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25-Aug-2019 23:08 by 7 Comments

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AI agents can also demonstrate novel behaviours through their interaction with the world and other agents that are impossible to predict with precision.

Commentators and scholars from diverse fields—including, but not limited to, cognitive systems engineering, human computer interaction, human factors, science, technology and society, and safety engineering—are raising the alarm about the broad, unintended consequences of AI agents that can exhibit behaviours and produce downstream societal effects—both positive and negative—that are unanticipated by their creators and others studying the implications in fairness, accountability and transparency (for example, the ACM conference on fairness, accountability and transparency (https://fatconference.org/)), many questions remain.This field overlaps with, but is distinct from, computer science and robotics. This is akin to how ethology and behavioural ecology study animal behaviour by integrating physiology and biochemistry—intrinsic properties—with the study of ecology and evolution—properties shaped by the environment.Animal and human behaviours cannot be fully understood without the study of the contexts in which behaviours occur.Further complicating this challenge is the fact that much of the source code and model structure for the most frequently used algorithms in society is proprietary, as are the data on which these systems are trained.Industrial secrecy and legal protection of intellectual property often surround source code and model structure.This Review frames and surveys the emerging interdisciplinary field of machine behaviour: the scientific study of behaviour exhibited by intelligent machines.

Here we outline the key research themes, questions and landmark research studies that exemplify this field.

For example, some AI agents are designed to aid learning outcomes for children.

These AI systems may benefit their intended humans by nudging those humans into better learning or safer mobility behaviours.

As these scientists create agents to solve particular tasks, they often focus on ensuring the agents fulfil their intended function (although these respective fields are much broader than the specific examples listed here).

For example, AI agents should meet a benchmark of accuracy in document classification, facial recognition or visual object detection.

We start by providing background on the study of machine behaviour and the necessarily interdisciplinary nature of this science.

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