Discrimination, Bias, Fairness, and Trustworthy AI

Abstract

In this study, we analyze “Discrimination”, ”Bias”, “Fairness”, and “Trustworthiness” as working variables in the context of the social impact of AI. It has been identified that there exists a set of specialized variables, such as security, privacy, responsibility, etc., that are used to operationalize the principles in the Principled AI International Framework. These variables are defined in such a way that they contribute to others of more general scope, for example, the ones studied in this study, in what appears to be a generalization–specialization relationship. Our aim in this study is to comprehend how we can use available notions of bias, discrimination, fairness, and other related variables that will be assured during the software project’s lifecycle (security, privacy, responsibility, etc.) when developing trustworthy algorithmic decision-making systems (ADMS). Bias, discrimination, and fairness are mainly approached with an operational interest by the Principled AI International Framework, so we included sources from outside the framework to complement (from a conceptual standpoint) their study and their relationship with each other.

Regulatory Considerations on the use of Machine Learning based tools in Clinical Trials

“…The requirements of transparency of data and trustworthy AI could support to increase the quality of the data pool limiting some bias, like the selection bias related to representativity of the sample, that consequently will influence also discrimination, and by extension, fairness discussed in the previous section. There is an important link between bias and discrimination, intrinsic to processes such as those of data gathering, data cleaning and data processing [ 32 ]. Furthermore, transparency can contribute to improve the scientific rigor of clinical trials that could be affected by a critical concern know as publication bias, when only positive results of clinical studies are published, generating data that could in turn potentially be used to train algorithms.…”

Human-Centered Design to Address Biases in Artificial Intelligence

“…Biases can exist in each step of the AI life cycle [ 23 ]. Moreover, they can be intertwined and induce a cascading effect on the final AI system performance and its potential biases.…”

Artificial intelligence in farming: Challenges and opportunities for building trust

“…Within ML, techniques have varied in terms of how transparent the models can be. Transparency is an attribute of AI, when it is “clear to an external observer how the system's outcome was produced, and the decisions/predictions/classifications are traceable to the properties involved” (Varona & Suarez, 2022, p. 10). ANNs have been most widely used in remote sensing applications in agriculture.…”

“…Explainability or interpretability refers to the propensity of humans to understand the results of AI algorithms (Slack et al., 2019). Explainability is when the decisions/predictions/classifications produced by the [AI] systems can be justified with an explanation that is easy to be understood by humans while being also meaningful to the end‐user Varona and Suarez (2022) p. 11. Explainability can be used for improving the credibility of the models and giving agency to farmers.…”