Artificial intelligence (AI) 's rise in the corporate world transforms how organisations operate, make decisions, and engage with their customers and employees. However, as AI becomes more embedded in our daily processes, we must ask ourselves a critical question: Are the technologies we adopt as inclusive as the cultures we strive to build?
As someone who has spent years in technology leadership and diversity advocacy, I’ve seen the intersection between AI and diversity, equity, and inclusion (DEI) become more pronounced. AI has the potential to either perpetuate existing biases or actively reduce them. The path organisations choose will depend mainly on how they approach integrating DEI principles into their technology decisions.
Here are some practical recommendations for companies looking to ensure that their AI systems support, rather than hinder, diversity and inclusion goals:
1. Bias Auditing in AI Systems
Recommendation: Companies should conduct regular bias audits of AI systems to identify and address any built-in biases that may affect decisions regarding hiring, promotions, performance evaluations, or customer interactions.
Action: Develop clear criteria for bias identification, including gender, race, ethnicity, age, disability, and other diversity factors. To evaluate AI models, consider adopting frameworks like Fairness, Accountability, and Transparency (FAT) principles.
Supporting Insight: Research shows that biased data sets can perpetuate systemic inequalities. For example, facial recognition technology is significantly less accurate in identifying people of colour and women, leading to concerns about discriminatory outcomes.
2. Diverse Data Sourcing
Recommendation: Companies should ensure that AI systems are trained on diverse data sets that accurately represent demographics, geographies, and social contexts.
Action: Include diverse stakeholders when defining data requirements. Companies should partner with DEI experts during the data collection and model training phases to ensure comprehensive representation.
Supporting Insight: Bias often arises because AI systems are trained on homogenous data sets that reflect the majority’s experience, leading to outcomes that disproportionately disadvantage minority groups.
3. Inclusive AI Development Teams
Recommendation: Encourage diverse hiring in AI development teams to minimise biases in how AI systems are designed, tested, and deployed.
Action: Build cross-functional teams that include technical experts and individuals from diverse backgrounds who can provide input on how the technology might impact different groups.
Supporting Insight: Diverse teams are more likely to identify blind spots in AI systems. Studies indicate that when teams include members from varied demographic backgrounds, they are better able to anticipate unintended outcomes and reduce biases in AI systems.
4. Ethical Procurement Policies
Recommendation: Introduce ethical guidelines into tech procurement decisions, prioritising vendors who commit to DEI.
Action: Create a DEI checklist or evaluation scorecard to assess whether technology providers have integrated diversity and inclusion into their AI products and services. This might include examining how their algorithms are trained, the diversity of their workforce, and their approach to mitigating bias.
Supporting Insight: Some organisations, like IBM, have established ethical AI standards that outline transparency and fairness requirements for AI solutions. Adopting similar practices can encourage vendors to prioritise DEI at the development stage.
5. Continuous Learning and Adaptation
Recommendation: AI systems must evolve alongside DEI initiatives to reflect societal changes, ensuring they remain inclusive as societal norms and values shift.
Action: Implement a continuous review process where AI systems are periodically retrained on updated, more inclusive data sets and reviewed by diverse groups within the organisation to catch emerging biases early.
Supporting Insight: AI models trained on static data risk becoming outdated, especially in environments where cultural and societal norms are rapidly evolving. Having a plan for continuous updates ensures AI keeps pace with changes.
6. Cross-Department Collaboration
Recommendation: Break down silos between technology and DEI departments to ensure AI systems are designed with a holistic view of their impact on all employees and customers.
Action: Establish a formal collaboration process where DEI officers and tech teams co-create procurement policies and frameworks. This may also involve inviting external DEI consultants to audit systems at critical points in development and implementation.
Supporting Insight: Cross-departmental collaboration ensures that ethical and practical considerations are integrated into AI deployment. As AI systems touch multiple parts of an organisation, having DEI embedded from the outset strengthens overall adoption.
By integrating DEI into your AI strategy, you can foster an efficient and equitable culture of innovation. As AI continues to reshape industries, organisations that proactively build inclusivity into their systems will be the ones that lead in both technology and diversity.
Key Takeaways:
AI systems need ongoing bias audits to maintain fairness.
Diverse data sets and development teams can help mitigate unintended biases.
Ethical procurement and continuous learning ensure that AI systems evolve with DEI priorities.
Collaboration between DEI and tech teams is essential for building inclusive technology solutions.
The future of AI is unfolding rapidly, and organisations have a choice: adopt systems that perpetuate bias or proactively build ones that support diverse and inclusive workplaces. Which path will your organisation take?
If your organisation needs help with AI decision-making and roadmap development, please email me (cynthiafortlage@cynthiafortlage.com) to discuss how I might assist you. Together, we can ensure your AI strategy is inclusive and forward-thinking.