Exploring Diversity and Fairness in Machine Learning

With algorithms, artificial intelligence, and machine learning becoming ubiquitous in our society, we need to start thinking about the implications and ethical concerns of new machine learning models. In fact, two types of biases that impact machine learning models are social injustice bias (bias created by society) and measurement bias (bias created by unbalanced sampling). Biases against groups of individuals found in machine learning models can be mitigated through the use of diversity and fairness constraints. This dissertation introduces models to help humans make decisions by enforcing diversity and fairness constraints. This work starts with a call to action. Bias is rife in hiring, and since algorithms are being used in multiple companies to filter applicants, we need to pay special attention to this application. Inspired by this hiring application, I introduce new multi-armed bandit frameworks to help assign human resources in the hiring process while enforcing diversity through a submodular utility function. These frameworks increase diversity while using less resources compared to original admission decisions of the Computer Science graduate program at the University of Maryland. Moving outside of hiring I present a contextual multi-armed bandit algorithm that enforces group fairness by learning a societal bias term and correcting for it. This algorithm is tested on two real world datasets and shows marked improvement over other in-use algorithms. Additionally I take a look at fairness in traditional machine learning domain adaptation. I provide the first theoretical analysis of this setting and test the resulting model on two deal world datasets. Finally I explore extensions to my core work, delving into suicidality, comprehension of fairness definitions, and student evaluations

Consensus and Subjectivity of Skin Tone Annotation for ML Fairness

Recent advances in computer vision fairness have relied on datasets augmented with perceived attribute signals (e.g. gender presentation, skin tone, and age) and benchmarks enabled by these datasets. Typically labels for these tasks come from human annotators. However, annotating attribute signals, especially skin tone, is a difficult and subjective task. Perceived skin tone is affected by technical factors, like lighting conditions, and social factors that shape an annotator's lived experience. This paper examines the subjectivity of skin tone annotation through a series of annotation experiments using the Monk Skin Tone (MST) scale, a small pool of professional photographers, and a much larger pool of trained crowdsourced annotators. Our study shows that annotators can reliably annotate skin tone in a way that aligns with an expert in the MST scale, even under challenging environmental conditions. We also find evidence that annotators from different geographic regions rely on different mental models of MST categories resulting in annotations that systematically vary across regions. Given this, we advise practitioners to use a diverse set of annotators and a higher replication count for each image when annotating skin tone for fairness research

Genetic Background Influence on Hippocampal Synaptic Plasticity: Frequency-Dependent Variations between an Inbred and an Outbred Mice Strain

For almost half a century, acute hippocampal slice preparations have been widely used to investigate anti-amnesic (or promnesic) properties of drug candidates on long-term potentiation (LTP)—a cellular substrate that supports some forms of learning and memory. The large variety of transgenic mice models now available makes the choice of the genetic background when designing experiments crucially important. Furthermore, different behavioral phenotypes were reported between inbred and outbred strains. Notably, some differences in memory performance were emphasized. Despite this, investigations, unfortunately, did not explore electrophysiological properties. In this study, two stimulation paradigms were used to compare LTP in the hippocampal CA1 area of both inbred (C57BL/6) and outbred (NMRI) mice. High-frequency stimulation (HFS) revealed no strain difference, whereas theta-burst stimulation (TBS) resulted in significantly reduced LTP magnitude in NMRI mice. Additionally, we demonstrated that this reduced LTP magnitude (exhibited by NMRI mice) was due to lower responsiveness to theta-frequency during conditioning stimuli. In this paper, we discuss the anatomo-functional correlates that may explain such hippocampal synaptic plasticity divergence, although straightforward evidence is still lacking. Overall, our results support the prime importance of considering the animal model related to the intended electrophysiological experiments and the scientific issues to be addressed

Interplay between 5-HT4 Receptors and GABAergic System within CA1 Hippocampal Synaptic Plasticity

International audienceThe type 4 serotonin receptor (5-HT4R) is highly involved in cognitive processes such as learning and memory. Behavioralstudies have shown a beneficial effect of its activation and conversely reported memory impairments by its blockade.However, how modulation of 5HT4R enables modifications of hippocampal synaptic plasticity remains elusive. To shed lighton the mechanisms at work, we investigated the effects of the 5-HT4R agonist RS67333 on long-term potentiation (LTP)within the hippocampal CA1 area. Although high-frequency stimulation-induced LTP remained unaffected by RS67333, themagnitude of LTP induced by theta-burst stimulation was significantly decreased. This effect was blocked by the selective5-HT4R antagonist RS39604. Further, 5-HT4R-induced decrease in LTP magnitude was fully abolished in the presence ofbicuculline, a GABAAR antagonist; hence, demonstrating involvement of GABA neurotransmission. In addition, we showedthat the application of a GABABR antagonist, CGP55845, mimicked the effect of 5-HT4R activation, whereas concurrentapplication of CGP55845 and RS67333 did not elicit an additive inhibition effect on LTP. To conclude, through investigation oftheta burst induced functional plasticity, we demonstrated an interplay between 5-HT4R activation and GABAergicneurotransmission within the hippocampal CA1 area