includes sensitive attributes like age
and gender, which can introduce bias into AI decision-making processes. To address
this, the AI Fairness 360 (AIF360) library is employed to assess and mitigate bias in
the data and models.

The study employs logistic regression and decision tree as the primary model and evaluates it using various fairness metrics such as Disparate Impact Ratio (DIR) and Smoothed Empirical Differential Fairness (SEDF). The findings reveal inherent biases in the dataset, particularly favouring older individuals, which highlights the need for careful bias management to ensure fair AI outcomes.

In addition to addressing bias and fairness issues, the research also emphasises the importance of explainability, robustness, and transparency in AI systems. To explain credit rejection, a questionnaire was first used to identify the most effective method. The questionnaire involved 34 participants, who were presented with three cases of credit rejection analysed using LIME, SHAP, and Counterfactual methods. The participants were asked which method produced the most understandable results and which provided the most reliable explanation. Based on those results, clustering was then applied to all rejection cases to identify the feature groups that contribute most to the rejection.

For robustness, a metric was developed to reward consistency between performance under adversarial attacks and performance with real data. Transparency, being a critical ethical requirement for trustworthy AI, has gained increased attention since the approval of the AI Act. The approach to transparency focuses on two key areas: datasets and AI systems, as outlined in the work of Hupont et al.

Principles Considered In The Study

– Explainability: The dashboard includes tools that provide post-hoc explanations
for AI decisions and visualisation and interpretation aids. These features help
users understand AI-driven outcomes, enhancing transparency and trust in the
system. The choice of the post-hoc method for explanations was based on the
results of a questionnaire, as described in the methodology.
– Fairness: The dashboard integrates fairness metrics, such as Disparate Impact
Ratio (DIR) and Smoothed Empirical Differential Fairness (SEDF). These tools assess and mitigate biases within the AI system, ensuring that it does not
disproportionately disadvantage any group, particularly those based on sensitive
attributes like age and gender.
– Robustness: The dashboard offers frameworks and tools to simulate adversarial
attacks and assess the AI system’s robustness. It includes features for comparative analysis to evaluate how well different models perform under stress or manipulation, ensuring consistent and reliable performance.
– Transparency: The dashboard includes transparency self-assessment tools that
allow users to evaluate the transparency of their AI systems and datasets. These
mechanisms ensure that the inner workings of AI systems are accessible and
understandable to stakeholders, supporting accountability and ethical standards.
– Trustworthiness: The dashboard provides comprehensive assessments
combining accuracy, robustness, and fairness evaluations. It includes continuous
monitoring tools to maintain the trustworthiness of AI systems over time, ensuring
that they operate reliably and ethically.
– Legal Compliance: The dashboard incorporates features that align AI metrics
with the requirements of the EU AI Act and other relevant legal standards. It
includes tools for documenting and justifying design choices supporting legal
compliance in developing and deploying AI systems.

Dashboard

To support the practical application of these ethical considerations, the study
introduces the dashboard “Metrics for Responsible AI Principles.” This dashboard
integrates various methodologies and tools to assess the ethical dimensions of AI
systems. It serves as a practical tool for users to understand the potential and
limitations of using metrics to evaluate compliance with ethical and legal standards,
particularly in the context of the AI Act.
This comprehensive approach to evaluating AI ethics in high-risk applications
provides valuable insights into the challenges and solutions for developing AI
systems that are fair, transparent, and robust.
Figure 1 illustrates the first page of the dashboard for the German Credit dataset
study. On the left side, users can select options related to the model, sensitive
attributes, and bias metrics. Results for Bias, Fairness, Explainability, and
Robustness are displayed immediately. Additional information on each dimension,
including legal aspects where applicable, is also provided.

Conclusion

The study provides a detailed analysis of the performance and robustness of various
AI models. It is observed that there is often a trade-off between accuracy and
robustness, with some models losing significant accuracy when subjected to
adversarial conditions. The research highlights the need for a balanced approach
that ensures high performance and strong resilience against adversarial attacks.
Legal implications are also a significant focus of the study, particularly in the context
of the EU AI Act, which sets rigorous standards for high-risk AI systems.

The research explores how the metrics used in the evaluation framework can help ensure compliance with these legal requirements, including transparency, fairness, and robustness. While metrics alone do not guarantee legal compliance, they are
essential tools for measuring and demonstrating adherence to legal standards.
The study concludes by advocating for using multiple metrics to assess AI systems
from various ethical perspectives. The research emphasises that developing ethical
AI systems is a complex process that requires careful consideration of trade-offs
between different ethical principles. The findings contribute to the broader
understanding of how to design and implement AI systems that are effective and
aligned with ethical and legal standards.