Risks and considerations

Introduction

If AI is the wonderful thing that people claim it to be, what’s the problem? Well, there are actually a whole lot of problems and risks which you have to consider as part of your due diligence and this is where ethics comes in.

In this context, ethics is the systematic practice of distinguishing right from wrong in decision-making, moving beyond legal compliance to address the broader moral implications of technology. It means actively questioning the impact of AI on human dignity, societal fairness, and ecological stability, rather than accepting technological capability as a justification for deployment. For organisations, this requires a shift in perspective: viewing ethics not as a barrier to innovation, but as the essential compass that guides responsible development and ensures that progress does not come at the expense of fundamental values.

So, in order for an organisation to “do it right” or “do it responsibly”, ethics must be the primary lens through which all other risks are viewed. Whether assessing the environmental footprint of data centres, the labour conditions within the supply chain, or the potential for algorithmic bias, an ethical perspective ensures that these issues are not treated as isolated compliance hurdles but as interconnected elements of a broader responsibility. By embedding this mindset into strategic planning, leaders can proactively identify and mitigate harms before they occur.

The following sections outline the critical areas where this scrutiny is most vital, providing a foundation for organisations to navigate AI adoption with integrity and foresight.

Risk of Bias and Discrimination

AI systems are fundamentally data-driven, and the outputs produced through AI systems are always a reflection of the data that those systems were trained on. Yet data is often a reductive and incomplete representation of reality. It captures what has been measured, frequently omitting the nuance and complexity of human experience and encoding historical prejudices. When models are trained on such datasets without intervention, they risk perpetuating or amplifying discrimination based on race, gender, age, or socioeconomic status. This "bias in, bias out" dynamic transforms past injustices into automated future decisions. For example, AI systems used in recruitment have often led to male applicants being identified as the most suitable candidates, because historic data used to train the model reflected previous prejudices of human recruiters. Presenting AI decisions as being mathematically objective or value free can make discriminatory outcomes harder to detect and challenge than human bias.

The risk extends beyond who is represented to who is missing. Gaps in data collection can render certain groups invisible, while flawed metrics may optimise for proxies that correlate with protected characteristics. In high-stakes areas like recruitment or talent identification, this can systematically exclude marginalised groups and undermine fairness. Mitigating these risks demands more than technical fixes; it requires a critical contextualisation of data. Organisations must actively audit datasets for representation gaps, question the historical context of their information, and maintain rigorous human oversight to ensure AI tools promote inclusion rather than cementing past prejudices.

Environmental Impact

While it often seems like digital technologies like AI are somewhat abstract and do not have a physical presence in the world, in reality their environmental impacts are substantial.

The environmental footprint of artificial intelligence extends far beyond the electricity used to power data centres; it is embedded deeply into the supply chain, development, and operational use of the technology. At the extraction stage, the mining of critical minerals such as lithium, cobalt, copper, and rare earth elements causes profound physical landscape destruction. Open-pit mining and chemical leaching processes strip vast areas of vegetation, destabilise soil structures, and generate toxic tailings that can contaminate local water tables and ecosystems for decades. This upstream impact is particularly severe because the rapid obsolescence of AI hardware accelerates the demand for new raw materials, creating a cycle of continuous extraction that outpaces natural regeneration and sustainable land management. Electronic waste, or ‘e-waste’, (created by the obsolete hardware) is also one of the fastest growing waste streams.

During the development and manufacturing phases, the environmental cost shifts to energy-intensive processing and fabrication. Refining rare earth elements and producing advanced semiconductors require immense amounts of electricity and water, often in regions where the power grid relies heavily on fossil fuels. The chemical processes involved in chip manufacturing also release hazardous pollutants and greenhouse gases, contributing to local air quality degradation and global climate change. Furthermore, the global logistics network required to transport these components from mines to refineries, then to factories, and finally to data centres adds a significant layer of carbon emissions through shipping and air freight, compounding the total lifecycle footprint before the AI system is even switched on.

In the operational phase, the environmental burden manifests through sustained high energy consumption and water usage, sparking growing controversy and local resistance. Training large-scale models and running inference tasks require massive data centres that generate intense heat, necessitating cooling systems that often consume millions of litres of fresh water daily. This has led to significant pushback in various regions, including the UK, parts of Europe, the United States, and in South America (see the AI Resist List) where communities and local authorities are increasingly opposing the construction of new facilities. Critics argue that these centres strain local power grids, divert scarce water resources from agriculture and residential use during droughts, and disrupt local ecosystems. As a result, permitting processes are becoming more contentious, with some projects facing delays or cancellations due to public outcry over their unsustainable resource demands and lack of transparency regarding their long-term environmental impact. So, when the technology sector talks about digital and the “cloud” we must remember that what lies beneath is not ethereal but is very real and physical.

When it comes to individuals’ own use of generative AI tools, environmental considerations should be one of the factors informing decisions about which tools to use, and for what purposes. Accurate figures about the actual impacts are difficult to obtain due to a lack of transparency from AI companies, however one study estimated that for every typical conversation with ChatGPT the model used 500ml of water. Another study estimated that created a single image using a generative AI model has the same carbon emissions as fully charging a mobile phone.

Human Rights and Labour Exploitation

The human rights implications of the AI supply chain are significant, beginning with the extraction of critical minerals in regions where regulatory oversight is often insufficient. In areas such as the Democratic Republic of the Congo, mining operations for cobalt and other essential materials have been consistently linked to serious labour violations, including the use of child labour and hazardous working conditions. These practices contribute to local instability and community displacement, creating a stark disparity where the economic benefits of AI technology are largely realised in developed nations, whilst the social costs are borne by vulnerable populations in resource-rich regions. Despite international attention, mechanisms for protecting affected communities remain weak, and the opacity of global supply chains continues to hinder effective enforcement of ethical sourcing standards.

Labour exploitation also extends to the digital workforce essential for training AI models. The development of sophisticated algorithms relies heavily on data labelling and content moderation, tasks frequently outsourced to workers in lower-income countries. These individuals often operate under precarious employment conditions, characterised by short-term contracts, low piece-rate pay, and a lack of basic benefits or job security. In addition to precarious working conditions, content moderators tasked with filtering