Learn about our methodology and processes.
01. Introduction

In the evolving landscape of AI ethics, co-creation transcends conventional workflows and deliverables. It is a methodology to develop sophisticated and meaningful interactions between humans and machines. It’s important to consider all stakeholders involved – from end-users and designers to analysts and engineers – to ensure that AI systems are fair and unbiased.

02. Tracing bias

Our approach is rooted in design ethnography that involves working closely with a diverse group of participants. Utilizing multi-modal methods we seek to clarify specific relationships between societal forms of discrimination and algorithmic bias. We visualize these complex interactions into a data dashboard, enabling stakeholders to understand the wider context

03. Intersectionality and detail

We prioritize intersectional perspectives to develop realistic user personas for rigorous software testing. This exploration of individual experiences provides a comprehensive understanding of potential biases, enabling refined adaptations to dataset compositions and system architectures.

04. Data Co-creation

Together with our participants, we co-create smaller exemplary datasets that can be scaled synthetically. This cooperative effort makes it possible to include a wide range of experiences and viewpoints into datasets.

05. Responding to bias

We guide development towards fair and sustainable designs that empower human agency to respond to bias. Our recommendations help align your organization with ethical standards, encouraging the development of fair and unbiased AI.

06. Conclusion

Our goal is to blend innovation with empowerment, developing sensitive AI systems that can account for human diversity. We are dedicated to uncovering and addressing bias and discrimination in order to ensure the sustainability of AI solutions and fairness for all.

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