Batman or the joker?: the powerful urban computing and its ethics issues

The exponential growth of the urban data generated by urban sensors, government reports, and crowd-sourcing services endorses the rapid development of urban computing and spatial data mining technologies. Easier accessibility to such enormous urban data may be a double-bladed sword. On the one hand, urban data can be applied to solve a wide range of practical issues such as urban safety analysis and urban event detection. On the other hand, ethical issues such as biasedly polluted urban data, problematic algorithms, and unprotected privacy may cause moral disaster not only for the research fields but also for the society. This paper seeks to identify ethical vulnerabilities from three primary research directions of urban computing: urban safety analysis, urban transportation analysis, and social media analysis for urban events. Visions for future improvements in the perspective of ethics are addressed.

[1]  Chang-Tien Lu,et al.  TREADS: a safe route recommender using social media mining and text summarization , 2014, SIGSPATIAL/GIS.

[2]  Douglas Heaven,et al.  Why deep-learning AIs are so easy to fool , 2019, Nature.

[3]  Chang-Tien Lu,et al.  Unsupervised Spatial Event Detection in Targeted Domains with Applications to Civil Unrest Modeling , 2014, PloS one.

[4]  Huseyin Polat,et al.  Shilling attacks against recommender systems: a comprehensive survey , 2014, Artificial Intelligence Review.

[5]  Chang-Tien Lu,et al.  Urban Traffic Flow Prediction Using a Spatio-Temporal Random Effects Model , 2016, J. Intell. Transp. Syst..

[6]  Jeffrey T. Hancock,et al.  Experimental evidence of massive-scale emotional contagion through social networks , 2014, Proceedings of the National Academy of Sciences.

[7]  Chang-Tien Lu,et al.  StreetNet: preference learning with convolutional neural network on urban crime perception , 2018, SIGSPATIAL/GIS.

[8]  Liang Zhao,et al.  Multi-resolution Spatial Event Forecasting in Social Media , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

[9]  Andrew D. Selbst,et al.  Big Data's Disparate Impact , 2016 .

[10]  Charles Elkan,et al.  Differential Privacy and Machine Learning: a Survey and Review , 2014, ArXiv.

[11]  Chang-Tien Lu,et al.  CROWDSAFE: crowd sourcing of crime incidents and safe routing on mobile devices , 2011, GIS.

[12]  Chang-Tien Lu,et al.  Determining Relative Airport Threats from News and Social Media , 2017, AAAI.

[13]  Vijayalakshmi Atluri,et al.  Sentiment based Analysis of Tweets during the US Presidential Elections , 2017, DG.O.

[14]  Chang-Tien Lu,et al.  TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction , 2019, SIGSPATIAL/GIS.

[15]  Mehryar Mohri,et al.  Sample Selection Bias Correction Theory , 2008, ALT.

[16]  Carlo Ratti,et al.  Towards Matching User Mobility Traces in Large-Scale Datasets , 2017, IEEE Transactions on Big Data.

[17]  Toon Calders,et al.  Data preprocessing techniques for classification without discrimination , 2011, Knowledge and Information Systems.

[18]  Gang Wang,et al.  Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media , 2017, CIKM.

[19]  Bamshad Mobasher,et al.  Towards Trustworthy Recommender Systems : An Analysis of Attack Models and Algorithm Robustness , 2007 .

[20]  Eleonora D'Andrea,et al.  Real-Time Detection of Traffic From Twitter Stream Analysis , 2015, IEEE Transactions on Intelligent Transportation Systems.

[21]  Justin Cranshaw.  Whose "city of tomorrow" is it?: on urban computing, utopianism, and ethics , 2013, UrbComp '13.

[22]  Chang-Tien Lu,et al.  Steds: Social Media Based Transportation Event Detection with Text Summarization , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[23]  Andrew Guthrie Ferguson,et al.  Policing Predictive Policing , 2016 .

[24]  K. Crawford.  The Hidden Biases in Big Data , 2013 .

[25]  Chang-Tien Lu,et al.  Multi-Task Learning for Transit Service Disruption Detection , 2018, 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[26]  Jon M. Kleinberg,et al.  Challenges in mining social network data: processes, privacy, and paradoxes , 2007, KDD '07.

[27]  Nicu Sebe,et al.  Are Safer Looking Neighborhoods More Lively?: A Multimodal Investigation into Urban Life , 2016, ACM Multimedia.

[28]  Yu Zheng,et al.  Traffic prediction in a bike-sharing system , 2015, SIGSPATIAL/GIS.

[29]  Liang Lin,et al.  Place-centric Visual Urban Perception with Deep Multi-instance Regression , 2017, ACM Multimedia.

[30]  Francesco Bonchi,et al.  Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining , 2016, KDD.

[31]  Yanhui Wu,et al.  Why Does China Allow Freer Social Media? Protests versus Surveillance and Propaganda , 2017 .