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"The Coalition for Content Provenance and Authenticity (C2PA) addresses the prevalence of misleading information online through the development of technical standards for certifying the source and history (or provenance) of media content. C2PA is a Joint Development Foundation project, formed through an alliance between Adobe, Arm, Intel, Microsoft and Truepic. "

Wearable sensor technology has the potential to transform health care and our understanding of our own bodies and habits. The investigation and testing of these sensors in the commercial sector offer an unprecedented opportunity to leverage biometric data, both to improve individual health through the development of better products and to advance the public good through research. However, research with wearable sensor data must be done in a manner that is respectful of ethical considerations and consumer privacy

Databox project is a new £1.5M EPSRC project led by Dr. Hamed Haddadi (QMUL) in collaboration with Dr. Richard Mortier (University of Cambridge) and Professors Derek McAuley, Tom Rodden, and Andy Crabtree (University of Nottingham) who will explore the development of the Databox as means of enhancing accountability and giving individuals control over the use of their personal data.

"The extraordinary growth of state surveillance of the UK population has been fuelled by political and technological developments in recent decades. But in the name of national security, the state has been eavesdropping on us for far longer than that."

"Julia Powles and Jat Singh from St Johns College Cambridge discuss why the internet of things poses huge risks to online privacy. Julia and Jat discuss their recent article on the issue and why the law needs to catch up with these technological developments."

"Through the design, development and implementation of the Living Room of the Future (LRoTF), we build upon existing work to progress two strands of research. The first explores how media broadcasters may utilise Object-Based Media (OBM) to provide more immersive experiences. Created in conjunction with the BBC R&D the LRofTF utilizes OBM to dynamically customise television content according to audiences' personal, contextual and derived data. OBM works by breaking media into smaller parts or 'objects', describing how they relate to each other semantically, and then reassembling them into personalized programmes. In addition to this media-delivery aspect, the LRoTF explores data protection issues that arise from OBM's use of data by integrating with the privacy-enhancing Databox system. "

The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding. Second, gender neutral words are shown to be linearly separable from gender definition words in the word embedding. Using these properties, we provide a methodology for modifying an embedding to remove gender stereotypes, such as the association between between the words receptionist and female, while maintaining desired associations such as between the words queen and female. We define metrics to quantify both direct and indirect gender biases in embeddings, and develop algorithms to "debias" the embedding. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks. The resulting embeddings can be used in applications without amplifying gender bias.

These principles were developed in conjunction with the 2017 Asilomar conference

"The big companies developing them show no interest in fixing the problem."

The timeline below identifies key developments on tech platforms used by journalism publishers. Here you can explore the significant shifts in the platform landscape as these companies adjust to new relationships with publishers.

BBC development of a personal data store

The standard for public code is a set of criteria that supports public organisations in developing and maintaining software and policy together

"OpenDP is a community effort to build trustworthy, open-source software tools for statistical analysis of sensitive private data. These tools, which we call OpenDP, will offer the rigorous protections of differential privacy for the individuals who may be represented in confidential data and statistically valid methods of analysis for researchers who study the data. "