Ethical decision-making is difficult, certainly for robots let alone humans. If a robot's ethical decision-making process is going to be designed based on some approximation of how humans operate, then the assumption is that a good model of how humans make an ethical choice is readily available. Yet no single ethical framework seems sufficient to capture the diversity of human ethical decision making. Our work seeks to develop the computational underpinnings that will allow a robot to use multiple ethical frameworks that guide it towards doing the right thing. As a step towards this goal, we have collected data investigating how regular adults and ethics experts approach ethical decisions related to the use in a healthcare and game playing scenario. The decisions made by the former group is intended to represent an approximation of a folk morality approach to these dilemmas. On the other hand, experts were asked to judge what decision would result if a person was using one of several different types of ethical frameworks. The resulting data may reveal which features of the pill sorting and game playing scenarios contribute to similarities and differences between expert and non-expert responses. This type of approach to programming a robot may one day be able to rely on specific features of an interaction to determine which ethical framework to use in the robot's decision making. 
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                            An ethical decision-making framework with serious gaming: a smart water case study on flooding
                        
                    
    
            Abstract Sensors and control technologies are being deployed at unprecedented levels in both urban and rural water environments. Because sensor networks and control allow for higher-resolution monitoring and decision making in both time and space, greater discretization of control will allow for an unprecedented precision of impacts, both positive and negative. Likewise, humans will continue to cede direct decision-making powers to decision-support technologies, e.g. data algorithms. Systems will have ever-greater potential to effect human lives, and yet, humans will be distanced from decisions. Combined these trends challenge water resources management decision-support tools to incorporate the concepts of ethical and normative expectations. Toward this aim, we propose the Water Ethics Web Engine (WE)2, an integrated and generalized web framework to incorporate voting-based ethical and normative preferences into water resources decision support. We demonstrate this framework with a ‘proof-of-concept’ use case where decision models are learned and deployed to respond to flooding scenarios. Findings indicate that the framework can capture group ‘wisdom’ within learned models to use in decision making. The methodology and ‘proof-of-concept’ system presented here are a step toward building a framework to engage people with algorithmic decision making in cases where ethical preferences are considered. We share our framework and its cyber components openly with the research community. 
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                            - Award ID(s):
- 1633098
- PAR ID:
- 10290893
- Date Published:
- Journal Name:
- Journal of Hydroinformatics
- Volume:
- 23
- Issue:
- 3
- ISSN:
- 1464-7141
- Page Range / eLocation ID:
- 466 to 482
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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