Bias and Representation in the Age of Artificial Intelligence
A critical issue with AI systems is that they need huge amounts of data to learn from, known as training data, and if this data is skewed or not representative, the AI itself will be biased.
The history of Artificial Intelligence (AI) goes back to the 1950s, but it is only since the launch of tools like ChaptGPT that AI has become an increasingly important part of everyday work and life. We are now at a point where it is clear that AI will transform our world in a variety of ways; for example by promoting innovation, streamlining work, and developing solutions to complex problems. However, beyond the promise and potential of AI, there is a troubling reality: AI mirrors and can amplify the biases that exist in our society. This issue threatens to undermine the fairness, accuracy, and potential benefits of AI systems across a wide range of domains, and has important implications for diversity, equity, and inclusion efforts.
AI refers to computer programs and systems that mimic human intelligence and problem-solving abilities through adaptative learning. A critical issue with AI systems is that they need huge amounts of data to learn from, known as training data, and if this data is skewed or not representative, the AI itself will be biased. For instance, facial recognition technologies are often less accurate for people with darker skin tones, and generative AI has been found to recreate traditional gender norms and produce sexualised images of women. These issues reflect societal biases and highlight the pervasive stereotypes that are embedded in the data used to train the AI. This is further exacerbated by the underrepresentation of minority groups in tech and data science roles and decision making, meaning that the teams creating and checking these AI systems may encode their own biases into the technology (consciously or unconsciously).
AI bias has important implications for businesses across all sectors, not just tech. Using AI in an organisational setting without awareness of the potential for biased outputs can perpetuate prejudice and discrimination and may marginalise diverse employees, consumers, and investors. Also, where AI systems are non-representative they are more likely to produce inaccurate solutions, meaning that their usefulness is at best reduced and at worse detrimental to business.
Diversity, Equity, and Inclusion (DEI) initiatives play a crucial role in addressing the current and future challenges of AI. DEI initiatives aim to ensure fair representation of all groups in the workplace and when applied to AI development and utilisation, these initiatives can support the use of systems that are fair, equitable, and truly representative of the diverse world we live in. Also, AI itself can be beneficial for DEI initiatives. For instance, AI can be used to make better, data-informed decisions about what works in supporting inclusion and how to mitigate unconscious bias across various organisational processes. However, it’s important to remember that AI is only as good as the data it learns from.
By acknowledging and actively working to address these biases in AI alongside societal biases, we can unlock the potential of AI to make the world a more equal and just place. However, overcoming bias is a multifaceted challenge that requires concerted effort across several fronts. For instance, we must work to raise awareness and build an understanding of AI bias and how it can impact business operations and decision-making processes. Also, organisations must continue to invest in DEI training programs to foster cultures of inclusivity where all systems (AI and otherwise) will be developed, checked, and deployed with and for diverse peoples. The emerging possibilities and risks of AI mean that it is now more important than ever to connect DEI to organisational digital transformation in more integrated ways.
- Jaimee