A Selection of Valuable Resources around AI, AI-Ethics(Human Rights), AI-Regulation(Governance), AI-Auditing, and Explainable AI

AI is such a broad field that it is almost impossible to cover it completely.

In the following, I would like to present a selection of sources that have helped me personally a lot to get a sustainable and holistic view of AI. Especially regarding the ethical implications of AI on society and the economy.

I hope my selection will inspire you as much as it inspired me.

Enjoy Reading

1. Understanding artificial intelligence ethics and safety — A guide for the responsible design and implementation of AI systems in the public sector

If you are seriously interested in AI ethics then this Guide is simply a Must-Read.

by Dr. David Leslie ( The Alan Turing Institute )

“By including a primer on AI ethics with the Guide, we are providing you with the conceptual resources and practical tools that will enable you to steward the responsible design and implementation of AI projects.”

Source: Understanding artificial intelligence ethics and safety

2. A Guide for Ethical Data Science (including an implementation checklist)

by the Royal Statistical Society & Institute and Faculty of Actuaries

“This guide has been developed jointly by the Royal Statistical Society (RSS) Data Science Section and the Institute and Faculty of Actuaries (IFoA) for members working in the area of data science. It is intended to complement existing ethical and professional guidance and is aimed at addressing the ethical and professional challenges of working in a data science setting.”

Contents:

1. Seek to enhance the value of data science for society

2. Avoid harm

3. Applying and maintaining professional competence

4. Seek to preserve or increase trustworthiness

5. Maintain accountability and oversight

Source: A Guide for Ethical Data Science (including an implementation checklist)

3. Explaining decisions made with AI

by Information Commissioner’s Office and The Alan Turing Institute

🔷 Part I: The basics of explaining AI

🔷 Part II: Explaining AI in practice

🔷 Part III: What explaining AI means for your organisation

This co-badged guidance by the ICO and The Alan Turing Institute aims to give organisations practical advice to help explain the processes, services and decisions delivered or assisted by AI, to the individuals affected by them.

Source: Explaining decisions made with AI

4. A Framework For The Ethical Use Of Advanced Data Science Methods In The Humanitarian Sector

Packed with useful information.

This framework provides action-orientated steps on how to innovate in an ethical, responsible, and practical way.

A collaborative effort between the Data Science Initiative (The Hague) IOM — UN Migration (Displacement Tracking Matrix (DTM)).

Contributors to this Framework:

United Nations OCHA

Berkman Klein Center for Internet & Society at Harvard University,

510 An Initiative of the Netherlands Red Cross

Translators without Borders

World Food Programme

Source: A Framework For The Ethical Use Of Advanced Data Science Methods In The Humanitarian Sector

5. A guide to using artificial intelligence in the public sector

Nice Guide by the GOV.UK

Artificial Intelligence (AI) has the potential to change the way we live and work

Embedding AI across all sectors has the potential to create thousands of jobs and drive economic growth. By one estimate, AI’s contribution to the United Kingdom could be as large as 5% of GDP by 2030.

A number of public sector organisations are already successfully using AI for tasks ranging from fraud detection to answering customer queries

The potential uses for AI in the public sector are significant, but have to be balanced with ethical, fairness and safety considerations.

Source: A guide to using artificial intelligence in the public sector

6. Artificial Intelligence, Human Rights, Democracy, and the Rule of Law

Highly Recommended.

by the Council of Europe and The Alan Turing Institute

(Dr. David Leslie, Dr. Christopher Burr, Dr. Mhairi Aitken, Josh Cowls, Michael Katell, Dr. Michael Katell, Morgan Briggs)

The purpose of this primer, co-produced by The Alan Turing Institute and the Council of Europe, is to introduce the main concepts and principles presented in the CAHAI’s Feasibility Study for a general, non-technical audience. It also aims to provide some background information on the areas of AI innovation, human rights law, technology policy, and compliance mechanisms covered therein. In keeping with the Council of Europe’s commitment to broad multi-stakeholder consultations, outreach, and engagement, this primer has been designed to help facilitate the meaningful and informed participation of an inclusive group of stakeholders as the CAHAI seeks feedback and guidance regarding the essential issues raised by the Feasibility Study.

Source: Artificial Intelligence, Human Rights, Democracy, and the Rule of Law

7. Artificial Intelligence and Children’s Rights

Very important issue!

“Most of the technologies that exist are not made with children in mind.”

Executive Summary Artificial Intelligence and Children’s Rights: This is an executive summary for the research memorandum on artificial intelligence and children’s rights.

by UNICEF and Human Rights Center, UC Berkeley School of Law

Children are the Future. Unfortunately, most of the technologies that exist are not made with children in mind.

Content:

🔹 What are children’s rights?

🔹 Children s Rights at Home YouTube

🔹 Children s Rights at Play Smart Toys

🔹 Children s Rights at School AI in Education

🔹 How Corporations and Governments Can Help Mitigate Harmful Impacts of AI on Children

Source: Artificial Intelligence and Children’s Rights

8. Harmonising Artificial Intelligence: The Role of Standards in the EU AI Regulation

by Mark McFadden, Kate Jones, Emily Taylor and Georgia Osborn

( Oxford Information Labs Ltd, Oxford Internet Institute, University of Oxford )

The draft EU AI Regulation is a far-reaching attempt to provide a regulatory foundation for the safe, fair, and innovative development of Artificial Intelligence in the European Union and is likely to have consequences across the globe. An important feature of the Regulation, which has so far provoked little academic debate, is its use of technical standards to help achieve its goals. However, standardisation is complicated and the nexus between standards and the European Commission’s goals is a challenging intersection of stakeholders, economic interests, and established standards development organizations.

Source: Harmonising Artificial Intelligence: The Role of Standards in the EU AI Regulation

9. Robustness & Explainability of Artificial Intelligence

by the European Commission

Authors: HAMON Ronan, JUNKLEWITZ Henrik and SANCHEZ MARTIN Jose Ignacio

“ This report puts forward several policy-related considerations for the attention of policy makers to establish a set of standardisation and certification tools for AI. First, the development of methodologies to evaluate the impacts of AI on society, built on the model of the Data Protection Impact Assessments (DPIA) introduced in the General Data Protection Regulation (GDPR), is discussed. Secondly, a focus is made on the establishment of methodologies to assess the robustness of systems that would be adapted to the context of use. This would come along with the identification of known vulnerabilities of AI systems, and the technical solutions that have been proposed in the scientific community to address them. Finally, the aspects of transparency and explainability of AI are discussed, including the explainability-by-design approaches for AI models.”

Source: Robustness & Explainability of Artificial Intelligence

10. Guidelines for the Ethical Development of AI and Big Data Systems: An Ethics by Design approach

Great Report, full of helpful design and development practices:

by Shaping the ethical dimensions of smart information systems– a European perspective (SHERPA)

Main authors: Philip Brey, Björn Lundgren, Kevin Macnish, and Mark Ryan

This report contains ethical guidelines for the technological development of artificial intelligence (AI) and big data systems. The guidelines differ from others in that they are directly related to design and development practices. They are intended to be actionable guidelines for systems and software development, rather than abstract principles that have no direct application in practice. We call such guidelines operational, meaning ready for use. Applying these guidelines in development practices would result in more ethical AI and big data products.

Source: Guidelines for the Ethical Development of AI and Big Data Systems: An Ethics by Design approach

11. Towards Auditable AI Systems — Current status and future directions

Informative Paper

by TÜV-Verband , Federal Office for Information Security (BSI) and Fraunhofer Heinrich Hertz Institute HHI

“Artificial Intelligence (AI) systems are playing an ever growing role as part of decision and control systems in diverse applications, among them security- and safety-critical application domains such as mobility, biometrics and medicine. The use of AI technologies such as deep neural networks offers new opportunities such as a superior performance as compared to traditional IT technologies. At the same time, they pose new challenges with regard to aspects such as IT security, safety, robustness and trustworthiness. In order to meet these challenges, a generally agreed upon framework for auditing AI systems is required. This should comprise evaluation strategies, tools and standards but these are either under development or not ready for practical use yet.”

Source: Towards Auditable AI Systems — Current status and future directions

12. List with over 150 Biases (Cognitive, Social, and Memory)

These biases affect belief formation, reasoning processes, business and economic decisions, and human behavior in general.

Contents:

🔹 Belief, decision-making, and behavioral

🔹 Memory

🔹 Social

Source: List with over 150 Biases (Cognitive, Social, and Memory)

“We need more people with strong computational statistics and machine learning skills who do not come from computer science, math or physics backgrounds but human-, social-, life- or environmental sciences. This would immensely enrich and benefit the developments of AI.” ~ (THE AI THOUGHT BOOK)

Murat Durmus
(Author of the Book: THE AI THOUGHT BOOK)

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Murat Durmus (CEO @AISOMA_AG)

Murat Durmus (CEO @AISOMA_AG)

CEO & Founder @AISOMA_AG | Author | #ArtificialIntelligence | #CEO | #AI | #AIStrategy | #Leadership | #Philosophy | #AIEthics | (views are my own)