Bias  Misinformation and Disinformation: Mental Health  Employment and Human Computer Interaction

Martin, Jennifer; McKay, Elspeth; Shankar, Janki

doi:10.28945/3016

Cited by 2 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An evaluation of an automated telephony mental health care system guided HCI included the findings that users preferred a system that sounded and spoke like a human-professional rather than a machine [10]. Information and communication technologies (ICTs) were primarily designed for commercial purposes-there were suggestions of bias, misinformation and disinformation because no web-based technologies and designs met the needs of people recovering from a severe mental illness who are seeking employment (codesign for HCI and mental health innovation were generally lacking for marginalized populations) [11]. But young people were very satisfied with internet-based self-help [12].…”

Section: Modern Developments In Human-computer Interaction and Digita...mentioning

confidence: 99%

Human-Computer Interaction in Digital Mental Health

Balcombe

Leo

2022

Informatics

View full text Add to dashboard Cite

Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have a good potential to improve prediction, identification, coordination, and treatment by mental health care and suicide prevention services. AI is driving web-based and smartphone apps; mostly it is used for self-help and guided cognitive behavioral therapy (CBT) for anxiety and depression. Interactive AI may help real-time screening and treatment in outdated, strained or lacking mental healthcare systems. The barriers for using AI in mental healthcare include accessibility, efficacy, reliability, usability, safety, security, ethics, suitable education and training, and socio-cultural adaptability. Apps, real-time machine learning algorithms, immersive technologies, and digital phenotyping are notable prospects. Generally, there is a need for faster and better human factors in combination with machine interaction and automation, higher levels of effectiveness evaluation and the application of blended, hybrid or stepped care in an adjunct approach. HCI modeling may assist in the design and development of usable applications, and to effectively recognize, acknowledge, and address the inequities of mental health care and suicide prevention and assist in the digital therapeutic alliance.

show abstract

Section: Modern Developments In Human-computer Interaction and Digita...mentioning

confidence: 99%

Human-Computer Interaction in Digital Mental Health

Balcombe

Leo

2022

Informatics

View full text Add to dashboard Cite

show abstract

“…A large body of work in HCI argues for careful considerations of issues of bias in the design of technology [16,27,20]. Biases, implicit or explicit, frequently limit opportunities for certain groups [51,32]. Developing solutions to mitigate bias is currently a major challenge in AI [6,1,38,35,4].…”

Section: Related Workmentioning

confidence: 99%

Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism

Mallari

Inkpen

Johns

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

Understanding how racial information impacts human decision making in online systems is critical in today's world. Prior work revealed that race information of criminal defendants, when presented as a text field, had no significant impact on users' judgements of recidivism [13]. We replicated and extended this work to explore how and when race information influences users' judgements, with respect to the saliency of presentation. Our results showed that adding photos to the race labels had a significant impact on recidivism predictions for users who identified as female, but not for those who identified as male. The race of the defendant also impacted these results, with black defendants being less likely to be predicted to recidivate compared to white defendants. These results have strong implications for how systemdesigners choose to display race information, and cautions researchers to be aware of gender and race effects when using Amazon Mechanical Turk workers.

show abstract

Bias Misinformation and Disinformation: Mental Health Employment and Human Computer Interaction

Cited by 2 publications

References 44 publications

Human-Computer Interaction in Digital Mental Health

Human-Computer Interaction in Digital Mental Health

Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism

Contact Info

Product

Resources

About