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Articles 1 - 5 of 5
Full-Text Articles in Legal Studies
Commonsense Knowledge In Sentiment Analysis Of Ordinance Reactions For Smart Governance, Manish Puri
Commonsense Knowledge In Sentiment Analysis Of Ordinance Reactions For Smart Governance, Manish Puri
Theses, Dissertations and Culminating Projects
Smart Governance is an emerging research area which has attracted scientific as well as policy interests, and aims to improve collaboration between government and citizens, as well as other stakeholders. Our project aims to enable lawmakers to incorporate data driven decision making in enacting ordinances. Our first objective is to create a mechanism for mapping ordinances (local laws) and tweets to Smart City Characteristics (SCC). The use of SCC has allowed us to create a mapping between a huge number of ordinances and tweets, and the use of Commonsense Knowledge (CSK) has allowed us to utilize human judgment in mapping. …
Data Mining And The Challenges Of Protecting Employee Privacy Under U.S. Law, Pauline Kim
Data Mining And The Challenges Of Protecting Employee Privacy Under U.S. Law, Pauline Kim
Scholarship@WashULaw
Concerns about employee privacy have intensified with the introduction of data mining tools in the workplace. Employers can now readily access detailed data about workers’ online behavior or social media activities, purchase background information from data brokers, and collect additional data from workplace surveillance tools. When data mining techniques are applied to this wealth of data, it is possible to infer additional information about employees beyond the information that is collected directly. As a consequence, these tools can alter the meaning and significance of personal information depending upon what other information it is aggregated with and how the larger dataset …
Data-Driven Discrimination At Work, Pauline Kim
Data-Driven Discrimination At Work, Pauline Kim
Scholarship@WashULaw
A data revolution is transforming the workplace. Employers are increasingly relying on algorithms to decide who gets interviewed, hired, or promoted. Although data algorithms can help to avoid biased human decision-making, they also risk introducing new sources of bias. Algorithms built on inaccurate, biased, or unrepresentative data can produce outcomes biased along lines of race, sex, or other protected characteristics. Data mining techniques may cause employment decisions to be based on correlations rather than causal relationships; they may obscure the basis on which employment decisions are made; and they may further exacerbate inequality because error detection is limited and feedback …
Reeling In Big Phish With A Deep Md5 Net, Brad Wardman, Gary Warner, Heather Mccalley, Sarah Turner, Anthony Skjellum
Reeling In Big Phish With A Deep Md5 Net, Brad Wardman, Gary Warner, Heather Mccalley, Sarah Turner, Anthony Skjellum
Journal of Digital Forensics, Security and Law
Phishing continues to grow as phishers discover new exploits and attack vectors for hosting malicious content; the traditional response using takedowns and blacklists does not appear to impede phishers significantly. A handful of law enforcement projects — for example the FBI's Digital PhishNet and the Internet Crime and Complaint Center (ic3.gov) — have demonstrated that they can collect phishing data in substantial volumes, but these collections have not yet resulted in a significant decline in criminal phishing activity. In this paper, a new system is demonstrated for prioritizing investigative resources to help reduce the time and effort expended examining this …
Data Mining Techniques In Fraud Detection, Rekha Bhowmik
Data Mining Techniques In Fraud Detection, Rekha Bhowmik
Journal of Digital Forensics, Security and Law
The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision treebased algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models.