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IWD 2024: Artificial intelligence is inherently bias - here’s how to remedy it
Fri, 8th Mar 2024

The efficiency, accuracy, and innovation that artificial intelligence promises to bring to the workplace is often overshadowed by the pervasive challenge of bias. AI technologies have consistently been shown to exhibit significant biases in content generation, often failing to produce inclusive and nuanced outputs. As AI continues to permeate the workplace, it is imperative that businesses become acutely aware of the implications bias in AI can have, not just on operational efficiency and risk management, but on the broader societal impact of the technologies we deploy.

Yet only 22% of data and AI professionals in the UK are women and just as worryingly, 79% of working women are employed in jobs most susceptible to AI automation. A diverse set of voices in the creation of AI programmes therefore takes on increased importance to ensure that women are not washed away in the AI wave.

A diverse group of AI builders

The root of the problem often lies in the lack of diversity within the teams developing these technologies. A homogenous group of developers is more likely to overlook or unconsciously encode their biases into AI systems. Consequently, the technology reflects a narrow perspective, limiting its effectiveness and applicability across different demographic groups. This not only affects the reliability of AI but also its ability to serve diverse global populations fairly.

A recent report by Aporia, the AI control platform company, highlights a concerning trend of hallucinations (instances where AI generates false or misleading information) and biases within generative AI and large language models, signalling a crucial challenge for an industry rapidly advancing towards maturity. An overwhelming 93% of machine learning engineers report encountering operational issues with production models, with 89% experiencing hallucinations manifesting as factual errors and biases.

The solution, therefore, partly lies in fostering more diversity within the AI field. By bringing different perspectives to the table, we can develop technologies that are not only more inclusive but also more reflective of the multifaceted nature of our global community. Diverse teams are better equipped to identify and mitigate biases, ensuring AI models evolve to generate content that is fair, accurate, and universally applicable.

Using AI to build better, diverse teams

Indeed, organisations can leverage AI to bolster the development of diverse talent. AI-driven learning and development platforms can mitigate the potential bias and exclusion inherent in traditional in-person learning. These platforms offer a more inclusive approach, enabling remote learning that can be undertaken anytime, anywhere. DEJI Digital provides such a platform, helping managers unlearn bias and equipping them with the tools to build effective, inspiring workplaces, powered by inclusive AI. This type of democratisation of learning opportunities is crucial for nurturing a diverse pool of talent, equipped to lead and innovate in an increasingly AI-driven world.

Automating routine workstreams through AI also plays a pivotal role in talent development. By offloading administrative tasks, time management, and repetitive tasks to AI, rising talent can focus on cultivating higher-level skills essential for senior roles. Skills such as strategic thinking, people management, and creativity become more attainable when individuals are not bogged down by the minutiae of day-to-day operations. This shift not only accelerates career progression but also ensures a pipeline of diverse, well-rounded leaders capable of steering organisations towards sustainable growth and innovation.

However, realising these benefits necessitates a conscientious effort to design AI systems that are both equitable and effective. This includes investing in monitoring and observability tools to detect and correct biases in real-time, a challenge underscored by 88% of machine learning professionals as per Aporia's report. Additionally, the development of these monitoring tools should not be seen as a mere operational expense but as a strategic investment in the long-term viability and ethical standing of AI technologies.

The path forward is clear. By advocating for diversity in AI development teams, championing AI-driven talent development, and prioritising investments in bias mitigation tools, we can lead the charge towards creating AI technologies that are not only innovative but also equitable and inclusive.

Addressing bias in AI is not just a technical challenge; it's a moral imperative and a strategic necessity. As leaders in finance, we have the responsibility and the opportunity to shape an AI-driven future that reflects the best of what we aspire to be: diverse, inclusive, and relentlessly innovative.