Category: ICT Research papers

Barriers to digital use are caused by a complex web of intersecting factors. Gender identity is just one of them. Age, education, socioeconomic status, race, physical and cognitive disadvantage all have a role to play. Focusing on one dimension of inclusion does not account for all the complexities. That’s the conclusion two researchers came to after looking at Microsoft, Apple, and Google websites.

The big software industry players have enthusiastically promoted their commitment to inclusive design. But how inclusive are they?

A paper discussing aspects of their inclusive practice from a gender perspective reveals that it is only a partial response. That’s because gender intersects with many other identities such as age, capability, and ethnicity.

“Regardless of efforts to promote inclusivity mainly in terms of gender, the intersectionality of identities is frequently overlooked and ignored in design.”

Microsoft, Apple, Google and Meta

The paper covers the issues of intersectionality and imagery, which is followed by tech industry case studies. Here is a brief overview:

Microsoft’s manual does a great job in explaining why there is a need for inclusive design. However, it is focused on disability and the images maintain the male/female binary. It covers theoretical and practical aspects.

Apple’s site has a detailed description of what the company thinks about inclusive design. It goes further than Microsoft on accessibility an introduces language use and stereotypes. Apple does not provide designers with many practical tools on how to do universal design.

Google acknowledges the need for equity and inclusion in their products by giving voice to the most underrepresented groups throughout the production process. They have a list of diversity segments for designers to consider.

Meta has implemented several key strategies in its design process to create inclusive products. The company is evolving to recognise the importance of accessible and inclusive products for all users. Meta conducts user research on a regular basis to gather feedback for improvements.

Guidelines for gender inclusion

The research resulted in guidelines for designing gender-inclusive tools in technology. They are applicable to both academia and industry. They are also useful for anyone responsible for the look, feel and accessibility of their organisation’s website and digital products.

• Consider intersectionality: avoid simplifying people to one-dimensional characters. People have complex identities, which go beyond belonging to one specific gender, race, or sexuality group.
• Avoid propagating stereotypes: attaching typical looks, occupations, and traits to a person based on their gender, race, or sexuality, contributes to social stereotypes that aggravate misogyny, racism, and homophobia.
• Overcome the gender-binary: avoid producing text and images that reinforce the gender binary and social stereotypes, regarding appearance, jobs, preferences, or skills.
• Make your text, tone, and imagery consistent and inclusive: it is necessary to maintain efforts for inclusivity throughout your copy, visuals, communication, and products.
• Show the diversity of each community: make a conscious effort to illustrate how multi-colored communities are, instead of simplifying them to stereotypes.
• Involve people with that particular identity: diversity and inclusion should be taken seriously. There’s no better person than the one with that particular identity to tell you about their concerns and challenges.
• Avoid concentrating on a single mode of communication: adapt your copy, images, and communication to different languages, cultures, and levels of complexity.
• Provide training in Diversity, Equity, and Inclusion: help your business or organization by providing constant training and mentorship.

The title of the paper is, Gender Inclusive Design in Technology: Case Studies and Guidelines, from the ResearchGate website.

From the abstract

The need for inclusion stems from the fact that the composition of the IT sector reflects a workforce that is not diverse enough. This can result in blind spots in the design process, leading to exclusionary user experiences. The idea of inclusive design is becoming more prevalent. In fact, it is becoming a general expectation to create software that is useful for and used by more people.

With a focus on intersectionality, inclusive user experience (UX) seeks to actively and consciously integrate minority, vulnerable, and understudied user groups in the design.

UX that is based on inclusive design and aims to overcome social disadvantages in all of their intersectional complexities. These arise from gender, sexual orientation, age, education, dis/ability, socioeconomic status, and race/ethnicity, among others. At the same time, gender-inclusive design has challenges and limitations: the idea of gender inclusion in design is not yet a reality.

Our research investigates academic literature, as well as tech industry practices, like the websites of Microsoft, Apple, Google, and Meta. Our analysis shows that intersectionality suffers even when inclusivity is considered. We also offer guidelines for factors that might be explored for a more inclusive design.

Artificial Intelligence (AI) is entering our everyday lives with increased speed and sometimes without our knowledge. But it is only as good as the data it is fed, and the worry about bias is a concern for marginalised groups. AI has the potential to enhance life for everyone, but that requires overcoming bias in AI development. In his article, Christopher Land argues for more advocacy and transparency in AI.

The power of machine learning comes from pattern recognition within vast quantities of data. Using statistics, AI reveals new patterns and associations that human developers might miss or lack the processing power to uncover.

Designing for the average is fraught with problems. Statistical averages do not translate to some kind of human average. That’s because statistics don’t measure human diversity. That’s why AI processes are at risk of leaving some people behind. But in gathering useful data there are some privacy issues.

AI shows great promise with robot assistants to assist people with disability and older people with everyday tasks. AI imaging and recognition tools help nonvisual users understand video and pictures.

Christopher Land outlines how AI and machine learning work and how bias is introduced into AI systems if not prevented. He also has some recommendations on strengthening legal protections for people with disability. The paper is not technical. Rather it explains clearly how it works, where it’s used, and what needs to be done.

The title of the article is, Disability Bias & New Frontiers in Artificial Intelligence. The “Black Box” issue is explained and the need for a “Glass Box” is presented.

From the abstract

Bias in artificial intelligence (AI) systems can cause discrimination against marginalized groups, including people with disabilities. This discrimination is most often unintentional and due to a lack of training and awareness of how to build inclusive systems.

This paper has two main objectives: 1) provide an overview of AI systems and machine learning, including disability bias, for accessibility professionals and related non-development roles; and 2) discuss methods for building accessible AI systems inclusively to mitigate bias.

Worldwide progress on establishing legal protection against AI bias is provided, with recommendations on strengthening laws to protect people with disabilities from discrimination by AI systems. When built accessibly, AI systems can promote fairness and enhance the lives of everyone, in unprecedented ways.

Diversity and inclusion in AI

An Australian book chapter takes a comprehensive and practical approach to how equity and inclusion should be considered throughout development. This should be done at both governance and development levels by applying inclusive design and human-centred design to the AI ‘ecosystem’.

The title of the chapter is Diversity and Inclusion in Artificial Intelligence.

The stereotype of grandchildren helping grandparents with their phone or remote controller is often perpetuated by older people themselves. Skill in using phones and websites depends on the reasons for using them. Younger people can have different interests from older adults meaning they use different apps and software. This doesn’t mean tech and older adults don’t belong together.

“Grandma cannot use her phone because it was not designed for her. Ubiquitous mass-market tools should not present obvious and avoidable hurdles to everyday users.” Robert Schumacher.

The stereotype is not based in evidence, and it might not be the tech that’s the barrier – poor vision or hand dexterity can also cause problems with using phones and computers.

It’s about mental models

According to Schumacher, the main difference between younger and older generations is when their mental models of how things work was formed. He explains how these mental models can widen any gap in understanding in how things work:

“Every generation has its own mental models of the world. In Tom Standage’s (1998) fascinating book The Victorian Internet, he provides several examples of how emerging technology scrambled everyone’s way of thinking. Take the introduction of the telegraph. In one example, a mother brought a bowl of sauerkraut to the telegraph office, insisting that they send it (across the wire) to her son on the battlefront. This mother, with good intentions, mixed up the atoms and the bits, which perhaps is understandable if you do not have a suitable mental model.”

Using this story we can see that many older adults will have mental models and beliefs of how things work. But they don’t align with the design models and systems. People who began working with computers and DOS systems in the 1980s have grown up with the evolution of technology. Their mental model has adjusted with each new development. But not everyone worked in an office with a computer.

Barriers for everyone

CAPTCHA requires good visual acuity, and multi-factor authentication (MFA) requires people to juggle more than one device and accounts. And access to healthcare is moving online with MFA requirements.

Electronic ticketing now requires a mobile app. The assumption is that the process is intuitive despite the many steps in the process. Downloading the app, getting the tickets by email, transferring to the app, retrieving and scanning the app at the gate. And access to a streaming service often requires a QR code – something else to learn.

Schumacher’s article discusses more on this topic and how to remedy the situation. More testing with older adults is essential. Their mental models aren’t the same as the developer’s – what’s intuitive for a seasoned tech user is not intuitive for everyone. However, it doesn’t mean older people are averse to using technology or too “stuck in their ways” to learn.

The title of the article is, Gran Got Tech: Inclusivity and Older Adults, published in the Journal of User Experience.

Mental models and autonomous vehicles

The concept of designing tech from the perspective of mental models is a factor in a research project for autonomous vehicles. As concepts evolve, eventually the need to design in-vehicle interfaces will be minimal with presets for each rider. In the meantime, touchscreens and audio controls will still be needed. These need to be co-designed with users to develop prototypes.

The title of a research paper on this topic is Designing Interaction with Autonomous Vhicles for Older Passengers.

Almost all designing is done for human use. Even designs for animals and plants link back to human use or human benefit in one way or another. So based on that premise, all design is human centred design. Well, that is one way to define it. 

In the world of technology the key question for human centred design approach is, what are people doing? Not, what can the technology do? Dr Peter Schumacher reminds us in a video below that humans were creating things for themselves in the Stone Age. However, digital technology isn’t crafted by hand like a stone axe where the maker is often the user. With artificial intelligence, technology can do amazing things. But are they the things that humans want?

Based on the Stone Age definition, Schumacher says human centred design is something we’ve been doing forever. From the moment we could, we’ve been making things we could use. We were creating technology for a purpose, for something we wanted to do. But technology, and the way we interact with it, is getting more complicated.

We are at a point where technology and the environment we live in is creating problems. Schumacher says that finding out what’s going on with humans and their relationship to technology is the key focus. It’s putting human back into the centre of the technology rather than saying what the technology can do.

So, you start with the question, “what are people doing?” This is followed by “what does the technology need to do in that context?” Then there is the question of what’s the right technology?

Human Centred Design Group

The Human Centred Design Lab at University of South Australia is focused on developing methods for the design of objects, environments, and systems for human use. They take a collaborative and trans-disciplinary approach to create products for human use. The Human Centred Design Group is a research centre for interactive and virtual environments. The video below explains a bit more and they invite other collaborators.

Also see their Design Clinic brings together researchers, industry and end users for co-design in healthcare. The Design Clinic can capture ideas and designs for new products, systems and services vital to the health and wellbeing sectors.