An ethical decision-making framework with serious gaming: a smart water case study on flooding

Sensors and control technologies are being deployed extensively in both urban water networks and rural river systems, leading to unprecedented ability to sense and control our water environment. Because these sensor networks and control systems 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, due to growth in system complexity, humans will continue to cede direct decision-making powers to decision-support technologies such as data algorithms. Systems will have ever-greater potential to effect human lives and yet humans will be insulated from direct decisions. Combined, these trends present a challenge for water resources management decision support tools to incorporate concepts ethical and normative expectations. Towards this end, 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 schemes. We then demonstrate the framework with a proof-of-concept use case where decision models are learned and deployed to respond to flooding scenarios. Results indicate the framework can capture group “wisdom” in learned models and use this to make decisions. We share our generalized framework and its cyber components openly with the research community.

[1]  Ibrahim Demir,et al.  An integrated web framework for HAZUS-MH flood loss estimation analysis , 2019, Natural Hazards.

[2]  Derek D. Rucker,et al.  Journal of Personality and Social Psychology Social Class , Power , and Selfishness : When and Why Upper and Lower Class Individuals Behave Unethically , 2015 .

[3]  Bekir Z. Demiray,et al.  D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks , 2021, SN Comput. Sci..

[4]  Ibrahim Demir,et al.  Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma , 2019, Int. J. Digit. Earth.

[5]  A. C. Grayling,et al.  Philosophy: a Guide Through the Subject , 1995 .

[6]  Ibrahim Demir,et al.  Serious gaming for participatory planning of multi-hazard mitigation , 2018, International Journal of River Basin Management.

[7]  R. Zeckhauser,et al.  Social Class and Un(ethical) Behavior: A Framework, with Evidence from a Large Population Sample , 2013 .

[8]  Aaron Poresky,et al.  Smarter Stormwater Systems. , 2016, Environmental science & technology.

[9]  Ibrahim Demir,et al.  An intelligent system on knowledge generation and communication about flooding , 2018, Environ. Model. Softw..

[10]  Anthony J. Parolari,et al.  Improved reliability of stormwater detention basin performance through water quality data-informed real-time control , 2018, Journal of Hydrology.

[11]  James H. Moor,et al.  The Nature, Importance, and Difficulty of Machine Ethics , 2006, IEEE Intelligent Systems.

[12]  Vincent Conitzer,et al.  Moral Decision Making Frameworks for Artificial Intelligence , 2017, ISAIM.

[13]  Stephen Vaisey,et al.  The New Sociology of Morality , 2013 .

[14]  W. Krajewski,et al.  The Iowa Watersheds Project: Iowa's prototype for engaging communities and professionals in watershed hazard mitigation , 2018 .

[15]  Ariel D. Procaccia,et al.  WeBuildAI , 2019, Proc. ACM Hum. Comput. Interact..

[16]  Gervase Vernon,et al.  Virtue ethics. , 2003, The British journal of general practice : the journal of the Royal College of General Practitioners.

[17]  Jun Yan,et al.  A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning , 2020, Water Resources Research.

[18]  Gabriela Cembrano,et al.  An MPC-Enabled SWMM Implementation of the Astlingen RTC Benchmarking Network , 2020 .

[19]  Gabriel Abend,et al.  Thick Concepts and the Moral Brain , 2011, European Journal of Sociology.

[20]  M. B. Abbott,et al.  Flood Risk and Social Justice: From Quantitative to Qualitative Flood Risk Assessment and Mitigation , 2012 .

[21]  Iyad Rahwan,et al.  Society-in-the-loop: programming the algorithmic social contract , 2017, Ethics and Information Technology.

[22]  J. Thomson The Trolley Problem , 1985 .

[23]  P. Vanrolleghem,et al.  Ecohydraulic-driven real-time control of stormwater basins , 2014 .

[24]  Branko Kerkez,et al.  Balancing water quality and flows in combined sewer systems using real-time control , 2020 .

[25]  J. Henrich,et al.  The Moral Machine experiment , 2018, Nature.

[26]  K. Binmore Egalitarianism versus Utilitarianism , 1998, Utilitas.

[27]  Peter Steen Mikkelsen,et al.  Controlling sewer systems – a critical review based on systems in three EU cities , 2017 .

[28]  Stéphane Côté,et al.  Higher social class predicts increased unethical behavior , 2012, Proceedings of the National Academy of Sciences.

[29]  Marian Muste,et al.  A web-based decision support system for collaborative mitigation of multiple water-related hazards using serious gaming. , 2019, Journal of environmental management.

[30]  A. Samuel Some Moral and Technical Consequences of Automation—A Refutation , 1960, Science.

[31]  Yusuf Sermet,et al.  Crowdsourced approaches for stage measurements at ungauged locations using smartphones , 2020, Hydrological Sciences Journal.

[32]  Vincent Conitzer,et al.  Crowdsourcing Societal Tradeoffs , 2015, AAMAS.

[33]  Anna Jobin,et al.  The global landscape of AI ethics guidelines , 2019, Nature Machine Intelligence.

[34]  Michael Pabst,et al.  Astlingen - a benchmark for real time control (RTC). , 2018, Water science and technology : a journal of the International Association on Water Pollution Research.

[35]  A. Bykov Rediscovering the Moral: The ‘Old’ and ‘New’ Sociology of Morality in the Context of the Behavioural Sciences , 2018, Sociology.

[36]  Muhammed Sit,et al.  D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks , 2020, SN Computer Science.

[37]  Marcello Ienca,et al.  Artificial Intelligence: the global landscape of ethics guidelines , 2019, ArXiv.

[38]  Ibrahim Demir,et al.  Towards an information centric flood ontology for information management and communication , 2019, Earth Science Informatics.

[39]  Mohamed M. Morsy,et al.  Leveraging open source software and parallel computing for model predictive control of urban drainage systems using EPA-SWMM5 , 2019, Environ. Model. Softw..

[40]  Ibrahim Demir,et al.  FLOODSS: Iowa flood information system as a generalized flood cyberinfrastructure , 2018 .

[41]  A. Smajdor,et al.  Consequentialism , 2021, Oxford Handbook of Medical Ethics and Law.

[42]  Yusuf Sermet,et al.  A serious gaming framework for decision support on hydrological hazards. , 2020, The Science of the total environment.

[43]  Abhiram Mullapudi,et al.  Deep reinforcement learning for the real time control of stormwater systems , 2020 .

[44]  Abhiram Mullapudi,et al.  Emerging investigators series: building a theory for smart stormwater systems , 2017 .

[45]  Michael W. Kraus,et al.  Class and compassion: socioeconomic factors predict responses to suffering. , 2012, Emotion.

[46]  K. Schilling,et al.  Iowa Statewide Stream Nitrate Load Calculated Using In Situ Sensor Network , 2018 .

[47]  Dong-Jun Seo Integrated Sensing and Prediction of Flash Floods for the Dallas-Fort Worth Metroplex (DFW) , 2017 .

[48]  Abhiram Mullapudi,et al.  Shaping Streamflow Using a Real-Time Stormwater Control Network , 2018, Sensors.

[49]  D. Krebs Morality: An Evolutionary Account , 2008, Perspectives on psychological science : a journal of the Association for Psychological Science.

[50]  J. Haidt The New Synthesis in Moral Psychology , 2007, Science.