Today, Friday, March 8th, is International Women’s Day. There is still inequality in the world. Events around the world are dedicated to celebrating the social, economic, cultural, and political achievements of women. While also striving to accelerate the push towards women’s equality. There are a huge number of events held globally to celebrate, educate, and entertain people.
Enterprise Times published a blog that reveals the opinions of five female tech leaders about tech innovation in the age of generative AI. One crucial aspect of generative AI is ensuring that the data it is fed is unbiased. For many organisations, the data they have access to is not objective. And is built on the foundations of human prejudices, which continue to pollute data even now.
Gender bias is just one example. But as IWD unfolds Tendü Yoğurtçu, PhD, Chief Technology Officer at Precisely, shares her views and tips about how organisations can address this. She notes the important steps organisations must take. To ensure that their generative AI outputs deliver more trustworthy and inclusive outcomes.
Data Integrity is critical for unbiased AI
Yoğurtçu comments, “Many organisations are still missing a crucial element of DEIB initiatives – the representation in their data. While we like to think of data as being impartial, the truth is that human biases create data biases, too. This presents a major challenge, particularly as the use of AI and automation grows. After all, AI models are a product of the data they are trained on.
“It’s already creating real-world issues – from impaired facial recognition software that less accurately identifies women and people of colour to inequities in healthcare provision, and more. To address this, businesses need to ensure that AI/GenAI and automation programmes are being fuelled with high-integrity data – allowing leaders to make better, more representative, decisions.
“Ahead of International Women’s Day, it is essential for organisations to realise that AI programmes need fuelling with high-integrity data. Data integrity is built on the core pillars of enterprise-wide integration, accuracy and quality, location intelligence, and data enrichment. By leveraging these pillars, organisations can ensure there is access to the right data, enrich it with trusted third-party datasets, correctly prepare it for use in intelligent models, and ensure data and AI governance.
“As sophisticated AI models continue to evolve in 2024, data integrity will play a pivotal role in unlocking their true potential, allowing organisations to elevate AI initiatives to new heights, and deliver trustworthy and dependable results that propel their business toward success.”
