Designing Responsible AI for the Public Good: How Ethical Practices Empower Better Public Services

Design for Responsible AI Development in the Public Sector

What Role Should Design Play in Shaping AI?

Designers face the challenge of creating public services that prioritize the common good. Achieving this requires more than just innovation—it demands informed professionals in the public sector who can responsibly manage the evolving AI landscape. Teams designing and delivering AI systems must recognize the ethical risks and opportunities inherent in these technologies.

Equally important is empowering public institutions to critically engage in ethical choices and collaborate to ensure equitable outcomes. This shift brings new responsibilities and learning curves for designers, who can facilitate ethical decision-making in collaborative development. Doing so drives responsible innovation and promotes better data practices.

This article covers five key topics:

• Responsible AI Matters
• Designers Have a Say
• Ethics by Design: A Practical Framework
• Internal Engagement: Participatory Workshops
• External Engagement: Co-Create Understanding

While AI governance is critical to shaping ethics, responsible practices are valuable even without formal structures. Many designers operate without established models, so embedding critical thinking into daily work is essential regardless of governance frameworks.

Responsible AI Needs Designers!

Many designers wonder how they can contribute to ethical AI without formal training in ethics or moral philosophy. The answer lies in Applied Ethics—a practical branch of ethics guiding everyday decisions in AI development.

Applied Ethics focuses on moral principles like fairness, justice, and right versus wrong. For example, in a welfare benefits system, it asks, “How do we ensure the system doesn’t discriminate based on gender or ethnicity?” These principles guide design and development.

Responsible AI builds on these principles by implementing them throughout the lifecycle. It translates values into actionable and measurable practices, ensuring technology aligns with human values in real situations. While ethics defines the “why,” Responsible AI defines the “how” by integrating values into processes, governance, and accountability.

Together, Applied Ethics and Responsible AI convert ethical principles into practical actions within AI projects.

Why Responsible AI Matters

Responsible AI is closely tied to inclusion. Diverse perspectives and datasets help create systems that serve everyone, especially marginalized groups. Models trained on varied data perform better across different inputs, improving accuracy and fairness.

Better performance attracts a wider user base, expands reach, and opens new opportunities. Responsible AI also supports compliance with regulations like the EU AI Act, reducing legal risks and building trust with users and regulators. However, regulations often lag behind technology.

Organizations must go beyond compliance by embedding responsible practices to guarantee fairness, transparency, and accountability. When laws fall short, best practices grounded in core values guide responsible innovation. Ultimately, responsible AI is about building trust, focusing on human impact, and creating technology that benefits society.

Ethics by Design: A Practical Framework

AI’s real value appears when integrated into products and services that reflect human values and meet actual needs. Ethics integration is continuous, adapting as systems evolve. Embedding AI governance within Design-Driven Development empowers teams to align innovation with societal and organizational goals. This fosters collaboration, inclusivity, accountability, and iteration.

Adding ethical checkpoints throughout the development process enables an Ethics-by-Design approach. This proactive method addresses risks early rather than reacting after problems emerge. Designers and developers hold increasing power in deciding what data is collected, how it’s interpreted, and how outputs are used—shaping societal narratives and priorities.

This shift increases moral responsibility across the AI lifecycle and demands collective effort to ensure fair outcomes. Integrating ethics early keeps responsibility and accountability at the center throughout development.

Internal Engagement: Participatory Workshops

Hosting internal workshops on ethical risks and bias awareness is a practical way to raise team consciousness about biases in data and algorithms. Hands-on activities and discussions help build shared understanding and establish bias mitigation practices.

Reflecting on Product Impact

Encourage teams to consider these questions to evaluate the product’s value and inclusivity:

• What Success Looks Like? What does success look like for this product? How will we measure it? List key performance indicators and metrics.
• Positive vs. Negative Impacts: What positive outcomes can this product deliver, and how can we enhance them? What negative outcomes might occur, how likely are they, and how can we reduce these risks?
• Impact on People: Who are the users and stakeholders? How do we ensure their needs and perspectives are represented? For whom might the product fail, and what unintended harm could arise for vulnerable groups? How can we mitigate these risks?
• Inclusivity and Accessibility: How do we design for inclusivity and accessibility? How will we validate this through testing or feedback? Are any groups unintentionally excluded, and how do we address this to prevent harm?

Bias Reflection Activity

Teams often don’t fully represent the people they design for. Encourage reflection on personal experiences, perspectives, and implicit biases. Questions to consider include:

• Are we diverse? Where does power lie in society?
• What implicit biases might influence our AI decisions?
• How can we avoid bias and promote fairness in AI systems?

This activity fosters shared understanding of bias and power dynamics, encouraging fairer, more accountable design practices.

External Engagement: Co-Create Understanding

Building trust in AI within public institutions is vital. However, over-reliance on AI carries risks. Unchecked AI systems can perpetuate biases or errors, reinforcing systemic inequalities. Responsibility must remain with humans.

Co-creating understanding of AI and ethics is challenging but rewarding. Many public servants encounter AI for the first time, so building shared terminology and knowledge step-by-step is crucial. Engagement should start with assessing participants’ capabilities and knowledge, fostering trust and curiosity.

Using participatory design methods—such as visual and tangible tools—makes abstract concepts tangible. Scenario-based activities tailored to specific roles help make AI ethics relatable and personal.

People’s ability to imagine broader societal impacts can be limited when facing complex new topics. Starting with concrete, context-specific examples eases learning. Facilitators should begin with lower abstraction levels, gradually introducing more complex ideas as understanding grows.

Context Sensitivity of Ethics

Ethics depends on context, shaped by the unique needs, values, and challenges of each situation. There is no one-size-fits-all approach. Customizing content to participants’ industries, knowledge, and the societal impact of their AI systems makes discussions more effective and solutions more relevant.

Customized workshops promote meaningful exploration, deeper engagement, and actionable insights. They empower participants to critically evaluate AI risks and benefits, becoming more responsible and informed users.

Conclusion: Shaping AI Responsibly Through Design

Designers play a vital role in guiding AI development to emphasize fairness, inclusivity, and positive societal impact. Integrating ethical principles at every stage, fostering collaboration, and customizing engagement bridges innovation and responsibility.

This approach demands new mindsets focused on human-centered, equity-driven practices. When done well, AI systems empower individuals, build trust, and reflect shared values. Together, we can ensure AI serves the common good and upholds public interest.

Digitalizing government services requires more than technical skills. It demands forward thinking, social awareness, and designing with real impact in mind.

For practical resources on AI ethics and responsible design, explore Complete AI Training’s latest AI courses.