Research Topic

Ethical Machine Learning and Artificial Intelligence (AI)

About this Research Topic

Machine learning algorithms have more and more impact on and in our day-to-day lives. Typical algorithmic assessment methods, used for predicting human outcomes such as recruitment, bail decisions, mortgage approvals, and insurance premiums, among many others, are currently being trialled and subsequently deployed. The time has therefore arrived that the most important area in machine learning is the implementation of algorithms that adhere to ethical and legal requirements. For example, the United States’ Fair Credit Reporting Act and European Union’s General Data Protection Regulation (GDPR) prescribe that data must be processed in a way that is fair/unbiased. GDPR also alludes to the right of an individual to receive an explanation about decisions made by an automated system.

Addressing how human traits such as fairness, accountability, transparency, and trustworthiness can be built into future AI and ML systems has attracted a lot of attention, as it will ensure the continued confidence of the general public in the deployment of automated systems. This Research Topic covers but is not limited to the below fields:

· fairness;
· accountability;
· transparency;
· trustworthiness,

and cover methods such as:
· causality and counterfactual reasonings;
· adversarial learning;
· reinforcement learning;
· probabilistic programs.


Keywords: ethics, AI, artificial intelligence, machine learning, fairness, accountability, transparency, trustworthiness, reinforcement learning, adversarial learning, GDPR


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Machine learning algorithms have more and more impact on and in our day-to-day lives. Typical algorithmic assessment methods, used for predicting human outcomes such as recruitment, bail decisions, mortgage approvals, and insurance premiums, among many others, are currently being trialled and subsequently deployed. The time has therefore arrived that the most important area in machine learning is the implementation of algorithms that adhere to ethical and legal requirements. For example, the United States’ Fair Credit Reporting Act and European Union’s General Data Protection Regulation (GDPR) prescribe that data must be processed in a way that is fair/unbiased. GDPR also alludes to the right of an individual to receive an explanation about decisions made by an automated system.

Addressing how human traits such as fairness, accountability, transparency, and trustworthiness can be built into future AI and ML systems has attracted a lot of attention, as it will ensure the continued confidence of the general public in the deployment of automated systems. This Research Topic covers but is not limited to the below fields:

· fairness;
· accountability;
· transparency;
· trustworthiness,

and cover methods such as:
· causality and counterfactual reasonings;
· adversarial learning;
· reinforcement learning;
· probabilistic programs.


Keywords: ethics, AI, artificial intelligence, machine learning, fairness, accountability, transparency, trustworthiness, reinforcement learning, adversarial learning, GDPR


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

13 January 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

13 January 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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