While AI is powerful, much of the time, human intuition and behavior is still more valuable for psychological research. This chapter focuses on crowdsourcing – a method for leveraging the intelligence of many people to complete a task. The chapter discusses the use of crowdsourcing and citizen science across several fields, and how to decide when to use crowdsourcing versus AI for analyzing complex psychological data. The chapter also provides practical advice on what platform to choose, and how to avoid low-quality data from bots or cheaters.

The term ‘social work’ was first coined by the American economist Simon Patten in 1900. He envisaged a new profession that would address the social problems of the modern world. These problems are neither timeless nor innate to human nature, but come into being at particular points in history as a result of people’s actions and the way they organise power in society. Looking at these issues historically enables us to see the way social problems (such as extreme inequality and poverty, mass urbanisation, industrial pollution, racism, sexism and different forms of violence) have been constructed and varied over time. More importantly, this lens may provide us with clues as to how people might un-make these problems and do something better. This historical perspective is vital for practice today because it locates critical social work as part of much wider and ongoing struggles for social justice and human rights.

Whenever we interact with technology, we are constantly providing data about who we are, what we think, and the choices we make. One of the major goals of this chapter is to help the reader think creatively about what data is being recorded that can be used to answer important psychological questions. First, we tell the story of a collaboration between a mobile game and psychology researchers that enabled new insights into visual attention. The chapter then provides analysis of what apps can record from us, and principles of user interface / user experience design that can inform psychological research. The chapter discusses other examples of psychological insight from apps and websites, including those related to romantic relationships, navigation and memory, concept representations, and games. Finally, the chapter provides advice on establishing academic-industry collaborations, as well as some words of caution on over-interpreting cognitive effects found in apps and games.

This book is about the potential of social work, and in particular the potential of critical social work. It is about what social work is, what social work can be and, from a critical perspective, what social work should be. We use the word ‘potential’ quite deliberately, as it implies that there are elements of uncertainty in endeavouring to make social work critical that are yet to be fully realised and never guaranteed. Yet, in the current context, the values and vision of critical social work are perhaps more relevant and important than ever before.

As we think and act, the brain is constantly producing Big Data in the firing of its neurons and in the connections that are strengthened and weakened. This chapter discusses how we can study the brain and the Big Data that it creates. First, we discuss how clever behavioral tasks, looking at development and other species, and natural variation across people are our first tools for understanding the brain. Next, we delve into describing several popular brain imaging methods – direct recording, electroencephalography, magnetoencephalography, magnetic resonance imaging, and a few others. We discuss how to interpret the Big Data shown by brain maps, and some Big Data methods like multiple comparisons correction to consider when viewing this data. Finally, we end the chapter discussing the ethical question of whether such neuroimaging allows mindreading.

This chapter describes the important role of artificial intelligence (AI) in Big Data psychology research. First, we discuss the main goals of AI, and then delve into an example of machine learning and what is happening under the hood. The chapter then describes the Perceptron, a classic simple neural network, and how this has grown into deep learning AI which has become increasingly popular in recent years. Deep learning can be used both for prediction and generation, and has a multitude of applications for psychology and neuroscience. This chapter concludes with the ethical quandaries around fake data generated by AI and biases that exist in how we train systems, as well as some exciting clinical applications of AI relevant to psychology and neuroscience.

Now equipped with broader participant samples and more diverse stimuli, we can create Big Data experiments. This chapter reviews research methods involved in running Big Data surveys and experiments. The chapter discusses overt and covert measurements that we can collect via online experiments. The chapter then discusses practical logistics to keep in mind when running a Big Data experiment, including experimental design decisions, and a behind-the-scenes look at how data is saved online via server-side coding. Next, once you have the data from an experiment, how do you clean the data and how do you visualize it? The chapter ends with discussion on the ethical implications of collecting covert measures and the useful applications of web-coding skills to create public-facing websites.

In Chapter 1, we invited you to consider the critical potential of social work: the potential for us as individual workers, and collectively as a profession, to question the social conditions and discourses that give rise to human suffering and what we might do about these. The critical standpoint is one that sensitises us to social injustice and the need for transformation. Being a critical practitioner is challenging: while we may decide that this is the path we wish to take, it is an ongoing process, borne out in day-to-day and week-to-week activities. Becoming a critical practitioner is not a single act of commitment, but an often-arduous journey of revelation and struggle. There are many potential setbacks along this journey. As the words of a great twentieth-century social reformer Martin Luther King remind us, ‘Human progress is neither automatic nor inevitable … Every step towards the goal of justice requires sacrifice, suffering and struggle; the tireless exertions and passionate concerns of dedicated individuals.’ Critical social workers are among those dedicated individuals with passionate concerns.

This chapter focuses on how to create Big Datasets by thinking like a data scientist. It begins by discussing examples of impactful open access datasets. It then teaches the reader the basics of data scraping to allow them to create their own datasets, including an introduction to client-side web coding. The chapter concludes with discussion on the ethical questions around data scraping, and current practices in Open Science to make your datasets publicly available.

In this and the following chapter we explore the importance of context for social work practice. Ife et al. suggest that context is vital because it impacts on how social workers understand the issues they are working with and how they will respond. Social work does not exist in a vacuum. Therefore, we focus on a number of powerful social forces that shape our social contexts. These consist of far-reaching (sometimes global) social structures and discourses. Social structures, as noted in Chapter 1, are the enduring social patterns, divisions and institutional arrangements that can give rise to inequality and harm. Discourses, on the other hand, are sets of ideas or language about a particular topic with shared meanings and assumptions that reflect and reinforce particular power relations. In other words, discourses are never neutral descriptions of reality, but actively justify certain asymmetric social structures; in turn, these structures promote discourses favourable to their maintenance.