Artificial intelligence (AI) is a key technology of the Fourth Industrial Revolution. It promises to reduce inefficiencies, boost economic productivity and is widely used as in various industries and sectors. AI has contributed a lot to global manufacturing innovations. From helping doctors make accurate diagnoses to providing targeted marketing insights for retailers, AI is a unique technology tool that allows us to utilise the reams of data in order to make smarter decisions. AI is the core element of 4IR as well.

However, despite AI’s potential to transform production, work and society, research has emerged about disparities and biases in the way this technology is utilised. Specifically, some believe this is because the people building AI systems are not representative of the communities those systems are meant to serve.

 

 

To begin with, AI developers are predominantly male. A review published by the AI Now Institute found that less than 20% of the researchers applying to prestigious AI conferences are women and that only a quarter of undergraduates studying AI at Stanford University and the University of California at Berkeley are female.

 

 

Although AI has strengthened the global manufacturing & industrialisation revolutions that raise the global income levels and improve quality of life. Recent research by Stanford University has found that the lack of diversity in AI development – based on race and ethnicity - risks creating an uneven distribution of power in the workforce and reinforces existing inequalities generated by AI systems, contributing to unjust outcomes. For example, in medical diagnosis where AI is trained and developed using biased data sets, misdiagnosis is increasingly likely, putting lives at risk. Caroline Criado Perez, author of ‘Invisible Women: Exposing Data Bias in a World Designed for Men’, explains that medical research is already systematically skewed towards men, whereby female heart attacks are routinely misdiagnosed because the "typical" heart attack symptoms are only typical to men.

 

 

Tackling Diversity Challenges

 

 

There is no easy fix to eradicate bias in AI research, but system-wide changes aimed at creating safe and inclusive spaces that support and promote researchers from underrepresented groups could play an important part. For example, both business and educational institutions need to understand and counter the pervasive stereotypes around gender and build a teaching environment that improves the confidence of girls.

 

 

In some parts of the world, government-level policy interventions are helping to drive this change. In the United Arab Emirates, the government recently organised the third edition of the Artificial Intelligence Program to provide both male and female participants with practical experience and skills in AI. In the United Kingdom, the government provided a £13.5m investment to boost diversity in AI roles through new conversion degree courses.

 

 

Still, on a global scale, there is very little government guidance on how AI developers should approach the issue of diversity in AI. The global manufacturing & industrialisation summit (GMIS) 2021, will highlight the way of leveraging AI’s ability to unlock meaningful data. This means that ensuring a balanced mixture of nationalities, ethnicities and genders when implementing AI will fall on the business implementing AI systems. Recognising this, tech companies need to re-examine their teams to ensure a multicultural and gender-balanced workforce and consider the impact of diversity issues on consumers.

 

 

The importance of having a vibrant corporate culture driven by clearly defined core values, such as inclusion, to ensure a motivated and capable workforce was brought into focus in the past year during the COVID-19 pandemic. To return again into the market Global Manufacturing & Industrialisation Summit (GMIS) 2021 will convene industry experts to discuss the best ways to prepare for the post Covid-19 recovery. As markets faltered, traditional organisational models were challenged, and only the nimble, able, and innovative companies succeeded during the crisis. Consequently, diversity and inclusion are at the heart of organisational success, the pace of innovation, social mobility and equality.

 

 

However, to make a real difference in addressing this issue, broader scale interventions are needed to change the image of who can work in AI. In essence, a “value system” that is imparted onto AI machines is needed to prevent bias from creeping into AI algorithms. According to Mo Gawdat, Chief Business Officer of Google X, “We need to act now to shape AI in order to ensure its positive contribution in the future, as once these machines are smarter than us in all domains – which is predicted to happen within the next 15 years – it might be too late for us to contain or control how the machines will behave.” Therefore, it is crucial that when building AI systems, the wider societal context is accounted for, and those building the technology reflect the communities they are building it for.