Our #BigDataEthics workshop keynote speaker today is Michael Zimmer an associate professor in the Department of Computer Science at Marquette University. @michaelzimmer @MarquetteCS @MarquetteU @pervade_team

I usually start with this article from @WIRED on Big Data & The End of Theory: https://www.wired.com/2008/06/pb-theory/

The patters and relationships contained within big data inherently produce meaningful and insightful knowledge about complex phenomena. #BigDataAnalytics #BigDataEthics

There is something different happening today. The ability to collect, process and explore #bigdata has exploded: social media and network analysis, predictive analytics, recommendation systems and algorithmic filtering, etc.

"With an increasing amount of data on every aspect of our daily activities...we are able to measure human behavior with precision largely though impossible just a decade ago...using large-scale demographic, behavioral and network data to investigate human activity & relationships

Recent research cases present novel approaches to understanding important social phenomena through big data. But each also generated ethical controversy about their collection and use of big data. #BigDataAnalytics #BigDataEthics

What makes big data hard? 1/The data are easy to get. Gone are the times when big data needed a major lab and thousands and thousands of dollars, run by tenure professors with plenty of ethical training (and plenty at stake)...

What makes big data hard? 2/The data are pervasive. More than just "big data" that's rich, deep, often identifiable data about people available for computational research. The data are about people's lives and activities, often collected, aggregated, exchanged, and mined.

Emergence of new tech leads to conceptual gaps in how we think about ethical problems, and how we address them. Computer tech transforms "many of our human activities and social institutions," and will "Leave us with policy and conceptual vacuums about how to use computer tech"

New conceptual gaps in our ethical understanding of: Privacy and anonymity, informed consent, human subjects and harm

Conceptual Gap: Privacy - Presumption that because subjects make info available online, they don't have an expectation of privacy.

New ethical considerations: privacy is often contextual; fails to recognize that old paradigm of public/private doesn't apply in today's networked-data society; need to track if architecture has changed, or if users even understand what is available to researchers.

Conceptual Gap: Consent -- Presumption that because something is shared online or available without a password, the subject is consenting to it being harvested for research.

New ethical considerations: Must recognize that a user making something public online comes with a set of assumptions/expectations about who can access and how; must recognize how research methods might allow un-anticipated access to presumed "restricted" data

Conceptual Gap: Human Subjects -- Big data researchers often interact only with datasets, objects, or avatars, thus feel a conceptual distance from an actual human. Might not consider what they do as "human subject" research.

New ethical considerations: Must bridge this (artificial) distance between research and the human subject; must also consider stakeholders.

Conceptual Gap: Harm -- presumption that "harm" means risk of physical or other tangible impact on subject.

New ethical considerations: Must move beyond concept of harm as requiring a tangible consequence; consider dignity/autonomy theories of harm

Understanding harms: persistent challenge among big data researchers to understand harms to vulnerable communities is a serious blind spot.

Understanding harms: Need to ensure our students receive proper training so that they are aware of their implicit biases and positionality, and increase attentiveness to harms felt by those perhaps less visible to them.

Where do we go from here? Multiple ways to engage pedagogically to help address challenges to big data ethics: updates for IRB/RCR training, deliver engaging tutorials/workshops; better embedding ethics in curricula, require an ethics class; add data literacy/data ethics courses

Where do we go from here? Lots for us to do not just within universities but also in industry and policy -- work with industry partners to study if/how ethical deliberation is part of research; work with HHS