AI is being used in myriad ways to enable social good but how does the [hopefully the government as well] charity sector - not traditionally at the sharp end of technology - ensure it doesn’t go awry, especially when charitable organisations serve some of the most vulnerable people, and public trust and goodwill are particularly important to their core mission.

How easy is it, really, to achieve social good with AI? Is the data available to support the effort? Are there enough experts who understand not just algorithms but the environments they’re working in? How do you even begin to check your project for ethical risks. And does the cost of managing an ongoing project outweigh its benefits. How do smaller charities cope?

Data, people and ethics - what’s the biggest hurdle?

In this episode we’ll hear from:

Julie Dodd, Director of Digital Transformation and Communication @ Parkinsons UK:
Julie is Parkinsons UK’s director of digital transformation and communication. She’s also written The New Reality, a study into how non-profit organisations can approach digital transformation and use digital technology for change. Among other things, Parkinsons is currently working with BenevolentAI to try and identify at least three currently available medicines that can be repurposed to address the condition and two brand-new ways to treat it with new drugs.

Giselle Cory, Executive Director @ DataKind UK:
DataKind is an organisation that is itself a charity whose volunteers support other charities and social enterprises, providing data science expertise and services such as exploratory analysis and prototyping. Projects they’ve worked on include helping homeless organisations identify issues people sought advice on before becoming homeless and analysing possible reasons why people progress differently in a shelter. It’s helped Global Witness uncover potential corruption with open data and the Citizens Advice Bureau better identify emerging social issues to support their staff training.

Michelle Eaton, Programme Manager in the Government Innovation Team - Data @ Nesta:
Michelle worked with the Essex Police Force for 11 years - starting in community policing before moving on to performance analysis, and then to strategic change, where she led the Essex Centre for Data Analytics. Now she oversees Nesta’s Offices of Data Analytics programme, which helps governments reform their services, and recently wrote a blog defending the use of AI in policing.

BBC Machine Learning Podcast: Machine Learning for Social Good

In the world where every organisation feels urgency to consider machine learning to leverage the value from data, the practitioners are still divided. Is the commitment strong enough and can we bring real value and deliver upon the promise?

This time, we will explore the ingredients of successful implementation of machine learning programmes across the private and public sector. We will discuss why, when and how to get started. 

Among our contributors we have:

Simon Raper is an RSS accredited statistician with over 15 years’ experience working in data science and many more in coding and software development. In 2014 he founded Coppelia Machine Learning and Analytics and has clients such as Sainsbury's, Direct Line Group, The Telegraph, Black Swan, Citizen's Advice and Net-a-Porter. Previously he ran data teams at Channel 4, News UK and Mindshare where he worked with McDonalds, Unilever and Jaguar. He writes articles on Applied Statistics for Significance magazine and his blog on machine learning and statistics has attracted over half a million visits.

Giles Pavey is Head of Data Strategy at the Department for Work and Pensions. Previously he was Chief Data Scientist at dunnhumby Ltd, leading analytics on Tesco ClubCard data.
Giles is Honorary Professor of Computer Science at UCL where he lectures and supervises research. He is also visiting Fellow in Industrial Mathematics at Oxford

Dr. Shahzia Holtom is currently leading the data science team at Pivotal Labs which is a Silicon Valley based company. Her experience lies in putting data science in to production through extreme programming and agile practices. Shahzia has previously worked as one of the first data scientist in the UK Government where she championed the use of data science for evidence based policy making.
Shahzia holds a PhD in Statistics from the University of Oxford and has also trained as a Software Engineer.

BBC Machine Learning Podcast: Getting Started

Predictive machines meet human. Can human centric creative process be improved by predictions?

We can’t possibly exhaust this topic in one podcast…! But it feels like we need to start somewhere. We already established that it is unlikely robots will take us over in creative production. Not soon anyway. However predictive machines are making their way into newsrooms and studios. Sometimes to make it cheaper, sometimes to make it better. Either way it turns out to be a spectacularly complicated process to create this human plus machine dream team.

In this episode we will explore the state of the art of collaboration between machine generated predictions and people in the creative process. What is the work ahead of us in order to make this join venture better? What is going to be the role of increased interpretability of predictions, the role of interfaces and how the work of content creator is going to change? Finally what are the dangers, and consequences to the business and the audience?

Our contributors are:  
Greg Detre, Chief Data Scientist from Channel 4 and Atte Jääskeläinen, a renowned ML researcher and a former Director of News and Current Affairs at Finnish Yle.

BBC Machine Learning Podcast: Predictive Machines Meet Human

All around the world, fake news is now the poison in the bloodstream of our societies – destabilising democracy and undermining trust in institutions and the rule of law’ - Speech by Tony Hall, Director-General of the BBCM - Lord Speaker Lecture – Wednesday 20th March 2019

Propaganda, deception, suppression of free speech, have all been enduring issues for every society, but in recent years terms like ‘fake news’ and misinformation have been heard in public discourse with alarming regularity.
So, what is happening to make it a live issue for news organisations? Can anything be done to push back against the wave of disinformation? What type of interventions are needed? Can ML help tackle disinformation?

In this episode we’ll hear from:
- Rasha Abdul-Rahim, Deputy Director, Amnesty Tech, Amnesty International
- Sam Jeffers, Managing Director, The Shop
- Jon Lloyd, Head of European Campaigns (Advocacy), Mozilla
- Dr David Corney, NLP Engineer at Full Fact

BBC Machine Learning Fireside Chats

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