Machine learning for social good

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4 min read

As the huge potential for data science, machine learning, and AI, to augment human performance emerges, two imperatives become clear. First, it is essential that these powerful technologies are directed towards social good and not just for commercial profit; and secondly, to get the best from these technologies, there needs to be a special focus on developing human capability alongside them - to innovate, to manage change, and to ensure ethical practices are maintained.

The new £250 million fund for a ‘National AI Lab’ to accelerate the use of machine learning in the UK’s National Health Service, announced this year, is a sign of things to come. Public as well as private organisations are beginning to respond to unmissable opportunities to improve operational and economic performance being presented by artificial intelligence.

The potential for machine learning to speed up the diagnosis of killer diseases, to collect and integrate genome sequencing data with disease research data, and to revolutionize NHS outpatient services, is huge. In harnessing that potential the challenge will be to develop the leadership and staff capability able to innovate new systems and manage the transformational changes this will imply.

On average, Google now processes more than 40,000 searches every second (or 3.5 billion searches each day). It is a statistic illustrative of an online world now awash with vast amounts of data, which is the essential fuel enabling AI and machine learning technologies to progress.

“Data is transforming the way business and society work,” as Imperial College Business School Dean Professor Francisco Veloso observes.

Experts at Imperial College Business School’s Gandhi Centre for Inclusive Innovation are now focusing on how data science and machine learning can unleash the potential of information for social good; how these new tools can be used to address the United Nations Sustainable Development Goals, for example - those related to poverty, inequality, climate, environmental degradation, prosperity, and peace and justice.

"Technology has gifted us with rich seams of data that have had a massive economic benefit. And yet, much of the potential for positive social impact is still locked up"

says Kieran Arasaratnam, professor of practice and the Associate Director of the Gandhi Centre.

To unlock this potential the technology has to be universally accessible and the principles of ethical data science must be applied, says Arasaratnam. These principles can be defined as:

  1. Creating systems and infrastructure able to collect data and document collection processes in a way that is professional, legal and ethical.

  2. Data obtained from an individual with their consent should not carry any trace of their identity when published or used by other organisations.

  3. Restrictions on the use of sensitive data (financial, medical and personal) should be honoured.

  4. Individuals should have a transparent view of how their data is used.

  5. Data should not be used to infer predictions not relevant to their original purpose.

  6. Action should be taken to avoid the unconscious biases that machine learning algorithms may absorb from a population.

For data science, AI, and machine learning to help us achieve these wider social goals, NGOs and other organisations involved must also address the same challenges faced by commercial businesses and by public sector organisations such as the NHS. They will have to innovate and embrace change.

In order to successfully integrate machine learning technologies into an organisation (into its practices, systems and culture) – innovation will be needed in three key areas:

  • Innovation directly related to the technology i.e. digital transformation.

  • Innovation related to human capital – i.e. recruitment, leadership development, and training to create an agile and digitally literate workforce.

  • Innovation related to strategy – i.e. embracing an innovation mindset to encourage different models and approaches (as explored in a recent article by Dr Anu Wadhwa, Associate Professor of Strategy and Entrepreneurship at Imperial College Business School).

Across all sectors, public and private organisations are (or are soon to be) dealing with the disruptive potential of these data-driven technologies. With disruption comes the opportunity for breakthroughs, for innovation and change for the better, not only for business but for society too. Unleashing the huge potential of these technologies will be as much about developing human capabilities, as it will be about developing the technology.

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