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Theoretical, Measured and Subjective Responsibility in Aided Decision Making
When humans interact with intelligent systems, their causal responsibility
for outcomes becomes equivocal. We analyze the descriptive abilities of a newly
developed responsibility quantification model (ResQu) to predict actual human
responsibility and perceptions of responsibility in the interaction with
intelligent systems. In two laboratory experiments, participants performed a
classification task. They were aided by classification systems with different
capabilities. We compared the predicted theoretical responsibility values to
the actual measured responsibility participants took on and to their subjective
rankings of responsibility. The model predictions were strongly correlated with
both measured and subjective responsibility. A bias existed only when
participants with poor classification capabilities relied less-than-optimally
on a system that had superior classification capabilities and assumed
higher-than-optimal responsibility. The study implies that when humans interact
with advanced intelligent systems, with capabilities that greatly exceed their
own, their comparative causal responsibility will be small, even if formally
the human is assigned major roles. Simply putting a human into the loop does
not assure that the human will meaningfully contribute to the outcomes. The
results demonstrate the descriptive value of the ResQu model to predict
behavior and perceptions of responsibility by considering the characteristics
of the human, the intelligent system, the environment and some systematic
behavioral biases. The ResQu model is a new quantitative method that can be
used in system design and can guide policy and legal decisions regarding human
responsibility in events involving intelligent systems
The Responsibility Quantification (ResQu) Model of Human Interaction with Automation
Intelligent systems and advanced automation are involved in information
collection and evaluation, in decision-making and in the implementation of
chosen actions. In such systems, human responsibility becomes equivocal.
Understanding human casual responsibility is particularly important when
intelligent autonomous systems can harm people, as with autonomous vehicles or,
most notably, with autonomous weapon systems (AWS). Using Information Theory,
we develop a responsibility quantification (ResQu) model of human involvement
in intelligent automated systems and demonstrate its applications on decisions
regarding AWS. The analysis reveals that human comparative responsibility to
outcomes is often low, even when major functions are allocated to the human.
Thus, broadly stated policies of keeping humans in the loop and having
meaningful human control are misleading and cannot truly direct decisions on
how to involve humans in intelligent systems and advanced automation. The
current model is an initial step in the complex goal to create a comprehensive
responsibility model, that will enable quantification of human causal
responsibility. It assumes stationarity, full knowledge regarding the
characteristic of the human and automation and ignores temporal aspects.
Despite these limitations, it can aid in the analysis of systems designs
alternatives and policy decisions regarding human responsibility in intelligent
systems and advanced automation
A Markov Chain Model Checker
Markov chains are widely used in the context of performance and reliability evaluation of systems of various nature. Model checking of such chains with respect to a given (branching) temporal logic formula has been proposed for both the discrete [17,6] and the continuous time setting [4,8]. In this paper, we describe a prototype model checker for discrete and continuous-time Markov chains, the Erlangen Twente Markov Chain Checker ), where properties are expressed in appropriate extensions of CTL. We illustrate the general bene ts of this approach and discuss the structure of the tool. Furthermore we report on first successful applications of the tool to non-trivial examples, highlighting lessons learned during development and application of )
Asymmetric effects of false positive and false negative indications on the verification of alerts in different risk conditions
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG gefƶrderten) Allianz- bzw. Nationallizenz frei zugƤnglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Indications from alerts or alarm systems can be the trigger for decisions, or they can elicit further information search. We report an experiment on the tendency to collect additional information after receiving system indications. We varied the proclivity of the alarm system towards false positive or false negative indications and the perceived risk of the situation. Results showed that false alarm-prone systems led to more frequent re-checking following both alarms and non-alarms in the high risk condition, whereas miss-prone systems led to high re-checking rates only for non-alarms, representing an asymmetry effect. Increasing the risk led to more re-checks with all alarm systems, but it had a stronger impact in the false alarm-prone condition. Results regarding the relation of risk and the asymmetry effect of false negative and false positive indications are discussed
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