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Related Experiment Video

Updated: Nov 20, 2025

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

Mediating artificial intelligence developments through negative and positive incentives.

The Anh Han¹, Luís Moniz Pereira², Tom Lenaerts^3,4

¹School of Computing and Digital Technologies, Teesside University, Middlesbrough, United Kingdom.

|January 26, 2021

Summary

This summary is machine-generated.

In technology races, rewards for safety compliance can accelerate development and ensure safe choices, unlike punishments which may stifle innovation. This research offers insights into effective regulatory strategies for artificial intelligence (AI) and other fast-evolving fields.

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Last Updated: Nov 20, 2025

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

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Construction of an Improved Multi-Tetrode Hyperdrive for Large-Scale Neural Recording in Behaving Rats

Published on: May 9, 2018

Area of Science:

Technological innovation
Behavioral economics
AI safety

Background:

The rapid advancement of Artificial Intelligence (AI) has generated significant societal anxiety and a competitive
AI race
narrative, potentially leading to compromised safety measures and overlooked consequences.
Existing models of technology races often overlook the nuanced impact of incentives on safety compliance and development speed.

Purpose of the Study:

To investigate the effects of positive (rewards) and negative (punishments) incentives on outcomes in technology races, specifically concerning AI development.
To analyze conditions under which punishments may hinder innovation or reduce development speed, and how rewards can promote safety without over-regulation.

Main Methods:

Development of a baseline model for technology races with significant benefits for winners.
Analysis of scenarios involving reward and punishment systems to influence participant behavior regarding safety precautions.
Comparative evaluation of the impact of rewards versus punishments on development speed and innovation.

Main Results:

Punishments can reduce the development speed of unsafe participants but may also lead to over-regulation and reduced innovation.
Rewards for adhering to safety measures can increase development speed while ensuring safer outcomes.
Reward systems avoid the over-regulation issues associated with punishment mechanisms.

Conclusions:

Incentive structures significantly influence safety compliance in technology races.
Rewards are a more effective mechanism than punishments for promoting safe AI development and innovation.
Findings offer guidance for regulatory actions to enhance safety compliance during technological shifts.