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| title | url |
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| fake ai hoax paper |
Title: "Deconstructing Binaries: Interrogating the Hegemonic Paradigms in Machine Learning"
Abstract: Machine Learning (ML) as a field has largely embraced binary constructs as foundational to its functioning, with 0/1 and true/false distinctions underpinning many of its core algorithms. This paper argues that these binary frameworks are not merely technical conveniences but are, in fact, reflective of deeper hegemonic paradigms that perpetuate exclusionary practices and systemic biases. By uncritically adopting these binaries, ML inadvertently reinforces a worldview that marginalizes complex, nuanced identities and experiences.
We propose a critical examination of these binaries, questioning the necessity and ubiquity of dualistic thinking within ML. We suggest that the field's reliance on binary classification not only limits its predictive accuracy in certain contexts but also fails to capture the rich, fluid nature of human experiences and societal structures. Instead, we advocate for a "fluid" approach to algorithms that allows for more nuanced and inclusive representations of reality, rejecting the oversimplified and often exclusionary nature of strict binary outcomes.
Furthermore, this paper argues for the integration of intersectional data that reflects the diverse and overlapping categories of identity, including race, gender, class, and more. Current ML models often overlook these complexities, leading to outcomes that fail to serve, and even harm, underrepresented populations. We critique the prevailing notion that ML is a neutral, objective tool, highlighting the lack of socio-political context in algorithmic decision-making processes.
In conclusion, we call for an epistemological shift in the field of Machine Learning. This shift involves moving away from a purely positivistic, binary approach towards one that is reflective, inclusive, and aware of the social dimensions of technology. By reimagining the foundational paradigms of ML, we can work towards a more equitable and nuanced understanding of the world, one that respects and represents the full spectrum of human experience.
Appendix:
A. Expanded Discussion on Binary Constructs in ML:
Detailed analysis of how binary constructs are embedded in ML algorithms.
Case studies highlighting the limitations and biases introduced by binary classification.
B. Theoretical Framework for a Fluid Approach:
Outline of theoretical underpinnings for moving beyond binary thinking.
Discussion on existing and potential methodologies that embrace complexity and fluidity.
C. Intersectionality in Data Science:
Exploration of intersectionality theory and its relevance to ML.
Examples of how intersectional analysis can be incorporated into data sets and model design.
D. Case Studies of Non-binary Models:
Detailed case studies where non-binary models have been successfully implemented.
Analysis of the outcomes, challenges, and opportunities presented by these models.
E. Ethical Considerations and Future Directions:
Discussion on the ethical implications of perpetuating binary constructs in ML.
Vision for a future where ML is more inclusive, reflective, and socially aware, including potential pathways and strategies to achieve this goal.