Analyzing Communication Patterns and Challenges in Fairness-Related Decisions for Public Sector AI Systems

Core Concepts

Diverse stakeholders involved in public sector AI systems face communication challenges that can lead to misinterpretation and misuse of algorithms, with critical implications for impacted populations. Key communication patterns include: 1) Developers play a predominant role but lack guidance from domain experts, 2) End-users and policy-makers lack technical skills to interpret system limitations, and 3) Citizens are structurally absent from the algorithm life-cycle.

Abstract

The study investigates the communication processes and challenges between diverse stakeholders involved in fairness-related decisions for public sector AI systems. Through semi-structured interviews with 11 practitioners, the researchers identified key elements of the communication patterns, including the roles, tasks, skills, and phases of the algorithm life-cycle.
The main findings are:

Developers play the most prominent role throughout the algorithm life-cycle, but they lack guidance and input from stakeholders with advisory and policy-making roles, as well as domain expertise. This can lead to developers taking on extra roles they are not equipped for.

End-users and policy-makers often lack the technical skills to interpret the limitations and uncertainty of the AI systems, and they rely on developers to make decisions about fairness issues. This can result in misunderstanding and misuse of the algorithms.

Citizens, who are the data subjects impacted by the AI systems, are structurally absent from the decision-making processes throughout the algorithm life-cycle. This may lead to fairness-related decisions that do not adequately consider the perspectives of affected populations.

The researchers constructed a conceptual framework to model the key elements and relationships in the communication patterns, which can be used to identify and analyze the challenges. The framework covers Actors, Roles, Skills, Tasks, Information Exchange, and Phases of the algorithm life-cycle, and can specify when elements are "missing" in the communication process.
Overall, the study highlights the need for better governance structures, skill development, and stakeholder collaboration to address fairness issues in public sector AI systems.

Stats

"We don't know if governments and municipalities can understand the model." - P3
"There should be more focus on asking users what policy-makers perceive as risks and biases" - P8
"it depends on the type of AI. If it has an impact on citizens or uses a lot of data from citizens, it would be relevant to include a focus group of citizens from the beginning but it is less relevant for e.g. road repairs." - P5

Quotes

"the technical colleagues give advice when the model is good enough, but it's a bit of a grey area. We also rely on literature and on the technical teams' judgment." - P2
"Training for users is needed, to remind users not to rely on the tool but that the decision is up to them." - P6
"There is no direct citizen participation." - P4

Key Insights Distilled From

Analysing and Organising Human Communications for AI Fairness-Related Decisions

by Mirthe Dankl... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00022.pdf

Deeper Inquiries

How can public sector organizations develop the necessary technical skills and domain expertise among non-developer stakeholders to enable more informed and inclusive fairness-related decisions?

To develop the necessary technical skills and domain expertise among non-developer stakeholders in public sector organizations, several strategies can be implemented:

Training Programs: Public sector organizations can provide training programs and workshops to enhance the technical skills of non-developer stakeholders. These programs can cover topics such as data literacy, algorithmic understanding, and the implications of AI technologies.

Cross-Functional Teams: Encouraging collaboration between different departments and roles within the organization can help non-developer stakeholders learn from their developer counterparts. This cross-functional approach can facilitate knowledge sharing and skill development.

External Partnerships: Public sector organizations can establish partnerships with external experts, consultants, or organizations that specialize in AI and fairness-related decisions. These partnerships can provide valuable insights and training opportunities for non-developer stakeholders.

Continuous Learning: Implementing a culture of continuous learning and professional development within the organization can encourage non-developer stakeholders to upskill and stay updated on technological advancements and best practices in AI.

Mentorship Programs: Pairing non-developer stakeholders with experienced developers or data scientists as mentors can provide personalized guidance and support in acquiring technical skills and domain expertise.

By implementing these strategies, public sector organizations can empower non-developer stakeholders to make more informed and inclusive fairness-related decisions in AI systems.

What are the legal, ethical, and procedural frameworks that can help establish clear roles, responsibilities, and accountability for fairness-related decisions in public sector AI systems?

Establishing clear roles, responsibilities, and accountability for fairness-related decisions in public sector AI systems can be supported by the following legal, ethical, and procedural frameworks:

Ethical Guidelines: Adhering to established ethical guidelines for AI, such as those outlined by the European Commission's Ethics guidelines on trustworthy AI, can provide a framework for ensuring fairness, transparency, and accountability in AI systems.

Data Protection Regulations: Compliance with data protection regulations, such as the GDPR, can help ensure that data used in AI systems is handled responsibly and ethically, reducing the risk of bias and discrimination.

Algorithmic Impact Assessments: Implementing algorithmic impact assessments as part of the development process can help identify and mitigate potential biases and discriminatory outcomes in AI systems.

Stakeholder Engagement: Involving a diverse range of stakeholders, including citizens, policymakers, and domain experts, in the decision-making process can help ensure that fairness considerations are adequately addressed.

Internal Policies and Procedures: Developing internal policies and procedures that clearly define roles, responsibilities, and decision-making processes related to fairness in AI systems can promote accountability and transparency within the organization.

By integrating these legal, ethical, and procedural frameworks, public sector organizations can establish a robust framework for ensuring fairness in AI systems and holding stakeholders accountable for their decisions.

In what ways can citizen engagement and participatory design approaches be effectively integrated into the development and deployment of public sector AI systems to better reflect the needs and concerns of impacted communities?

Integrating citizen engagement and participatory design approaches into the development and deployment of public sector AI systems can enhance the inclusivity and responsiveness of these systems to the needs and concerns of impacted communities. Here are some effective ways to achieve this integration:

Community Consultations: Conducting community consultations and feedback sessions to gather input from citizens on the design and implementation of AI systems can ensure that their perspectives and concerns are taken into account.

Co-Creation Workshops: Organizing co-creation workshops where citizens can collaborate with developers and designers in the design process can help ensure that AI systems are tailored to the specific needs of the community.

User Testing: Involving citizens in user testing and feedback sessions throughout the development process can provide valuable insights into usability, accessibility, and fairness considerations from the end-users' perspective.

Transparency and Education: Promoting transparency about how AI systems work and providing educational resources to citizens can empower them to understand the technology better and make informed decisions about its use.

Ethics Advisory Boards: Establishing ethics advisory boards comprised of community representatives, experts, and stakeholders can provide oversight and guidance on the ethical implications of AI systems and ensure that they align with community values.

By incorporating citizen engagement and participatory design approaches, public sector organizations can create AI systems that are more responsive, inclusive, and reflective of the diverse needs and concerns of the communities they serve.