The ethical implications of AI and how organisations can use it responsibly

26th March 2025

Everyone’s talking about artificial intelligence at the moment, and it’s likely that your organisation is having conversations about how it can be leveraged too.

There are a lot of operational questions to answer here, particularly around whether or not AI tools can deliver efficiencies without compromising standards, but there are also ethical implications when it comes to AI.

In this article we’re going to take a look at some of those implications and how you can use AI technology responsibly.

Impact on the jobs market

From the invention of the wheel through to the advent of the internet, technology has always had an impact on the work of human beings.

While some developments have sparked entire new industries (multiple new industries in the example of the internet), others have reduced or eliminated jobs altogether. 

Artificial intelligence technology has the potential to have a big impact on the job market, and there are concerns it won’t all be positive. In a report released last year by the Tony Blair Institute for Global Change, it was estimated that ‘full and effective adoption of AI by UK forms’ could be the equivalent to the annual output of 6 million workers, and that 1 to 3 million jobs could be lost to AI.

Use AI to get the best from skilled workers

As the hype around AI grows some businesses might be tempted to delegate more and more tasks to these tools in a bid to reduce staff costs, but this technology still needs a significant amount of human oversight to ensure quality output.

AI tools are still in their early stages and a long way from being able to completely replace a  role (if it ever gets there at all), but the time saved on repetitive and low-skilled tasks could mean organisations need fewer people to deliver the same level of work. The benefit to employees is that they’ll be able to focus on more rewarding higher impact work over monotonous tasks that can negatively affect job satisfaction and employee engagement.

For example, ResourceSpace’s GPT integration allows for automated metadata tagging, with the recognition tool identifying objects, items, faces and places present in an image and tagging them accordingly. This saves the DAM Manager (or DAM user uploading the image) significant time otherwise spent manually tagging with relevant keywords. Instead, this time can be used on more strategic projects that have a much larger impact on DAM operations.

READ MORE: Exploring the ResourceSpace-OpenAI integration

Copyright implications

Generative AI tools like ChatGPT for copy, and Midjourney and DALL-E for image generation, appear to create content from scratch, but the models behind them are trained on the work of thousands of human writers and artists—and that poses ethical questions about whether or not AI generated content is ‘stolen’ from the works that AI is trained on.

There are already some ongoing court cases around this subject, in particular Andersen v. Stability AI, a class action lawsuit brought by internet cartoonist Sarah Andersen, along with several other artists, against several AI companies, including Stability AI, Midjourney and DeviantArt.

A common misconception is that AI models store or contain the original artworks they are trained on, but this isn’t the case. The datasets used for training are far too large to allow for any kind of direct storage or retrieval of specific images. Instead, AI models learn patterns, styles, and structures in much the same way a human artist might be influenced by the works they study. However, unlike traditional artistic influence, AI learns from vast datasets at an unprecedented scale, raising new ethical and legal challenges.

This is a rapidly evolving area where legal frameworks are still catching up. While AI doesn’t store or reproduce artworks in a literal sense, the question of whether training on copyrighted material without permission constitutes infringement remains unresolved. 

Create AI content based on pre-owned media

When you create content using generative AI tools that use the work of other writers and artists as inspiration, it’s currently unclear whether those creators might be able to bring a copyright case against your organisation or the software itself.

However, the best approach to ensure you’re not generating and using content that’s based on the work of others is to use your pre-owned media as a prompt.

For example, you can pre-train your own GPTs on content you’ve already written, ensuring that not only is the content in the tone of voice of your organisation, but also that it’s not primarily based on other writers’ copy. You can do the same thing with image generation, reducing the likelihood that you’re using images and graphics you don’t have usage rights for.

An interesting recent development involves artist-specific models. Innovative platforms like exactly.ai are empowering artists to create AI models based on their unique styles. By uploading a portfolio of 5 to 50 images, artists can train models that generate new artwork in their signature style. This approach not only preserves the artist's creative identity but also ensures they retain copyright over their work. Moreover, artists can earn revenue by licensing their models for commercial use, receiving compensation each time their model is utilised. This model fosters a more ethical and sustainable integration of AI in the creative industry, aligning technological advancement with artists' rights and interests.

Transparency

Does your organisation use AI tools for the processing of customer data, or in any element of the user experience delivered by your services? Transparency is a key issue, and according to Zendesk’s CX Trends Report, 75% of businesses expect a lack of transparency to increase customer churn in the future.

There are three key requirements when it comes to AI transparency:

1. Explainability—the ability of an AI system to provide easy-to-understand explanations for decisions and actions
2. Interpretability—the human understanding of how an AI model operates and behaves.
3. Accountability—ensuring AI systems are held responsible for their actions and decisions.

Organisations using AI tools within their operations will face challenges around transparency that include ensuring customer and employee data security, and being able to explain how these complex AI models work to different stakeholder groups that might not be familiar with the technology and how it works.

Be proactive in communicating AI usage

Don’t hide how your organisation uses AI from customers or employees, and proactively develop visuals and diagrams that illustrate how these complex AI models function within your processes.

When it comes to data security you should already have a robust data privacy policy, but make sure it’s updated to reflect process changes as a result of AI tools. If you haven’t already, appoint at least one person whose sole responsibility is data protection.

Environmental cost

AI requires a huge amount of computing power and, inevitably, this means it has a massive carbon footprint.

AI tech is housed in data centres, temperature-controlled buildings that house massive computing infrastructure including servers, data storage drives and network equipment. The global data consumption of data centres rose to 460 terawatts in 2022, making it the 11th largest electricity consumer in the world, and it’s expected to reach 1,050 terawatts by 2026.

Data centres also produce electronic waste, while they also consume large amounts of water and rely on minerals and elements that are often mined unsustainably.

ResourceSpace’s commitment to sustainability

At ResourceSpace, we're deeply committed to minimising our environmental footprint. We understand that the integration of AI technologies, such as OpenAI's GPT models, into our platform raises questions about energy consumption and sustainability. 
With this in mind, we'd like to share how our partnership with OpenAI and their use of Microsoft's Azure infrastructure aligns with our environmental values.

Understanding the environmental concerns surrounding AI

Artificial intelligence has seen rapid advancements and widespread adoption across various industries. However, this growth has brought attention to its environmental implications.

Training large AI models demands substantial computational power, leading to increased energy consumption. For example, a single AI-generated image can use as much energy as half a smartphone charge, using the least efficient model.

What’s more, the infrastructure supporting AI, particularly data centres, contributes to environmental concerns, with these facilities consuming significant amounts of electricity and water for cooling purposes. In regions like Ireland, data centres account for a notable portion of the national electricity demand, prompting discussions about their sustainability.

In the Republic of Ireland, the likes of Amazon, Google, Meta, Microsoft and TikTok run huge data centres that account for a sizeable portion of the national electricity demand, with concerns around rolling blackouts leading Ireland’s grid operator to pause plans for new data centres until 2028.

Microsoft Azure's commitment to sustainability

Microsoft has been carbon neutral since 2012, meaning they remove as much carbon each year as they emit, either through carbon offsetting or by reducing emissions. By 2025, Azure aims to shift to 100% renewable energy supply, ensuring that all their data centres, buildings and campuses are powered by green energy.

In addition to renewable energy commitments, Azure has implemented innovative solutions to enhance energy efficiency. For example, they are exploring liquid immersion cooling methods to reduce energy consumption in data centres.

By leveraging OpenAI's GPT models hosted on Azure, ResourceSpace ensures that our AI integrations benefit from these sustainable practices. We are committed to continuous improvement and are exploring ways to further enhance the energy efficiency of our services.

Looking ahead

The tech industry is actively addressing the environmental challenges associated with AI. At ResourceSpace, we are dedicated to adopting best practices and supporting initiatives that promote sustainability in AI development and deployment.

We hope this provides clarity and assurance regarding the environmental considerations of our AI integrations. Your trust in our commitment to sustainability is important to us, and we will continue to prioritise responsible practices in all aspects of our operations.

To find out more about our sustainability credentials get in touch with our team here. If you’d like to see how ResourceSpace is leveraging AI to improve Digital Asset Management, you can book your free demo below.