Ethical Considerations in AI: An introduction to the ethical challenges and responsibilities associated with AI development

Across every industry, businesses of all sizes are increasingly using predictive analytics: mathematical modelling, statistical analysis, machine/deep learning and even AI. The results can be transformative, offering new and novel insights to develop and grow a business. 

The lifeblood of the insights, patterns and trends is data. Without this, the algorithms (regardless of the approach) can’t undertake their tasks. But, achieving this means using all data responsibly, starting with if the data can be used in an algorithm: before starting any analysis check if any data is personally identifiable data; what consent has been given for its use; how it will be stored securely and who has access to the data.  It’s important to remember that ethical data use not only aligns with regulatory compliance, such as GDPR, but also builds trust with your customers, which is invaluable for business growth.

Extending this, not only the data but the techniques used to develop tools, need to be selected and applied with ethical considerations in mind.  An machine learning system trained on biased data, for example, will yield biased outcomes, which can perpetuate and amplify societal inequities.  It is important to spend time understanding the structure of the data, what is includes (and excludes) as well as how the underpinning maths in the algorithm will use this data.  Only then can the correct technique be selected, rigorously testing and validation before the results are evaluated to ensure fairness and impartiality.

The output of any analysis, including AI, the decisions made upon it and actions that could be taken will have real-world consequences.  That has be to front and centre in the minds of developers and end users.  This will allow the modelling and mathematics to show not only what is can do, but what it should do (and not become “magic”): it creates an awareness of the implications of the model. 

As a SME business owner, I know firsthand that placing ethics at the centre of modelling is not only the only and right thing to do, it creates trust within your team to allow them to innovate responsibly.  By incorporating ethical principles into your modelling and AI strategies, you are setting a foundation for sustainable and socially conscious growth.

To achieve this, it’s important to encouraging your teams to learn more about data ethics so they can develop mindful solutions.  There are a wide range of online resources such as the Centre for Data Ethics and Innovation to help upskill your team.  In fact it’s important to make this part of continual professional training as ethics inevitably evolves with both societal norms and technological advancements.  Simply put, the ethics of your work  cannot be an afterthought it has to central to your business so you can lead with integrity and innovate with confidence.

By Dr Sophie Carr

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