|11 Jan 2023||16:30||17:30|
Governments and big corporations are, in many cases, abusing the use of machine learning. But citizen organisations and journalists are finding creative ways of making the most of machine learning to conduct investigations in the public interest. The problem of abundance of available information has been met with the use of machines to process and analyse data faster than ever. This has opened the door to a plethora of methods and techniques to investigate our world — from identifying the traces of mining in the Amazon among thousands of satellite images, to analysing geospatial information to predict the occurrence of serious human rights violations.
But how are these techniques applied? How to replicate them in different contexts? And what ethical problems are there while using these technologies?
The speakers at the Cambridge University Social Data School’s Public Event will try to respond to these questions, in a horizontal space where attendees are invited to interact and engage with them:
Hyury Potter from the Amazon Mining Watch (The Rainforest Initiative/Pulitzer Center):
Amazon Mining Watch uses machine learning to map the scars of mining activities in the Amazonian countries. By constantly analysing high-resolution and historical satellite images, this tool aims at identifying the fast-paced growth of open-pit mining in the largest rainforest in the world. This database helps journalists, activists, and researchers better understand the causes and impacts of the mining industry.
Jorge Ruiz Reyes, Oxford Internet Institute and Data Civica:
Data Civica, CentroGeo, the Universidad Iberoamericana and Elementa DDHH, with the support of Amnesty International, trained data to develop a spatial analysis model that delimits areas in Mexico where it is likely that new clandestine graves will be located. This new approach includes three combined methods: Point pattern analysis, accessibility combined with visibility analysis, and hyperspectral analysis.
Advancing Cambridge University Social Data School’s aim of making digital research methods accessible, this event is open to any researcher interested in how social data is collected and analysed, from journalists, civil servants, NGOs, unions, and others.
This event is the Public Event of our ongoing online Social Data School (9-13 January). If you are interested in joining our next Data School, find more information here: www.cdh.cam.ac.uk/dataschools