Atomist or holist? A diagnosis and vision for more productive interdisciplinary AI ethics dialogue.

In response to growing recognition of the social impacts of new artificial intelligence (AI)-based technologies, major AI and machine learning (ML) conferences and journals now encourage or require papers to include ethics impact statements and undergo ethics reviews. This move has sparked heated debate concerning the role of ethics in AI research, at times devolving into name calling and threats of "cancellation." We diagnose this conflict as one between "atomist" and "holist" ideologies.

Adjusting to the GDPR: The Impact on Data Scientists and Behavioral Researchers.

Rapid growth in the availability of behavioral big data (BBD) has outpaced the speed of updates to ethical research codes and regulation of data privacy and human subjects' data collection, storage, and use. The introduction of the European Union's (EU's) General Data Protection Regulation (GDPR) in May 2018 will have far-reaching effects on data scientists and researchers who use BBD, not only in the EU, but around the world. Consequently, many companies are struggling to comply with the Regulation.

Research Dilemmas with Behavioral Big Data.

Behavioral big data (BBD) refers to very large and rich multidimensional data sets on human and social behaviors, actions, and interactions, which have become available to companies, governments, and researchers. A growing number of researchers in social science and management fields acquire and analyze BBD for the purpose of extracting knowledge and scientific discoveries. However, the relationships between the researcher, data, subjects, and research questions differ in the BBD context compared to traditional behavioral data.

Automated time series forecasting for biosurveillance.

For robust detection performance, traditional control chart monitoring for biosurveillance is based on input data free of trends, day-of-week effects, and other systematic behaviour. Time series forecasting methods may be used to remove this behaviour by subtracting forecasts from observations to form residuals for algorithmic input. We describe three forecast methods and compare their predictive accuracy on each of 16 authentic syndromic data streams.

Statistical issues and challenges associated with rapid detection of bio-terrorist attacks.

The traditional focus for detecting outbreaks of an epidemic or bio-terrorist attack has been on the collection and analysis of medical and public health data. Although such data are the most direct indicators of symptoms, they tend to be collected, delivered, and analysed days, weeks, and even months after the outbreak. By the time this information reaches decision makers it is often too late to treat the infected population or to react in some other way.

Early statistical detection of anthrax outbreaks by tracking over-the-counter medication sales.

The recent series of anthrax attacks has reinforced the importance of biosurveillance systems for the timely detection of epidemics. This paper describes a statistical framework for monitoring grocery data to detect a large-scale but localized bioterrorism attack. Our system illustrates the potential of data sources that may be more timely than traditional medical and public health data.