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Ethical Framing and the WHY-SHOULD-HOW method

This folder contains all principles available for ethical framing (fairness, privacy, reliability, sustainability, transparency, truthfulness). After the evaluation stage, the EPS framework requires that human evaluators classify the system under consideration in an impact matrix. The matrix comprises three recommendation levels tailored to each impact level - high, intermediate, and low.

A graphical representation of an Algorithmic Impact Assessment and Ethical Framing in AI, focusing on human-centered design. The image consists of two main sections with pie charts and a grid layout, indicating levels of privacy, consumer rights, antidiscrimination, transparency, and fairness. A grey silhouette icon represents a human-centered approach. The legend explains the meaning of the colors.

Hence, each principle has three distinct possible recommendations tailored to specific impact levels, e.g., Sustainability-low, Sustainability-intermediate, and Sustainability-high.

An infographic illustrating the criteria for Human-Centered Evaluation, with icons and text representing Fairness, Privacy, Reliability, Transparency, Truthfulness, and Sustainability. Each criterion is rated on a scale from low to high. The legend explains the meaning of the colors.

Each principle folder contains the corresponding set of tailored recommendations. These recommendations come in a WHY-SHOULD-HOW format.

The WHY-SHOULD-HOW methodology is the format in which the evaluation outcome is presented. The WHY step is structured to demonstrate the relevancy of each principle, providing the conceptualization and highlighting paradigmatic cases of deficit implementation in a structure that answers the questions "What is said principle?" and "Why should you care about it?". The SHOULD and HOW are attached to streamline the normative guidance and the practical tools to address it.

A diagram illustrating the relationship between AI and privacy, with a focus on transparency and fairness levels. The image shows a progression from low to high levels of transparency, privacy, and fairness, leading to AI’s role in privacy. Questions about the importance of privacy and methods to protect it are highlighted.

In each folder, the user can find a human-readable version of our recommendations as markdown files, while the HTML files are used to render our demo.