In recent years, there is a surge of interest in Artificial Intelligence (AI) algorithms and applications. This interest is largely due to the proliferation of the data points that are available for building AI systems, as well as due to the unprecedented growth in the available computing and storage capacities of the systems that manage and analyze these data. Therefore, the number of AI deployments is increasing at a rapid pace. Nevertheless, recent experiences with the development and deployment of AI systems show that AI success is not only a matter of developing advanced technology and finding effective business models. It turns out that the ever-important human factors play a decisive role in the success of AI deployment. Future AI systems must exhibit human-centric properties such as transparency, trustworthiness and explainability. These properties will ensure that humans understand how AI systems operate in the scope of a specific application context. As such they will be a foundation for ensuring that humans trust the operation of AI systems and are willing to adopt and use them at scale.
Human-centric AI applications must be unbiased i.e., they must operate in an objective and fair way, which leads to inclusive applications and leave no citizen behind. For instance, AI systems must not favour any user group over another and must avoid taking decisions that cannot be adequately justified to humans. This is a challenging data science problem given that bias is a very common issue in the development of AI systems. Bias can be caused by a variety of factors such as the lack of representative data or the repurposing of an AI system for use in an application context different than the context where the system was trained at the first place. Human intelligence suffers from numerous types of bias such as the well-known “placebo” bias, the choice supportive bias and other forms of cognitive biases. Artificial Intelligence systems are no different than humans in this respect. When trained with biased data or in non-representative contexts, they are bound to lead to subjective choices and decisions.
One of the most common problems with AI bias is that it is in most cases unintended. This means that many data scientists and ai experts build biased systems without understanding their problems and the implications of their use. In principle biased systems can be classified into two very broad categories:
In this context, biased systems are unintentionally created in one or more of the following ways:
The above list of biased is non-exhaustive, yet it provides a good starting point for understanding the problem of unintended biases in AI. Following the understanding of the biases, enterprises and their ai experts had better take some the following actions:
Overall, when developing advanced ai technology, there is no point in ignoring human factors and trustworthiness aspects. Bias detection and removal are among the most important development steps of a human-centric AI system. Modern enterprises must therefore look for potential biases in ai systems towards ensuring their fair and objective operation. Moreover, they must comply with the emerging regulatory environment for AI systems and applications.
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