Data Science, Equality, Social good
AI for Social Good & Equality of Opportunity
On 25 September 2015 at the annual UN meeting, 193 countries signed an agreement on the 17 Sustainable Development Goals (SDG) and 169 associated targets. These goals aim to move us towards an inclusive, just and sustainable society by 2030. The first goal is to end extreme poverty. Other goals focus on health, education, sustainable energy, climate change and reducing inequality.
Over the past five years, some hopeful results have been achieved according to the SDG 2020 report. For example, the rate of children and youth dropping out of school has decreased; access to safely managed drinking water has improved; and women’s representation in leadership roles has increased. At the same time, there’s still an enormous amount of work to be done in the next ten years: the number of people suffering from food insecurity is on the rise, the natural environment continues to deteriorate at an alarming rate, and dramatic levels of inequality persist throughout the world.
Can AI help achieve the SDG’s?
In a recent study published in Nature Communications, Vineusa et al. showed that AI has a positive impact on 80% of the SDGs. AI, for example, enables the creation of circular economies and smart cities that efficiently use their resource and underpin low-carbon systems. However, AI also seems to increase inequality in many socio-economic areas. In part this is because AI is often used with the aim of optimising speed and minimising costs, rather than promoting equality of opportunity.
A good example is the recent controversy in the UK, where an algorithm was used to predict final exam results for students who were unable to take their final exams because of the COVID-19 pandemic. This was thought to be a quick and cost effective way to assign grades to students. However, because the algorithm was calibrated using location data instead of relying on individual performance alone, it produced biased results for students from less advantaged socioeconomic backgrounds.
The Civic AI Lab
It is against this background of deploying AI for good that we launched the 15th ICAI lab this summer: Civic AI Lab (CAIL). CAIL is a collaboration between the City of Amsterdam, the Ministry of Interior Affairs, Vrije Universiteit Amsterdam (VU) and the University of Amsterdam (UvA). The lab’s vision is a society in which citizens from all backgrounds have equal opportunity to contribute to and benefit from an innovative and thriving society. The lab’s mission is to develop AI technology that promotes economic and social human rights, such as the right to health, education and employment, while respecting fundamental human rights such as non-discrimination and equality.
We will start with five PhD projects in the areas of education, environment, mobility, health and well-being. The projects will be carried out within interdisciplinary teams of (AI) scientists, and on the basis of use cases and data provided by the City of Amsterdam. Each project aims to create insights into inequality of opportunity in the City of Amsterdam and to create new ways to increase equality of opportunity with the help of new AI technologies. For example, in the education project we work together with the city’s education department to create fairer models for the distribution of finances between schools in order to give school children in Amsterdam the fairest possible chance of a good education.
A call to action
In September 2020, on the fifth anniversary of the SDGs, dozens of Dutch organizations, including all universities held a flag campaign to express their support for the United Nations’ Sustainable Development Goals. We are using this anniversary as an opportunity to call on AI scientists and Data Science researchers in Amsterdam and beyond to step in, and step up efforts for equal opportunity and SDGs in general. We all need to shift up a gear, now that COVID-19 is leading to an unprecedented health, economic and social crisis and making the achievement of SDGs even more challenging.
For the past five years, Elsevier has been an enthusiastic participant in the UvA Master’s Student programme. In total, more than 45 students have been supervised by researchers across the company, which has led to 12 new recruits for our Data Science teams.
Data Science is rapidly changing industries around the world, yet the digital transformation remains difficult for Fashion. Fashion (Design, Business, Branding, and Marketing) has never been known for maths geniuses. (There are a few, but they keep it a secret.) While maths and data may not be a given in the industry, people who work in fashion are material experts. So what would it mean if we treated data as if it were a material?
In August 2020, VodafoneZiggo and Accenture wrapped up their three-month CodeMasters training programme for refugees. The training course was tailor-made to help refugees integrate in the Dutch labour market by teaching participants to write computer code.