Transcribing can be a pain, and although recent progress in speech recognition software has helped, it remains a challenge. Speech recognition programs, do, however, raise ethical/consent issues: what if person-identifiable interview data is transcribed or read by someone who was not given the consent to do so? Furthermore, some conversational elements aren’t transcribed well by pattern recognition programs.
Most institutions see the market as the only legitimate form of organization, but different visions towards public policy, some involving artificial intelligence, have been the subject of consideration from academics and politicians alike. Under what circumstances, and to what extent, could artificial intelligence replace the market as the end-all guiding force in crafting reasonable public policy? Brexit may play a leading role in the transition.
Today, and into the future, consulting archival documents increasingly means reading them on a screen. This brings with it opportunity — imagine being able to search for keywords across millions of documents, leading to radically faster search times — but also challenge, as the number of electronic documents increases exponentially.
The future of the academic monograph has been questioned for over two decades. At the heart of this ‘monograph crisis’ has been a publishing industry centred on the print publication of monographs and a failure and lack of incentives to develop business models that would support a transition to open digital monographs. In this post Mike Taylor argues that if monographs are to be appropriately valued, there is a pressing need to further integrate monographs into the digital infrastructure of scholarly communication. Failing this, the difficulty in tracking the usage and discovery of monographs online, will likely make the case for justifying further investment in monographs harder.
More textual data than ever before are available to computational social scientists—be it in the form of digitized books, communication traces on social media platforms, or digital scientific articles. Researchers in academia and industry increasingly use text data to understand human behavior and to measure patterns in language.
For centuries, being a scientist has meant learning to live with limited data. People only share so much on a survey form. But, at least in the area of text analysis (AKA content analysis, or natural language processing), the old limits are crumbling
Creators and participants in the Evidence Synthesis Hackathon ask what’s the solution to coping with the increasing volume of evidence needed to build effective, solid policy? They argue that technology is the key. With accessible software tools and workflows, machines can be left to do the laborious work so that people can focus on planning, thinking and doing.
Burned out by the hamster-wheel of academe and the regime of metrics, John Postill decided the tonic would be to write a spoof spy thriller about a Spanish nerd with a silly name who moves to London in 1994 and accidentally foils a terrorist plot by an evil anthropologist.
An article in the journal Psychological Science, “The New Statistics: Why and How” by La Trobe University’s Geoff Cumming, has proved remarkably popular in the years since and is the third-most cited paper published in a Sage journal in 2013. The idea of an autonomous vehicle – i.e., a self-driving car – isn’t particularly new. Leonardo da Vinci had some ideas he […] Social sciences can also inform the design and creation of ethical frameworks and guidelines for AI development and for deployment into systems. Social scientists can contribute expertise: on data quality, equity, and reliability; on how bias manifests in AI algorithms and decision-making processes; on how AI technologies impact marginalized communities and exacerbate existing inequities; and on topics such as fairness, transparency, privacy, and accountability. If schools provide the proper support and resources, they will help educators move from anxiety to empowerment when integrating AI into the classroom. An article in the journal Psychological Science, “The New Statistics: Why and How” by La Trobe University’s Geoff Cumming, has proved remarkably popular in the years since and is the third-most cited paper published in a Sage journal in 2013. By carefully interrogating the system of economic incentives underlying innovations and how technologies are monetized in practice, we can generate a better understanding of the risks, both economic and technological, nurtured by a market’s structure.
EXPLORE
Impact
Paper Opening Science to the New Statistics Proves Its Import a Decade Later
READ MORE
A Milestone Dataset on the Road to Self-Driving Cars Proves Highly Popular
READ MORE
Why Social Science? Because It Can Help Contribute to AI That Benefits Society
READ MORE
Innovation
AI Upskilling Can and Should Empower Business School Faculty
READ MORE
Paper Opening Science to the New Statistics Proves Its Import a Decade Later
READ MORE
To Better Forecast AI, We Need to Learn Where Its Money Is Pointing
READ MORE
