Data scientists across the world are currently modelling the spread of coronavirus to make predictions. It’s important that we can all understand these findings and where they came from, use our #DataScienceGuide to get you started. Bit.ly/datascienceguideBit.ly/datascienceguide
We’re headed to Seattle this week to launch our #DataScienceGuide in the US. We’ll be discussing the question – How can we trust data science? To attend RSVP on Eventbrite. eventbrite.co.uk/e/lunchtime…
As discussed in our #DataScienceGuide this article highlights that before AI is used to make decisions it must have undergone real world testing. The fact that algorithms learn from historical data means that unexpected variables are hard to account for ft.com/content/f5bd21da-33b8…
As AI is discussed throughout public life, in hospitals, prisons, and parliamentary offices, it’s important that we can all ask searching questions of data science. Our #DataScienceguide gives you the tools you need. bit.ly/datascienceguide
We see news every day about new uses of algorithms in public life, but are we all equipped to scrutinise their use in decision making? Our #DataScienceGuide breaks down how you can ask searching questions of AI and data science bit.ly/datascienceguide
Our #DataScienceGuide is a guide which helps the public understand ask searching questions of AI and data science, regardless of experience of algorithms or machine learning. A useful starting point for anyone looking to explain their use of data science: bit.ly/datascienceguide
Machine learning models can only ever be based on historical data so their use in hiring processes should raise alarm bells. They are likely to simply perpetuate existing biases as discussed in our #DataScienceGuidebit.ly/datascienceguide… bit.ly/datascienceguide
Society – decision makers, the public, journalists – need to be in a position to judge the quality of data science claims. Our #DataScienceGuide aims to start a public conversation. bit.ly/datascienceguide
Our #DataScienceGuide gives accessible explanations of how algorithms work, and how we can ask searching questions to expose biases and misinterpretations. bit.ly/datascienceguide
It is easy to assume that algorithms are free from human biases, but in fact they can often reinforce them. Read our #DataScienceGuide for the tools to join the discussion… newstatesman.com/science-tec…
Our research & policy coordinator is going to TICTeC Local, a conference on the use of Civic Tech in communities/ local government, to show attendees our #DataScienceGuide. Sign up to the conference at: bit.ly/35qNAbk , and read the guide at: bit.ly/2pj3tjH
Inspiring talk by Tracey Brown from @senseaboutsci on why evidence matters, critical questioning, their #DataScienceGuide for society and public engagement tools.