> For the complete documentation index, see [llms.txt](https://ayyucekizrak.gitbook.io/computers-and-ethics-lecturenotes/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ayyucekizrak.gitbook.io/computers-and-ethics-lecturenotes/computers-and-ethics/computers-and-ethics-lecture-notes/4.-technical-recommendations-on-the-ethics-of-ai.md).
# 4. Technical Recommendations on the Ethics of AI
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About [Dr. Mehmet HAKLIDIR](https://www.linkedin.com/in/mehmet-haklidir-a7203839/?originalSubdomain=tr)
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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." ([OECD Definition](https://oecd.ai/en/ai-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." ([The National Artificial Intelligence Strategy of Türkiye Definition](https://cbddo.gov.tr/SharedFolderServer/Genel/File/TRNationalAIStrategy2021-2025.pdf))
#### Global AI Ethics
* [**OECD Network of Experts on AI - ONE AI**](https://oecd.ai/en/network-of-experts)**:** The OECD.AI expert group on implementing Trustworthy AI (ONE TAI) aims to highlight how tools and approaches may vary across different operational contexts.
* [**Global Partnership on AI – GPAI**](https://gpai.ai/)**:** 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.
* [**Ad Hoc Committee on Artificial Intelligence of the Council of Europe - CAHAI**](https://www.coe.int/en/web/artificial-intelligence/cahai)**:** 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 Intergovernmental Meeting related to the draft Recommendation on the Ethics of AI**](https://en.unesco.org/artificial-intelligence/ethics)**:** 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.
#### Case Study - Gender Bias: [Unfairness Of Deep Learning Methods Arising Gender Bias COVID-19 Diagnosis of Medical Images](https://www.researchgate.net/publication/352055329_Unfairness_of_Deep_Learning_Methods_Arising_Gender_Bias_in_Covid-19_Diagnosis_of_Medical_Images#fullTextFileContent)
**When AI Goes Bad:** Google Photos’ Shame
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Recomended Documantary- Coded Bias
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### 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.
#### Case Study - [An Explainable AI (XAI) Platform for Automated Guide Vehicle](https://arxiv.org/pdf/2103.05154.pdf)
#### Open Source Tool 1 - [An eXplainability toolbox for machine learning](https://ethicalml.github.io/xai/index.html)
#### Open Source Tool 2 - [TransparentAI](https://github.com/Nathanlauga/transparentai)
### Safety and security
### Right to Privacy, and Data Protection
#### Case Study - [Towards Federated Learning In Identification Of Medical Images: A Case Study](https://www.researchgate.net/publication/351305905_Towards_Federated_Learning_in_Identification_of_Medical_Images_A_Case_Study)
### Human oversight and determination
#### Open Source Tool - [H2020 – Human AI Net](https://www.humane-ai.eu/)
### Proportionality and Do No Harm
### Responsibility and accountability
### Technical and non-technical methods to realize Trustworthy AI
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. [A process like the one in the figure can be taken as an example.](https://ec.europa.eu/futurium/en/ai-alliance-consultation.1.html)
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.