Research shows that visualising one's future self has a direct impact on healthier outcomes by encouraging change. With COVID-19 we learnt that lifestyle, ethnicity and socio-economic factors played a part in how badly people were affected by the virus. For example, obesity, ethnic minorities, or living in poorer areas increased the chances of hospitalisation.The BMJ (British Medical Journal) reported that working age adults in England's poorest areas were almost four times more likely to die from COVID-19 than those in the wealthiest areas. Being obese can contribute to preventable diseases such as cancer, diabetes and heart disease. Change My Face (CMF) software addresses these problems by way of behavioural change and helps to educate and level up. For example, encouraging healthier employees in the workplace. CMF is a B2B2C enterprise which aims to help our clients improve their customer's health. This is achieved through individuals visualising their future self in 10 or 20 years' time with the effects of lifestyle. Importantly, CMF recently pivoted to a SaaS model having received ERDF & UWE S4G funding resulting in a prototype whitelabel lifestyle age calculator app which is already in use by companies and organisations around the world. However, although AI is breaking new ground in health, and embracing 4.0 futures, The National Institute of Standards and Technology (NIST) reports that _evaluated facial-recognition algorithms from around 100 developers from 189 organizations, including Toshiba, Intel and Microsoft_ leaned towards a white male bias. Recommendations are that inclusivity should be emphasised in the design process and with consideration for diverse groups such as gender, race, class, and culture. CMF, due to costs have built an app for age models using publicly available image datasets which lean towards caucasian celebrity faces. The Inclusive Innovation Award will help CMF to construct an image dataset to become more inclusive representing our multi-cultural society. CMF can exploit new markets for commercialisation including global opportunities into new markets such as personalised health (wearables) and personalised skincare. _Inclusive AI for Healthy Change, Retaining Identity Preservation_ project will begin with the annotation and data labelling of between 40- 50k images of faces to ensure nonbiased outputs for different ethnicities, genders and age groups. Secondary annotation and labelling categorisations will take into consideration health, such as weight, skin quality, wrinkles, sunspots and under eye bags. Benchmark for positive age outcomes will be put into place.