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This thought-provoking course introduces students to the AI technologies, and more broadly, the algorithms currently informing criminal justice policy and practice in contemporary justice systems. Theory and research from the fields of criminology and sociology are used to explore the complex role of the technologies. Students are also introduced to digital research methods for researching criminological and sociological topics to influence criminal justice policy. Additionally, the module considers the methodological and ethical issues that arise when researching topics such as online radicalisation and victimisation, for criminal justice policy impact.
While the module focuses on England and Wales, in order to deepen students’ appreciation of the merits and demerits of AI technologies and algorithms more broadly, and perceive common problems inherent in the technologies, international comparisons are embedded throughout the module.
Participation in this module does not require prior knowledge of how algorithms are operationalised or their technical dimensions. The module focuses on criminological and sociological perspectives on their use and their broader social, political, and cultural implications.

In this module you will explore the application of Artificial Intelligence (AI) in analysing social problems and formulating public policy responses. You will examine how AI technologies are reshaping our understanding of societal issues and influencing policy-making processes. The module is designed for postgraduate students from various disciplines, particularly those interested in public policy, social sciences, and the societal implications of AI.

In this module you will explore the application of Artificial Intelligence (AI) in analysing social problems and formulating public policy responses. You will examine how AI technologies are reshaping our understanding of societal issues and influencing policy-making processes. The module is designed for postgraduate students from various disciplines, particularly those interested in public policy, social sciences, and the societal implications of AI.

Artificial intelligence requires access to data and computation. Both data and computational are material: they are produced by people, made possible by resource extraction, need power to survive, and both inhabit and resculpt the landscape. The use of AI, then, contributes to the climate crisis, but that role can be hard to see, hidden as it often is by a veneer of utopian hype that surrounds the information technology sector. Drawing on scholarship from digital media studies, environmental history, computer science, science and technology studies, climate science, and archival science, this module examines the past, present, and future intersections of AI, data, computation, and the natural environment. It lifts the lid on the countercultural origins of techno-utopianism. It examines the environmental degradation and injustices that techno-utopianism has and continues to hide (e.g. the instrumentalisation of personal climate responsibility). And it opens a pathway for building an intersectional and justice-oriented environmentalist practice in relation to AI.

Artificial intelligence requires access to data and computation. Both data and computational are material: they are produced by people, made possible by resource extraction, need power to survive, and both inhabit and resculpt the landscape. The use of AI, then, contributes to the climate crisis, but that role can be hard to see, hidden as it often is by a veneer of utopian hype that surrounds the information technology sector. Drawing on scholarship from digital media studies, environmental history, computer science, science and technology studies, climate science, and archival science, this module examines the past, present, and future intersections of AI, data, computation, and the natural environment. It lifts the lid on the countercultural origins of techno-utopianism. It examines the environmental degradation and injustices that techno-utopianism has and continues to hide (e.g. the instrumentalisation of personal climate responsibility). And it opens a pathway for building an intersectional and justice-oriented environmentalist practice in relation to AI.

Artificial intelligence requires access to data and computation. Both data and computational are material: they are produced by people, made possible by resource extraction, need power to survive, and both inhabit and resculpt the landscape. The use of AI, then, contributes to the climate crisis, but that role can be hard to see, hidden as it often is by a veneer of utopian hype that surrounds the information technology sector. Drawing on scholarship from digital media studies, environmental history, computer science, science and technology studies, climate science, and archival science, this module examines the past, present, and future intersections of AI, data, computation, and the natural environment. It lifts the lid on the countercultural origins of techno-utopianism. It examines the environmental degradation and injustices that techno-utopianism has and continues to hide (e.g. the instrumentalisation of personal climate responsibility). And it opens a pathway for building an intersectional and justice-oriented environmentalist practice in relation to AI.

This module is the lab programme for all first-year students enrolled on an AICE degree programme. A range of lab activities which tie into each of the first year modules are provided. It aims to give students the opportunity to apply the theory that they learn in their other modules, and to provide them with transferrable, subject-based and professional skills that they will need for their degree and career. The module must be passed as a whole to progress onto year 2.

Structurally, the AICE Part One Laboratory Programme is organized to cover all practical and laboratory based work in the first-year in a single timetable organized into central laboratory locations.

There are a number of technical laboratories integrated into the Laboratory Programme which cover practical Learning Outcomes from other technical modules in the Programmes.

This module is the lab programme for all first-year students enrolled on an AICE degree programme. A range of lab activities which tie into each of the first year modules are provided. It aims to give students the opportunity to apply the theory that they learn in their other modules, and to provide them with transferrable, subject-based and professional skills that they will need for their degree and career. The module must be passed as a whole to progress onto year 2.

Structurally, the AICE Part One Laboratory Programme is organized to cover all practical and laboratory based work in the first-year in a single timetable organized into central laboratory locations.

There are a number of technical laboratories integrated into the Laboratory Programme which cover practical Learning Outcomes from other technical modules in the Programmes.

The module has been designed to impart the scientific knowledge required to tackle the many problems associated with air and other types of environmental pollution, including how to identify and assess the nature, sources and effects of pollutants, how to measure and monitor pollution, and how to remediate existing problems using technology and/or management strategies. For example, we have learned over the last 60 years that poor air quality can have damaging effects on both the living and non-living environment. Air pollution can degrade forests, lakes, crops, wildlife, buildings and other materials as well as having a detrimental effect upon human health. We have had to devise methods to accurately measure and monitor the air that we breathe, develop technology to clean up polluted air from the industries we have created, and institute strategies to ensure that the air remains fit for now and for the future. All this has to be done within a limited budget and using best available practice. This module focuses upon all of these issues. It aims to equip students with the scientific knowledge and skills to make a professional contribution to current and future debates about air and other environmental pollution, and to the practical steps which need to be taken – whatever they may be – to maintain and improve environmental quality. Students will use their knowledge and skills to complete assignments that will test the learning outcomes for the module.

This module does not have any pre-requisites, but some background in chemistry and biology is preferred. Students will be required to perform mathematical activities.

The module has been designed to impart the scientific knowledge required to tackle the many problems associated with air and other types of environmental pollution, including how to identify and assess the nature, sources and effects of pollutants, how to measure and monitor pollution, and how to remediate existing problems using technology and/or management strategies. For example, we have learned over the last 60 years that poor air quality can have damaging effects on both the living and non-living environment. Air pollution can degrade forests, lakes, crops, wildlife, buildings and other materials as well as having a detrimental effect upon human health. We have had to devise methods to accurately measure and monitor the air that we breathe, develop technology to clean up polluted air from the industries we have created, and institute strategies to ensure that the air remains fit for now and for the future. All this has to be done within a limited budget and using best available practice. This module focuses upon all of these issues. It aims to equip students with the scientific knowledge and skills to make a professional contribution to current and future debates about air and other environmental pollution, and to the practical steps which need to be taken – whatever they may be – to maintain and improve environmental quality. Students will use their knowledge and skills to complete assignments that will test the learning outcomes for the module.

This module does not have any pre-requisites, but some background in chemistry and biology is preferred Students will be required to perform mathematical activities