Digital Dackel Inc. (DD) and Linguistische Unternehmenskommunikation (LU) have entered into a partnership for a new project in sociolinguistic data mining. The project focuses on finding conspicuous words in corporate documents and aims to inform companies about potential challenges while at the same time proactively offering alternatives. Examples include male and female encoded words in texts such as job postings. The final product will assist in decreasing gender discrimination and thereby assist in getting more women into leadership positions.
The gender decoding tool is developed by DD and LU in cooperation with the University of Heidelberg and will be offered to IT service providers from 2017 onwards.
DR. SIMONE BUREL ON WHY SHE SELECTED FABIAN A. BOEHM FROM DIGITAL DACKEL INC.
Dr. Simone Burel, when asked why she selected Digital Dackel Inc., explained:
“I want to work with Fabian A. Boehm as he is one of the leading applied data scientists in large scale quantitative text analysis. Fabian’s impressive academic background and his state-of-the-art industry knowledge were the two main reasons why I had reached out to him. I appreciate his very clear mind and his strict work ethic. I think he is as mad as I am when it comes to science. And I need an off-beat mind for creating new ideas that others would label as crazy.”
FABIAN A. BOEHM (CEO DIGITAL DACKEL INC.) ON WHY HE IS VERY EXCITED ABOUT THIS PROJECT
Our CEO, Fabian A. Boehm, also asked to add the following statement from him: “I always try to work with experts that far exceed my intellectual capacities and Dr. Simone Burel even far exceeds this criteria. I also hate discrimination and when Dr. Simone Burel explained the idea to me, I immediately knew how we can scale this and I truly believe that we can significantly decrease gender discrimination by providing companies and Executives actionable insights.
Artificial intelligence (AI), data mining and machine learning tools such as IBM Watson and IBM SPSS text modellers will be used to mine data and increase our understanding through machine learning that would otherwise not be possible through our current scientific and human limitations.
I definitely believe that there is intentional gender discrimination, but I also believe that some of the gender discrimination in larger companies is more subtle and implied than we currently understand; giving visibility and proving gender discrimination in corporate documentation will encourage Executives and HR professionals to act.”
