‘Be the Change, you want to see in the world.’
~Mahatma Gandhi
Assistant Professor Bendoni is a Data Ethics Advocate, Data Ethics Trailblazer, and a member of the DataEthics4All Think Tank working on making an impact on ethical data mining and raising awareness of biases data.

This past year DataEthics4All invited Wendy K. Bendoni Assistant Professor of Marketing from Woodbury University’s School of Business in April 2020 to officially become a Think Tank founding member. The Think Tank consists of AI Researchers, Data Scientists, Policy Makers and Data Ethics Advocates.  The area of AI data analysis has been a primary focus for Professor Bendoni research for marketers in the last past five years.   She looks forward to contributing in the area of ethical data research.  The DataEthics4All Think Tank will be responsible for Independent Research on the Impact of Unethical Data Use. The committee follows the Ethical Pillars that include: Preserving Data Privacy, Fair Data Processing, Clear Data Ownership, Data Transparency and Trust, Preventing Ad Technology Weaponization and Evolution of Data Ethics in Times of Crisis. Some additional members of our DataEthics4All Advisory Board include: Chief Data Officer, TD Ameritrade; Data Strategist at IBM; Head of Blockchain, Digital Assets, and Data Policy, World Economic Forum; Corporate Vice President, New York Life Insurance Company, Head of Digital Intelligence & Analytics, First Republic Bank: VP & Global Chief Data Officer, GE; VP and Chief Information Security Officer, Hitachi Vantara.  In June 2020 Professor Bendoni also become part of the development event committee for Ethics4NextGen AI Summit and Hackathon to raise the next generation of Inclusive AI.  

Find out more about DataEthics4Al.com

DataEthics4All 12 Ethical Pillars

Learn more at DataEthics4All.org

1. Preserving Data Privacy

 Preventing the Violation of Data Privacy and data mishandling by selling to third parties without consent.

2. Fair Data Processing

Making sure that the Data gathering and processing from multiple sources with the intention of combining these into a single data set for insights, trends and analysis is done fairly and without manipulation through aggregation. 

3. Preventing Data Misuse

Making sure that there is no inappropriate use of data as perceived by the data subject as defined when the data was initially collected.

4. Clear Data Ownership

Clear understanding of ownership and accountability of personal identifiable information. (PII)

5. Explicit Data Consent

Making sure that there is an explicit data consent for the personal information obtained directly from a customer and both parties are fully aware of the use and benefits of that data.

6. Data Power and Control

Stopping Companies from unlimited control over personal data and on the non-transparent and uncontrolled proliferation of data transactions.

7. Data Transparency and Trust

Giving Customers control of their personal data and earning their Trust and Goodwill.

8. Data Quality Auditing

Preventing Unfair Discrimination by auditing the quality of data and making sure that the available data is representative of the whole population or phenomenon of study.

9. Interdisciplinary Algorithmic Auditing

Through Interdisciplinary Algorithmic Auditing we can detect Confirmation and other Biases in Artificial Intelligence Models and make sure there are no unwanted consequences.

10. Combating Deliberate Disinformation

Combating deliberate disinformation such as Fake News and Misleading Memes by making sure our Technology verifies the Sources of News before amplifying it.

11. Preventing Ad Technology Weaponization

Making sure that our Social Media Platforms and Ad Technologies are not being used for Political and Personal Gains.

12. Evolution of Data Ethics in Times of Crisis

Understanding the Pros and Cons of the Evolution of Data Ethics: Privacy, Consent, Transparency, etc in times of crisis.