Computers & Ethics Lecture Notes
  • 🤖COMPUTERS & ETHICS
    • 🇺🇸Computers & Ethics Lecture Notes
      • 1. Introduction and Defining the Field of Computer Ethics
      • 2. Perspectives on Artificial Intelligence
      • 3. Concepts of AI Ethics
      • 4. Technical Recommendations on the Ethics of AI
      • 5. Ethical Principles, Benefits and Issues of AI
      • 6. Data Privacy-Preserving Techniques
      • 7. Legal Aspects of IoT
      • 8. Cybersecurity Cases on Global Perspectives
      • 9. Stakeholders, Ethical Digital Ecosystem and Standards
      • 10. Human Rights and AI
      • 11. AI Ethics & Consequences
      • 12. Blockchain and Ethical Perspective
      • 13. Metaverse and Gaming Technologies by Ethical Perspective
      • 14. Responsible Use of AI in Digital Organizations
      • 15. Reliable AI to Design a Better Future - Student Presentations, Discussion & Conclusion
      • Recommended Podcasts
    • 🇹🇷Bilgisayarlar & Etik Ders Notları
      • 1. Bilgisayar Etiği Alanına Giriş ve Kavramlar
      • 2. Yapay Zekâya Genel Bakış
      • 3. Yapay Zekâ Etiği ve Kavramları
      • 4. Yapay Zekâ Etiğine İlişkin Teknik Öneriler
      • 5. Yapay Zekânın Etik İlkeleri, Yararları ve Sorunları
      • 6. Veri Mahremiyetini Koruyucu Teknikler
      • 7. Nesnelerin İnternetinin Hukuki Yönleri
      • 8. Küresel Perspektiflerde Siber Güvenlik Vakaları
      • 9. Paydaşlar, Etik Dijital Ekosistem ve Standartlar
      • 10. İnsan Hakları ve Yapay Zekâ
      • 11. Yapay Zekâ Etiği ve Getirdiği Sonuçlar
      • 12. Blokzincir ve Etik Perspektif
      • 13. Metaverse ve Oyun Teknolojilerine Etik Bakış
      • 14. Dijital Organizasyonlarda Yapay Zekânın Sorumlu Kullanımı
      • 15. Daha İyi Bir Gelecek Tasarlamak için Güvenilir Yapay Zekâ - Öğrenci Sunumları, Tartışma ve Sonuç
      • Önerilen Podcastler
Powered by GitBook
On this page
  • UNESCO Recommendation on the ethics of AI
  • Fairness and non-discrimination
  • Transparency and explainability
  • Safety and security
  • Right to Privacy, and Data Protection
  • Human oversight and determination
  • Proportionality and Do No Harm
  • Responsibility and accountability
  • Technical and non-technical methods to realize Trustworthy AI
  1. COMPUTERS & ETHICS
  2. Computers & Ethics Lecture Notes

4. Technical Recommendations on the Ethics of AI

April 1, 2022 lectured by Dr. Mehmet HAKLIDIR and written by Dr. Merve Ayyüce KIZRAK

Previous3. Concepts of AI EthicsNext5. Ethical Principles, Benefits and Issues of AI

Last updated 3 years ago

About

In this week's lecture, We are glad to be hosted Head of Cloud Computing and Big Data Research Lab (B3LAB) at TUBITAK BILGEM Dr. Mehmet Haklıdır. In this course, after briefly talking about the ethical approaches of different organizations, we discussed the subject technically through case studies by referring to UNESCO's recommendations on the Ethics of AI guide.

Trustworthy Artificial Intelligence (TAI)

"Trustworthy AI refers to AI that respects the values-based principles." ()

"AI built upon value-based principles such as inclusive growth, sustainable development and well-being, human-oriented values and objectivity, transparency and explainability, robustness, security and trust, and accountability." ()

Global AI Ethics

  • : The OECD.AI expert group on implementing Trustworthy AI (ONE TAI) aims to highlight how tools and approaches may vary across different operational contexts.

  • : GPAI strive to foster and contribute to the responsible development, use and governance of human-centred AI systems, in congruence with the UN Sustainable Development Goals.

  • : The Committee examined the feasibility and potential elements on the basis of broad multi-stakeholder consultations, of a legal framework for the development, design and application of AI, based on Council of Europe’s standards on human rights, democracy and the rule of law.

  • : UNESCO developed an international standard-setting instrument on the ethics of AI, in the form of a recommendation.

UNESCO Recommendation on the ethics of AI

Values

  • Respect, protection and promotion of human rights and fundamental freedoms and human dignity

  • Environment and ecosystem flourishing

  • Ensuring diversity and inclusiveness

  • Living in peaceful, just and interconnected societies

Principles

  • Proportionality and Do No Harm

  • Safety and security

  • Fairness and non-discrimination

  • Sustainability

  • Right to Privacy, and Data Protection

  • Human oversight and determination

  • Transparency and explainability

  • Responsibility and accountability

  • Awareness and literacy

  • Multi-stakeholder and adaptive governance and collaboration

Fairness and non-discrimination

The bias can be caused by one or multiple of the steps explained below.

Data Collection step is where bias is most encountered. Dataset-based bias may occur if the data is created by people with a certain tendency or the equipment where the data is collected is distorted.

Data Preprocessing is preparing the data for the model. The processes applied at this stage may cause bias. For example, data that represents missing values can cause bias or data filtering operation can be the reason for the break-in of data integrity.

Modeling is the training process to recognize patterns at this step, the reason for the bias may be due to the parameters of the model.

When AI Goes Bad: Google Photos’ Shame

Transparency and explainability

The Explainable AI (XAI) aims to create a suite of machine learning techniques that:

  • Produce more explainable models, while maintaining a high level of learning performance (prediction accuracy); and

  • Enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners.

Explainable AI has two main parts:

  • Explainable Model: Developing more efficient and more advanced AI techniques than existing methods, or renewing existing methods in this context, in order to make the AI system 'explainable'

  • Explainable Interface: Making end users, who do not have expertise in AI, but who are experts in the application field in which AI is used, evaluate and interpret the outputs of AI by interacting with the created model at an advanced level.

Safety and security

Right to Privacy, and Data Protection

Human oversight and determination

Proportionality and Do No Harm

Responsibility and accountability

Technical and non-technical methods to realize Trustworthy AI

Next week, we will do a case study in our face-to-face class at Bahçeşehir University Beşiktaş campus. We will not have guests. Let's see what the results will be.

Case Study - Gender Bias:

Case Study -

Open Source Tool 1 -

Open Source Tool 2 -

Case Study -

Open Source Tool -

Both technical and non-technical methods can be used to implement the above requirements. These cover all phases of an AI system's lifecycle. An evaluation of the methods used to implement the requirements, as well as reporting and justifying changes to implementation processes, should be done on an ongoing basis.

🤖
🇺🇸
Unfairness Of Deep Learning Methods Arising Gender Bias COVID-19 Diagnosis of Medical Images
An Explainable AI (XAI) Platform for Automated Guide Vehicle
An eXplainability toolbox for machine learning
TransparentAI
Towards Federated Learning In Identification Of Medical Images: A Case Study
H2020 – Human AI Net
A process like the one in the figure can be taken as an example.
Dr. Mehmet HAKLIDIR
OECD Definition
The National Artificial Intelligence Strategy of Türkiye Definition
OECD Network of Experts on AI - ONE AI
Global Partnership on AI – GPAI
Ad Hoc Committee on Artificial Intelligence of the Council of Europe - CAHAI
UNESCO Intergovernmental Meeting related to the draft Recommendation on the Ethics of AI
Recomended Documantary- Coded Bias
the iceberg
Realising Trustworthy AI throughout the system’s entire life cycle - Ethics Guidelines for Trustworthy AI
News Source