> For the complete documentation index, see [llms.txt](https://docs.avidml.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.avidml.org/taxonomy/effect-sep-view/ethics.md).

# Ethics

This domain is intended to codify ethics-related, often unintentional failure modes, e.g. algorithmic bias, misinformation.

<table><thead><tr><th width="97">ID</th><th width="90">Sub-ID</th><th>Name</th><th>Description</th></tr></thead><tbody><tr><td>E0100</td><td></td><td>Bias/Discrimination</td><td>Concerns of algorithms propagating societal bias</td></tr><tr><td></td><td>E0101</td><td>Group fairness</td><td>Fairness towards specific groups of people</td></tr><tr><td></td><td>E0102</td><td>Individual fairness</td><td>Fairness in treating similar individuals</td></tr><tr><td>E0200</td><td></td><td>Explainability</td><td>Ability to explain decisions made by AI</td></tr><tr><td></td><td>E0201</td><td>Global explanations</td><td>Explain overall functionality</td></tr><tr><td></td><td>E0202</td><td>Local explanations</td><td>Explain specific decisions</td></tr><tr><td>E0300</td><td></td><td>User actions</td><td>Perpetuating/causing/being affected by negative user actions</td></tr><tr><td></td><td>E0301</td><td>Toxicity</td><td>Users hostile towards other users</td></tr><tr><td></td><td>E0302</td><td>Polarization/ Exclusion</td><td>User behavior skewed in a significant direction</td></tr><tr><td>E0400</td><td></td><td>Misinformation</td><td>Perpetuating/causing the spread of falsehoods</td></tr><tr><td></td><td>E0401</td><td>Deliberative Misinformation</td><td>Generated by individuals., e.g. vaccine disinformation</td></tr><tr><td></td><td>E0402</td><td>Generative Misinformation</td><td>Generated algorithmically, e.g. Deep Fakes</td></tr></tbody></table>