Biased by Design

What Is Biased by Design?

Biased by Design is a free resource in the design category that examines how design choices contribute to algorithmic bias in systems like AI tools and interfaces. It focuses on the role of designers in embedding or mitigating bias through structural decisions, such as interaction flows, metrics, and output presentations. Biased by Design fits into the design workflow during the planning and auditing phases, where teams assess potential fairness issues before deployment.

What Biased by Design Does

• Explains how design elements like interfaces and dropdown menus can propagate data biases into user interactions.
• Details the impact of metrics design on algorithmic outcomes, showing how narrow success definitions lead to skewed results.
• Highlights oversight risks in visual presentations, such as search results or automated messages that reinforce disparities.
• Outlines participatory design methods for engaging affected communities to identify vulnerabilities.
• Describes fairness by design frameworks to integrate bias considerations across the development lifecycle.
• Covers bias audits and mitigation strategies for ongoing assessment of algorithmic outputs.

How Biased by Design Can Be Used

• In product design teams, to review AI interfaces for lighting or skin tone variations that affect facial recognition performance.
• During hiring tool development, to audit training data for historical biases that disadvantage certain groups.
• For creating transparency mechanisms, like explanation interfaces for algorithmic decisions, to empower users.
• In workflow audits, to apply bias impact statements that probe assumptions about algorithm purpose and process.
• By cross-functional teams brainstorming potential harms before model training, followed by regular disparate impact checks.

Who Is Biased by Design For?

Biased by Design serves designers, product managers, and AI developers working on systems with societal impacts, such as hiring platforms or recommendation engines. It suits teams at any experience level conducting ethical reviews, particularly those addressing indirect discrimination in assessments or feedback loops from skewed data. Employers implementing AI in workplaces can use it to assess bias risks and train staff on equality obligations. Visit Biased by Design