Digital transformation depends on diversity – TechCrunch

Across industries, companies are now technology and data companies. The sooner they understand and experience this, the faster they will meet the needs and expectations of their customers, create more business value and grow. It is increasingly important to re-imagine the business and use digital technologies to create new business processes, cultures, customer experiences and opportunities.

One of the myths about digital transformation is that it’s about harnessing technology. It’s not. To be successful, digital transformation requires and is inherently based on diversity. Artificial intelligence (AI) is the result of human intelligence, made possible by its vast talents and also sensitive to its limits.

Therefore, it is imperative for organizations and teams to make diversity a priority and think about it beyond the traditional sense. For me, diversity revolves around three key pillars.

People

People are the most important part of artificial intelligence; the point is, humans create artificial intelligence. The diversity of people – the team of decision makers in the creation of AI algorithms – must reflect the diversity of the general population.

It goes beyond ensuring opportunities for women in AI and tech roles. In addition, it includes all dimensions of gender, race, ethnicity, skills, experience, geography, education, perspectives, interests and more. Why? When you have diverse teams reviewing and analyzing data to make decisions, you mitigate the risks that their own individual and uniquely human experiences, privileges, and limitations will blind them to the experiences of others.

Collectively, we have the opportunity to apply AI and machine learning to power the future and do good. It starts with diverse teams of people who reflect all the diversity and rich perspectives of our world.

The diversity of skills, perspectives, experiences and geographies has played a key role in our digital transformation. At Levi Strauss & Co., our growth strategy and AI team doesn’t just include data and machine learning scientists and engineers. We recently brought in people from across the organization across the globe and made a conscious effort to train people with no previous coding or statistics experience. We took people in retail operations, distribution centers and warehouses, and in design and planning and put them through our very first machine learning boot camp, building on their expert retail skills and overloading them with coding and statistics.

We haven’t limited the backgrounds required; we simply sought out people who were curious about problem solving, analytical by nature, and persistent in looking for different ways of approaching business issues. The combination of existing retail expert skills and additional machine learning knowledge has enabled employees graduating from the program to now have meaningful new perspectives in addition to their business value. This one-of-a-kind initiative in the retail industry has helped us develop a talented and diverse team of team members.

Data

Artificial intelligence and machine learning capabilities are only as good as the data fed into the system. We often limit ourselves to thinking of data in terms of structured tables – numbers and digits – but data is all that can be digitized.

The digital images of the jeans and jackets that our company has produced for 168 years are data. Customer service conversations (recorded only with permissions) are data. Heat maps of how people move through our stores are data. Our consumers’ opinions are data. Today, everything that can be digitized becomes data. We need to broaden the way we think about data and make sure that we are constantly integrating all data into the work of AI.

Most predictive models use data from the past to predict the future. But with the apparel industry still in the infancy of digital, data, and AI adoption, having past data to reference is often a common problem. In fashion, we anticipate trends and demand for entirely new products, which have no sales history. How do we do that?

We are using more data than ever before, for example, both images of new products and a database of our products from past seasons. We then apply computer vision algorithms to detect similarities between old and new fashion products, which helps us predict the demand for these new products. These apps provide much more accurate estimates than experience or intuition, supplementing past practices with predictions based on data and AI.

At Levi Strauss & Co., we also use digital images and 3D resources to simulate the feel of clothes and even create new fashion. For example, we train neural networks to understand the undertones of various styles of jeans like tapered legs, mustache patterns, and distressed looks, and detect the physical properties of components that affect drapes, pleats, and pleats. We are then able to combine this with market data, where we can tailor our product collections to meet the changing needs and wants of consumers and focus on our brand inclusiveness across demographics. In addition, we use AI to create new styles of clothing while always maintaining the creativity and innovation of our world-class designers.

Tools and techniques

In addition to people and data, we must ensure the diversity of tools and techniques that we use in the creation and production of algorithms. Some AI systems and products use classification techniques, which can perpetuate gender or racial biases.

For example, classification techniques assume that gender is binary and generally refer to people as “male” or “female” based on their physical appearance and stereotypical assumptions, which means that all other forms of gender identity. genre are deleted. This is a problem, and it is up to all of us who work in this space, in any business or industry, to prevent prejudice and advance techniques in order to capture all the nuances and ranges of life. people. For example, we can remove race from the data to try to blind an algorithm to race while still protecting against bias.

We value the diversity of our AI products and systems and to achieve this we use open source tools. Open source tools and libraries are by nature more diverse as they are accessible to everyone in the world and people from all walks of life and fields are working to improve and advance them, enriching them with their experiences and thus limiting bias.

An example of how we do this at Levi Strauss & Company is our US loyalty program Red Tab. When fans create their profiles, we don’t ask them to choose a gender or allow the AI ​​system to speculate. Instead, we ask them to choose their style preferences (Women, Men, Both, or Don’t know) to help our AI system create tailored shopping experiences and product recommendations. more personalized.

The diversity of people, data, techniques and tools is helping Levi Strauss & Co. revolutionize its business and our industry as a whole, turning manual into automated, analog into digital, and intuitive into predictive. We also build on the legacy of social values ​​of our company, which has championed equality, democracy and inclusion for 168 years. Diversity in AI is one of the last opportunities to carry on this legacy and shape the future of fashion.