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<!DOCTYPE html>
<html lang="pt-BR">
<head>
<meta charset="UTF-8" />
<meta http-equiv="X-UA-Compatible" content="IE=edge" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>AI Ethics Guide</title>
<link rel="stylesheet" href="./assets/css/reset.css" />
<link rel="stylesheet" href="./assets/css/style.css" />
<link rel="shortcut icon" href="./assets/img/favicon.ico" />
</head>
<body>
<header class="header">
<!-- Nav -->
<nav class="nav">
<a href="index.html" class="nav__logo">
<img class="logo" src="./assets/img/logo.png" alt="" srcset=""
/></a>
<ul class="menu">
<li class="menu__item"><a href="index.html">Introduction</a></li>
<li class="menu__item"><a href="game.html">Guide</a></li>
<li class="menu__item"><a href="principles.html">Principles</a></li>
<li class="menu__item"><a href="tools.html">Tools</a></li>
<li class="menu__item"><a href="tradeoffs.html">Trade-offs</a></li>
<li class="menu__item"><a href="about.html">About</a></li>
</ul>
</nav>
<!-- Main Section -->
<section class="principal">
<div class="content__title">
<h1 class="title">Ethics in AI</h1>
<h3 class="subtitle">
Guide for Artificial Intelligence Ethical Requirements Elicitation
</h3>
<a href="game.html" class="button__game">Start Guide</a>
</div>
<div>
<picture>
<img
class="illustration"
src="./assets/img/card-game.svg"
alt="RE4AI Ethical Guide logo"
/>
</picture>
</div>
</section>
</header>
<!-- Section Tools -->
<main class="container main">
<h2 class="about__title">Tools</h2>
<p>List of tools on the cards</p>
<p>
1. <strong>DALEX</strong>. Principle: Transparency. Ethical issues:
Explainability; Explicability; Understandability; Interpretability. The
DALEX package takes an X-ray of any model and helps explore and explain
its behaviour, helps understand how complex models are working. Link:
https://github.com/ModelOriented/DALEX
</p>
<p>
2. <strong>InterpretML</strong>. Principle: Transparency. Ethical
issues: Explainability; Explicability; Understandability;
Interpretability. A Microsoft open source package that incorporates
machine learning techniques where you can train interpretable models and
explain black box systems, supporting global understanding of models or
the reasons behind predictions. Link:
https://github.com/interpretml/interpret
</p>
<p>
3. <strong>CALIMOCHO</strong>. Principle: Transparency. Ethical issues:
Explainability; Explicability; Understandability; Interpretability. An
implementation of Explanatory Active Learning (XAL) based on
Self-explanatory Neural Networks. Link:
https://github.com/stefanoteso/calimocho
</p>
<p>
4. <strong>ABOD3</strong>. Principle: Transparency. Ethical issues:
Explainability; Explicability; Understandability; Interpretability.
ABODE is an integrated development environment (IDE) for Behavior
Oriented Design (BOD) that allows you to visualize, develop and debug AI
in real time. Link:
https://github.com/RecklessCoding/ABOD3
</p>
<p>
5. <strong>TransparentAI</strong>. Principle: Transparency / Justice and
fairness / Non-maleficence / Responsibility / Sustainability. Ethical
issues: Explainability; Explicability; Understandability;
Interpretability; Showing / Non-bias; Redress / Security, Safety; Harm /
Responsibility, Accountability; Acting with integrity / Energy,
Resources (energy). A toolbox in Python to know if an AI-based system is
ethical, based on the AI HLEG. Link:
https://github.com/Nathanlauga/transparentai
</p>
<p>
6. <strong>Multi Accuracy Boost</strong>. Principle: Transparency /
Justice and fairness. Ethical issues: Explainability; Explicability;
Understandability; Interpretability / Non-bias. A tool for auditing and
post-processing black-box algorithms to ensure accurate predictions in
datasets with protected attributes. Link:
https://github.com/amiratag/MultiAccuracyBoost
</p>
<p>
7. <strong>Variational Fair Autoencoders (VFAE)</strong>. Principle:
Justice and fairness. Ethical issue: Non-bias. A tool that enables
training models and obtaining predictions that are less biased by the
sensitive properties of people on a pre-defined dataset. Link:
https://github.com/yevgeni-integrate-ai/VFAE
</p>
<p>
8. <strong>The Impartial Machines Project</strong>. Principle: Justice
and fairness. Ethical issue: Non-bias. A tool that attempts to eliminate
potential influences/ biases in news. Link:
https://github.com/abhayrjoshi/The-Impartial-Machines-Project
</p>
<p>
9.
<strong
>Fairness-Aware-Ranking in Search & Recommendation Systems</strong
>. Principle: Justice and fairness. Ethical issue: Non-bias. A tool that
attempts to eliminate potential influences/ biases in ranked lists
generated by recommender systems. Link:
https://github.com/saikumarkorada20/Fairness-Aware-Ranking
</p>
<p>
10. <strong>Fair-ML-4-Ethical-AI</strong>. Principle: Justice and
fairness. Ethical issue: Non-bias. Pedagogical resources for bias
detection and elimination in datasets using R. In French. Link:
https://github.com/wikistat/Fair-ML-4-Ethical-AI
</p>
<p>
11. <strong>Fair-Forest</strong>. Principle: Justice and fairness.
Ethical issue: Non-bias. A Java library that attempts to eliminate
potential influences/ biases in decision trees and random forests
. Link:
https://github.com/pjlake98/Fair-Forest
</p>
<p>
More comprehensive tools with no direct relationship to a principle:
</p>
<p>
1. <strong>Deon</strong>. A command line tool that allows you to add an
ethical checklist to data science projects. Link:
https://github.com/drivendataorg/deon
</p>
<p>
3. <strong>Interpretable AI</strong>. Principle: Transparency. Ethical
issues: Explainability; Explicability; Understandability;
Interpretability. A list of Interpretability techniques for building
robust AI applications and examples of AI propagating biases. Link:
https://github.com/thampiman/interpretability
</p>
<p>
4. <strong>Melusine</strong>. A high-level Python library for email
classification and feature extraction with a focus on the French
language. Contains Ethical Guidelines for evaluating AI design based on
the AI HLEG. Link: https://github.com/MAIF/melusine
</p>
<p>
5. <strong>SWED</strong>. An educational argument diagramming tool for
the domain of Software Engineering ethics, with a specific version to
discuss AI ethics. Link:
https://github.com/JoshuaCrotts/Software-Engineering-Ethics-Debater
</p>
<p>
6. <strong>AI collaboratory</strong>. A project that performs analysis,
evaluation, comparison and classification on some pre-defined datasets.
Link: https://github.com/nandomp/AICollaboratory
</p>
<p>
7. <strong>Fooling LIME and SHAP</strong>. Principle: Transparency.
Ethical issues: Explainability; Explicability; Understandability;
Interpretability. Code from an article where the authors aim to cheat
LIME and SHAP (two tools for XAI). Link:
https://github.com/dylan-slack/Fooling-LIME-SHAP
</p>
<p>
8. <strong>social and Ethics in ML</strong>. Principle: Justice and
fairness / Privacy. Ethical issues: Fairness, Non-bias, Consistency /
Personal or Private information. It shows how privacy and equity was
achieved in a project. Link:
https://github.com/belsonna/social_and_Ethics_in_ML
</p>
<p><br /></p>
<p><strong>Additional material</strong>:</p>
<p>
1.
<a href="https://www.exploreaiethics.com/category/tools/"
>https://www.exploreaiethics.com/category/tools/</a
>
</p>
<p>
2.
<a
href="https://docs.google.com/document/d/1h6nK9K7qspG74_HyVlT0Lx97URM0dRoGbJ3ivPxMhaE/edit"
>https://docs.google.com/document/d/1h6nK9K7qspG74_HyVlT0Lx97URM0dRoGbJ3ivPxMhaE/edit</a
>
</p>
<p>3. <a href="http://aequitas.dssg.io/">http://aequitas.dssg.io/</a></p>
<p>4. <a href="http://ethicstoolkit.ai/">http://ethicstoolkit.ai/</a></p>
<p>
5.
<a href="https://github.com/marcotcr/lime"
>https://github.com/marcotcr/lime</a
>
</p>
<p>
6.
<a href="https://github.com/slundberg/shap"
>https://github.com/slundberg/shap</a
>
</p>
<p>
7.
<a href="https://github.com/linkedin/LiFT"
>https://github.com/linkedin/LiFT</a
>
</p>
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