How much does software design impact the way women and men perform tasks? Seems there is a gender bias. A study found the amount of thinking required (cognitive load), aesthetics, and emotional design could affect task performance. The level of usability, however, has little significance when it comes to gender.
Gender is not factored into the design for usability or performing tasks. Female users are poorly represented in software development which means males are designing for themselves.
Language processing and visual perception are notably different in females and males. Technology applications usually need additional cognitive processing determined by emotional perception. They also need retained working and memory details. So if men are the ones designing software, they will build in a bias towards their cognitive strengths. Consequently, women deal with increased cognitive load when using software applications.
Reducing gender bias in software design
The study introduces the key theories and the study design. Twenty-three females and seventeen males were participants in the study. Statistical analyses support the findings. Cognitive load and emotional design was found to have more of an impact than aesthetics for females. Consequently, software design should aim to reduce cognitive load. Men were not significantly impacted on either variable indicating the design suited their them – hence the bias.
Stereotypes have a major role to play so particular colour schemes, icons and language are ineffective. Minimalist design principles are recommended to minimise distraction to keep attention on the task. Another recommendation is to make it clear what the next step in the task is the sequence. The key point is to integrate psychological and biological differences into technology applications.
Most software designers are men, while women are usually linked with the aesthetic aspect of software design.
The title of the paper is, Is software design gender biased? A study on software-design effect on task performance. This study is a step toward debunking previous assumptions that explain female task performance. The author makes a note about gender diversity.
From the abstract
Software design is critical to the development of software, but literature suggests a gender bias. This bias might be causing differences in task performance between males and females. Applying cognitive load theory, emotional design theory and Aesthetic-Usability Effect we explore the differences between women and men.
The study was performed on two groups that possessed comparable educational backgrounds and professional experiences. The investigation encompassed two tasks aimed at evaluating performance in both professional and domestic contexts.
The study identified disparities among females, including high perception of cognitive load and lack of emotional design. It emphasizes the importance of incorporating phycological cognitive differences in design and ensuring inclusive design personas in software development.
Addressing the cognitive and emotional aspects of software design will reduce task performance discrepancies. It shifts the misbelief that task performance discrepancies are attributable to gender-based intellectual differences, rather than deficiencies in software design.
Overcoming bias in AI
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.
Politicians and planners make frequent calls for older Australians to give up their three bedroom homes to make way for “working families”. They expect them to move into apartments. But is urban density the answer? Regardless of the ageist inference that older people are “hogging all the houses”, with the political focus on working families, little room is left to discuss the housing needs of older cohorts. 
This is a major work by Bruce Judd, Diana Olsberg, Joanne Quinn and Oya Demirbilek (2010). It challenges the often held assumption that older people are “taking up space” in big houses that they no longer need – assumptions that their homes are “underoccupied”. 
Three bedrooms and urban living are what most older people want. These are two of the key findings in a new Australian report from AHURI. Age specific housing is not a preference. So researchers suggest more innovation to attract the older cohort so they can age in place after all. 
What do government representatives think is the best way to supply homes suitable for people with disability? Mandating accessible features in all new mainstream housing is the way to go. That means both owners and renters would benefit. Plus accessible housing would suit ageing in place and not be detrimental to the rest of the population.
This article was written when the Australian Building Codes Board called for responses to their Options Paper on Accessible Housing in 2017. Using facts and figures, she challenges the many false assumptions that the industry and others hold about the need for universal design in housing. She also covers assumptions about costs. So yes, it’s possible.
If you ask an older person if their home will suit them in their later years, they are likely to say yes. But how will they know and will they find out when it’s too late? That is the key issue when policy makers talk about ageing in place. Are we actually prepared for it? And not only are they people’s homes, they are potentially the workplaces for care service staff. So what next for an ageing Australia?
A significant number of people find certain aspects of the built environment uncomfortable, distressing or a barrier due to neurological differences. To address this, the British Standards Institute has a guide for designing built environments to include people who are neurodivergent. The whole population is neurodiverse, individuals might be neurotypical or neurodivergent. 
Design for the Mind