Data exemplifies good, bad and ugly reflections of our society. While there is not a consensus on how to handle data, overlapping themes are falling within three main categories: motivation & application, analytical & creative inquiry and decision & outcomes. The motivation and application phase is concerned about the ‘WHY’ with respect to business, government and/or civic needs. The questions driving the data engagements are key, particularly those that follow the SMART (specific, measurable, attainable, relevant and timely) criteria. The analytical and creative inquiry phase considers the ‘HOW’ — as it relates to answering those aforementioned questions. Lastly, we have the decision phase which focuses on the ‘WHAT’. It’s important to assess what the analytical and creative inquiries reveals and what are impactful next steps.
The narrative and discussions surrounding fairness in algorithms are troublesome. Avoiding bias as well as fairness in and of technology are misnomers. Bias exists in unquantifiable and varying degrees. We, at least in the computer science continuum of data science, are attempting to create fairness and bias computationally-aware metrics. But, in what situation has everyone agreed that they were treated fairly? “Life isn’t fair” is a common colloquialism. I argue that since we, as a human race, have yet to experience fairness in the physical world, then there is no model to represent it in the digital world. We are reframing to accurately portray what scholars want to do — which is to computationally minimize discrimination.
The PAIR Principles
Let’s shift past the neutral state of talking and enact change every facet of an organization. This growth must happen in two arenas, simultaneously — in staffing and technology. First, we have to conduct an audit of existing organization culture. Second, we can set a strategic plan for embedding and sustaining a growth culture. Lastly, we have to repeat the first and second step every five to six months.
Publications & Invited Presentations
Brandeis Marshall (June 2019). "From Us, To Us: An Inclusivity Architecture", EGG NYC Conference. Dataiku. [YouTube]
Abstract: Time and time again, research proves that diverse teams increase innovation and profits as well as enhance work quality and end products. Yet while the philosophy of diversity in thought continues to gain traction, the full spectrum of diversity — gender, ethnic, racial, class, and sexuality — has yet to be applied. Bridging this gap is where data comes in, as it has led to greater investment in broadening Participation, Access, Inclusion and Representation (PAIR). In this talk, Dr. Marshall will share plausible approaches on how today’s enterprise can construct, support, and sustain inclusivity using data and the power of the PAIR principles.
Thema Monroe-White and Brandeis Marshall (December 2019). "Data Science Intelligence: Mitigating Public Value Failures Using PAIR Principles", Pre-ICIS SIGDSA Symposium on Inspiring mindset for Innovation with Business Analytics and DataScience, Munich Germany. [paper]
Brandeis Marshall (June 2020). "Inclusion at Scale". Women in Analytics Conference, Columbus Ohio. [presentation]
Abstract: In this talk, we’ll discuss public value failures in the data science ecosystem describe and focus on two public value failures which offer significant challenges and opportunities for data scientists and the organizations they serve. Finally, we pose the Participation, Access, Inclusion and Representation (PAIR) principles framework for organizations seeking to minimize the impacts of these failures via the creation of a taxonomy capable of deploying data science that reflects the values of the communities they aim to serve. Plausible approaches will be shared on how today’s enterprise can construct, support, and sustain inclusivity using data and the power of the PAIR principles.