Technology has advanced enormously in the 30 years since the introduction of the first Children Act, which shaped the UK's system of child safeguarding. Today a computer-generated analysis – "machine learning" that produces predictive analytics – can help social workers assess the probability of a child coming on to the at-risk register. It can also help show how they might prevent that happening. But with technological advances come dilemmas unimaginable back in 1989. Is it right for social workers to use computers to help promote the welfare of children in need?

The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and the resultant datasets also featuring missing labels, computational data-driven mechanisms are needed to address the data sparsity challenge. In this paper, we propose a machine learning-based method to accurately predict the Air Quality Index (AQI), using environmental monitoring data together with meteorological measurements. To do so, we develop an air quality estimation framework that implements a neural network that is enhanced with a novel Non-linear Autoregressive neural network with exogenous input (NARX), especially designed for time series prediction. The framework is applied to a case study featuring different monitoring sites in London, with comparisons against other standard machine-learning based predictive algorithms showing the feasibility and robust performance of the proposed method for different kinds of areas within an urban region.