Our society is in a technological paradox. For many people, life events are increasingly influenced by algorithmic decisions, yet we are discovering how they make much-needed algorithmic distinctions. Because of that paradox, IT management is in a unique position to combine human interventions that address diversity and inclusion with a team and equitable algorithms that are accountable to a diverse society.
IT managers today are faced with this paradox due to the increasing application of Machine Learning Operations (MLOps). MLOps rely on IT teams to help manage the pipelines that have been created. Algorithmic systems involving IT teams need to be critically scrutinized for consequences that may be accompanied by social bias.
To understand social bias it is necessary to define diversity and inclusion. Diversity is an appreciation of the traits that make a group of people unique, while inclusive practices and norms make people from these groups feel welcome to participate in a given organization.
Social bias occurs through two major processes when developing processes initiated by programmatic software or algorithmic decisions. One source is the fragility inherent in machine learning classification methods. Models classify the training data either through statistical clustering of observations or by creating a threshold that mathematically predicts how the observations are related, such as in regression. The challenge is when those associations are announced without considering the social issues that raise real-world concerns.
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Many biases exist in the commercial machine learning applications people use every day. Researchers Joy Boolamwini and Timnit Gebru released a 2018 research study that explored how gender and skin type biases exist in commercial artificial intelligence systems. His research team conducted the study after discovering an error in which a facial recognition display could only work with a person with a fair skin tone.
Another source of systemic bias sometimes occurs during data cleaning. A dataset may have its observations classified such that it may not adequately represent the amount of real world features in a statistically significant proportion. Significant differences in observations lead to the condition of an unbalanced dataset, in which the data classes are not represented equally. Training a model on an imbalanced dataset can introduce model drift and produce biased results. The possible scale of imbalanced datasets is wide, ranging from undersampled to oversampled data. For years technologists have warned that few publicly available datasets collect consistently representative data.
As algorithmic models influence operations, executive leaders may bear liability, especially if the public is involved in the outcome. Price poses the risk of imposing a vast system that reinforces institutionalized discriminatory practices.
The George Washington University research team published a study of Chicago rideshare trips and census data. They concluded that fare bias depends on whether the neighborhood of the pick-up point or destination has a high percentage of non-white residents, low-income residents, or high-education residents. This is not the first social bias exploration for commercial services.
In 2016, Bloomberg reported that the algorithm for the Amazon Prime Same Day delivery service meant to suggest neighborhoods in which the "best" recipients live ignored African American neighborhoods in major cities, a long list of economically redistributed communities. Problem. Copying the pattern coming from . Political leaders urged Amazon to adjust its service. The expansion of software and machine learning has increased the demand for training people to correct model inaccuracies, especially when the cost of an error is high.
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IT leaders and managers have a golden opportunity to substantially advance the quality of ML initiatives and the objectives of diversity and inclusion. IT executives can focus on diversity metrics when recruiting for positions related to an organization's machine learning initiatives. This will enhance the organization's accountability for benefit inclusion and diversify the personnel who recommend accountability strategies during the design, development and deployment phases of algorithm-based systems.
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