It goes without saying that businesses ignoring Artificial Intelligence (AI) are at risk of falling behind the curve. The game-changing tech has the potential to streamline operations, personalise customer experiences, and reveal critical business insights. The promise of AI and Machine Learning (ML) presents immense opportunities for business innovation. However, realising this potential requires an ethical and empathetic approach.
Our research, is AI a craze or crucial: what are businesses really doing about AI? found that 99% of organisations are looking to use AI and ML to seize new opportunities. It also reported that 80% of organisations say they’ll commit 10% or more of their total AI budget to meeting regulatory requirements by the end of 2024.
If this is the case, the questions businesses should be asking themselves are: How to implement AI ethically? What are the concerns they should be aware of? And is it a philosophical question to answer or a technological one? Or perhaps a social and organisational one?
Implementing ethical AI
Businesses shoulder a significant responsibility in shaping the ethical development of AI. For AI to genuinely serve people’s interests, developing AI ethically must be a part of the process from the outset. It’s essential that those impacted by the transformative changes brought about by AI are involved from the very start. Ethics must central to the process from inception and ideation, to the design of AI-based solutions and products.
Implementing AI ethically requires stringent data governance, making algorithms fair and unbiased. AI developers also need to ensure they build transparency into how AI systems make decisions that impact people’s lives. With that, addressing fairness and bias mitigation throughout the AI lifecycle is also vital. It involves identifying biases present in training data, algorithms, and outcomes, and then taking proactive measures to address them.
One way in which organisations can ensure fairness and bias mitigation is by employing techniques such as fairness impact assessments. This assessment involves having a diverse team, consulting stakeholders, examining training data for biases, and ensuring the model and system are designed and function fairly to mitigate biases.
Fostering transparency in AI systems
Fostering transparency in AI systems isn’t just a nice-to-have; it’s imperative for ensuring ethical use and mitigating potential risks. This can be achieved through data transparency and governance. Users should feel like they’re in the driver’s seat, fully aware of what data is being collected, how it’s being collected, and what it’s being used for. It’s all about being upfront and honest.
Developers must implement robust data governance frameworks to ensure the responsible handling of data including data minimisation, anonymisation and consent management practices. Transparent data governance isn’t just about ticking boxes; it’s about building trust, empowering users, and ensuring that AI systems operate with integrity. The more transparent this is, the more easily users will be able to understand how data is used.
Aligning AI systems with human values
Ensuring AI systems align with human values is a significant challenge. It’s a technological hurdle requiring significant work, but also a philosophical and ethical dilemma. We must put in the social, organisational and political work to define the human values for AI alignment, consider how differing interests influence that process, and account for the ecological context shaping human and AI interactions.
Current AI systems learn by ingesting vast amounts of data from online sources. However, this data is often disconnected from real-world human experiences and factors. It may not represent nuances such as interpersonal interactions, cultural contexts, and practical life skills that humans rely on. As a result, the capabilities developed by these AI systems could be out of touch with authentic human needs and perspectives that the data fails to capture comprehensively.
The values we are concerned with, such as respect for autonomy, fairness, transparency, explainability, and accountability, are embedded in this data. The best AI systems we have, and the ones that are successful, use humans and human judgements again as a source of data. These humans judgements guide these models in the right direction.
Next steps
The way that AI model developers architect and train their models can result in more than issues of data quality. They can also result in unintended biases. For example, users of chat systems may already be aware of the strange relationship of those systems to uncertainty. They don’t really know what they don’t know and therefore cannot act to fill in the gaps during conversation.
Businesses must audit algorithms, processes, and data to ensure fairness, or risk legal consequences and public backlash. Assumptions and biases embedded in these algorithms, process and data, as well as their unpredicted emergent properties, potentially contribute to disparities and dehumanisation that conflict with a company’s ethical mission and values. Those who deploy AI solutions must constantly measure their performance against these values.
Without a doubt, businesses have a significant obligation to steer AI’s development ethically. Ongoing dialogues with stakeholders, coupled with a diligent governance approach centred on transparency, accountability, empathy and human welfare – including concern for people’s agency – will enable companies to deploy AI in a principled manner. This thoughtful leadership will allow businesses to unlock AI’s benefits while building public trust.
- Data & AI
