Ten articles to read on the future of inclusion in AI: Why AI matters in social innovation

AI is already shaping the way people live and affecting day-to-day functions in society. It holds promises for social good, but also presents many challenges and risks. Yet, for many it remains inaccessible. How can everyday voices be included in the debate? How can we design AI for the common good? How do we ensure accessibility to non-technical sectors? These ten articles are curated by SIX to spur conversation on these debates.

1) Design in Tech Report– John Maeda, Automattic, March 2018

John Maeda, Head of Computational Design and Inclusion at Automattic explains that the future opportunity of artificial intelligence lies in inclusive design. Amidst the rise of AI, Maeda presents challenges of these emerging technologies, such as data algorithms, which are historical data riddled with systematic discrimination against women and people of colour. 

2) Koreo Futures: The Future of Meaningful Work– Koreo, February 2018

When we imagine the potential impact of technologies on the way we work, it often results in a depiction of the future as utopian or dystopian. However, if we see future of work as inherently tied to the social impact we make, we begin to notice that it’s less a matter of an AI-enhanced workforce, but more on how those efficiencies might impact the community of people working in various sectors and disciplines. This article looks at the future of work from people perspective, exactly what the activity means and what purpose it brings to many people’s lives.

3) A roadmap for AI: 10 ways governments will change (and what they risk getting wrong)– Geoff Mulgan, Nesta, February 2018

What are the potential benefits and risks of using AI in government? Chief Executive Office of Nesta, Geoff Mulgan, shares his insights from Nesta’s research to tackle the often misinterpreted and misunderstood ideas of AI capabilities. Focusing, above all, on not only the functions, but rather on how these technologies can be used to achieve the impact we want.

4) It is More Vital than Ever that Charities Get to Grips with AI– Rhodri Davies, CAF Giving Thought, January 2018

Davies highlights the absence of charities from the debate on AI ethics and argues the sector must engage with the connected issues. Ethical and political questions cannot be left up to technical experts, who may miss key social implications. Charities represent the most marginalised in society, who will be affected by negative consequences of AI (workplace automation, algorithmic bias). Davies poses a number of questions and challenges to the sector.

5) What we talk about when we talk about fair AI– Fionntan O’Donnell, BBC News Labs, December 2017  

In the near future, the BBC and other news agencies will be using more and more machine learning algorithms for storing, retrieving, and tagging and even creating content. This raises critical questions: what if audiences are directed only to articles they want to read, without being challenged? What data are audiences comfortable having collected? Should algorithms used be open to public scrutiny? O’Donnell discusses fairness, accountability, and transparency in AI to present a more holistic picture of the AI fairness question.

6) 10 principles for public sector use of algorithmic decision making– Eddie Copeland, Nesta, February 2018

Eddie Copeland, the Director of Government Innovation at Nesta, argues that a stronger case should be made for the government and the public sector where the individuals rarely have an option to opt-out  of using a corporate service. In the article, he introduces ten principles that he thinks should be adhered by public sectors to make a responsible algorithmic decision making.

7) Artificial Intelligence: The Future of the Charity Sector– Chloe Green, February 2018

This article discusses how AI can be harnessed by the average small charity, rather than it being concentrated solely in the hands of big-name organisations. For instance, AI can be used by charities through the provision of advice with Chatbots, sifting through large amounts of web information and presenting it in a clear way at any time of day. The article presents a number of entry points for small charities.

8) How to Involve the Public in the Development of Artificial Intelligence– Tom Saunders, Nesta, December 2017

Given that AI is already shaping the way ordinary people live; as such, this article asks ‘where are the voices of ordinary people in the conversation about how AI should develop?’ Saunders presents three reasons why governments should engage the public in this conversation, and three suggestions of how to do it.

9) Controlling Machine-Learning Algorithms and their Biases– Tobias Baer, McKinsey, November 2017

While machine learning has been presented as offering a solution to human biases, it is designed to emulate mechanics of the human brain – as such, machines too can be biased. For example, algorithms can formalise biased parameters set in loan applications and  machine learning relies on historical criteria to predict behavioural outcomes, reinforcing past biases. Baer presents practical tips to eliminate these risks.

10) AI is helping refugees to find more than just a place to live – Kyree Leary, World Economic Forum, February 2018

In 2016, nearly 65.6 million people across the world were forcibly removed from their homes due to war and human rights violations. Not only were they faced with the challenge of finding the new home, but often host countries would be selected based on availability of space. However, researchers from Stanford University and ETH Zurich developed an algorithm that could help place refugees in locations that could potentially boost chances of finding employment and integrate into an unfamiliar society.

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