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There are a lot of Dangers to Human Society if Data Science and Artificial Intelligence are used without Ethical Considerations. 

Ethical challenges arise when opinions on what is considered right and wrong diverge. 

The Framework V2: 12 Pillars.

DataEthics4All-12-Ethical-Pillars

Presenting The 12 Pillars of DataEthics4All Foundation’s Ethics 1st Data and AI Framework (Revised in 2023)

1. Responsible Data Collection and Use

This pillar focuses on starting at the drawing board with a clear purpose and a use case. What is it that we’re trying to do and what data do we need for that?

Avoiding the collection of unnecessary or irrelevant data is a key aspect of responsible data collection and use. Collecting data without a clear purpose or understanding of how it will be used can lead to data hoarding and potential privacy and security risks. It can also contribute to the perpetuation of biases and inequalities, as data that is not relevant or necessary for a given task may still contain sensitive or personal information.

Instead, organizations should adopt a “data minimization” approach, which involves collecting only the data that is necessary and relevant for a specific purpose. This includes understanding the context of the data and considering the potential benefits and risks associated with collecting and using it.

Adopting this approach requires a shift in mindset, away from the notion that “more data is always better” and towards a more deliberate and responsible approach to data collection and use.

2. Data Transparency and Trust

This pillar emphasizes the importance of transparency in communicating to the data subjects what data is being collected, how it will be used, and who will have access to it.

This includes providing clear and accessible privacy policies and terms of service.

3. Explicit Consent

This pillar requires obtaining explicit, informed consent from the data subjects before collecting and using their data.

It also includes providing the option to opt-out of data collection and use.

4. Sunset Policy

This pillar involves having a clear and specific timeframe for how long data will be retained, after which it will be deleted or anonymized.

5. Data Privacy and Security

This pillar highlights the importance of protecting data subjects’ privacy and ensuring data security.

This includes avoiding collecting personally identifiable information (PII) and implementing robust security measures to protect against unauthorized access, breaches, and data misuse.

6. Data Brokering

This Pillar prohibits Companies from unlimited control over personal data and on the non-transparent and uncontrolled proliferation of data transactions including selling or exchanging of personal data for commercial purposes without explicit consent from the data subjects.

7. Data Inclusivity

This pillar focuses on ensuring that data and AI systems do not perpetuate or exacerbate existing biases and inequalities.

This includes ensuring that data and AI systems are designed to be inclusive of all individuals and groups, regardless of demographic factors.

8. Data Power and Control

This pillar addresses the power dynamics inherent in data and AI systems and emphasizes the importance of giving data subjects control over their own data.

This includes providing mechanisms for data subjects to access, edit, or delete their data as well as giving them agency in decision-making around data collection and use.

9. Disinformation and Polarization

This pillar involves taking steps to mitigate the spread of disinformation and polarization through data and AI systems including content generated by Generative AI Tools and systems.

This includes promoting fact-checking and accuracy in data and AI systems and minimizing the amplification of harmful or divisive content.

10. Algorithmic Auditing

This pillar involves regularly auditing and testing AI algorithms to ensure that they are fair, transparent, and unbiased.

This includes testing for bias and unintended consequences and being transparent about the algorithmic decision-making processes.

11. Interdisciplinary Auditing

This pillar emphasizes the importance of interdisciplinary collaboration and ethical oversight in data and AI systems.

This includes involving stakeholders from different disciplines and fields from within the Company, including technical, social, and legal experts, in auditing and governance processes.

12. Independent Body Review

This pillar involves having an independent body or regulatory agency responsible for reviewing and overseeing data and AI systems to ensure compliance with ethical principles and legal requirements.

This includes ensuring accountability and transparency in decision-making and mitigating potential harms to individuals and society as a whole.

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