Threat of racial and economic inequality increases preference for algorithm decision-making

Bigman, Yochanan E.; Yam, Kai Chi; Marciano, Déborah; Reynolds, Scott J.; Gray, Kurt

doi:10.1016/j.chb.2021.106859

Cited by 33 publications

(21 citation statements)

References 57 publications

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“…We suggest that the implemented AI recommendation did not work as assumed because of algorithmic aversion (Berger et al, 2021 ; Dietvorst et al, 2015 ; Ochmann et al, 2021 ). Although algorithmic aversion usually occurs if people with domain knowledge can select between a human and an algorithm recommendation (Dietvorst et al, 2015 ), some previous research has suggested that there are cases of algorithm aversion occurring even if there is no human recommender alternative (Bigman et al, 2021 ). The qualitative analysis also provides evidence for this, suggesting that participants with domain knowledge in HR relied less on the AI recommendations compared to participants without domain knowledge.…”

Section: Discussionmentioning

confidence: 99%

Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring

et al. 2022

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Assuming that potential biases of Artificial Intelligence (AI)-based systems can be identified and controlled for (e.g., by providing high quality training data), employing such systems to augment human resource (HR)-decision makers in candidate selection provides an opportunity to make selection processes more objective. However, as the final hiring decision is likely to remain with humans, prevalent human biases could still cause discrimination. This work investigates the impact of an AI-based system’s candidate recommendations on humans’ hiring decisions and how this relation could be moderated by an Explainable AI (XAI) approach. We used a self-developed platform and conducted an online experiment with 194 participants. Our quantitative and qualitative findings suggest that the recommendations of an AI-based system can reduce discrimination against older and female candidates but appear to cause fewer selections of foreign-race candidates. Contrary to our expectations, the same XAI approach moderated these effects differently depending on the context.

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Section: Discussionmentioning

confidence: 99%

Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring

et al. 2022

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“…Results from empirical studies provide some support for this approach. Drawing people's attention to racial inequalities in medical outcomes increased support for bias‐reduction interventions such as the use of algorithm decision‐making during the triage process in hospitals (Bigman et al., 2021). Other studies show that people do not perceive affirmative action to violate principles of fairness when they were presented with persuasive evidence of discrimination against the beneficiary group (Son Hing et al., 2002).…”

Section: Threat and Opposition To Dei Policiesmentioning

confidence: 99%

Understanding advantaged groups' opposition to diversity, equity, and inclusion (DEI) policies: The role of perceived threat

Iyer

2022

Social & Personality Psych

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To combat social inequality, organizations develop and implement initiatives that seek to improve the status of disadvantaged groups (e.g., women, racial/ethnic minority groups). Such diversity, equity, and inclusion (DEI) policies are controversial, because people disagree about whether they are necessary and what their (positive and negative) consequences may be. Opposition can be particularly fierce from people who belong to advantaged groups that benefit from the status quo (e.g., men, racial/ethnic majority groups). Given the power wielded by advantaged groups, their opposition can undermine the successful implementation of DEI policies, thus resulting in continued inequality, wasted resources, and potential for tension in the organization. In this paper, I draw on theory and research to consider three types of threat that can explain advantaged groups' opposition to DEI policies: (1) resource threat, or concern about losing access to outcomes and opportunities; (2) symbolic threat, or concern about the introduction of new values, culture, and expectations; and (3) ingroup morality threat, or concern about their group's role in perpetuating inequality. I review strategies identified by the literature to

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“…A review of chatbots and conversational agents used in mental health found a small number of academic psychiatric studies with limited heterogeneitythere is a lack of high-quality evidence for diagnosis, treatment or therapy but there is a high potential for effective and agreeable mental health care if correctly and ethically implemented [95]. A major research constraint is that chatbots and predictive algorithms may be biased and perpetuate inequities in the underserved and the unserved [96][97][98][99]. The ethics of a patient-therapist relationship and the limited skills and emotional intelligence of chatbots requires a solution [100].…”

Section: Artificial Intelligencementioning

confidence: 99%

Human-Computer Interaction in Digital Mental Health

Balcombe

Leo

2022

Informatics

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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.

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Threat of racial and economic inequality increases preference for algorithm decision-making

Cited by 33 publications

References 57 publications

Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring

Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring

Understanding advantaged groups' opposition to diversity, equity, and inclusion (DEI) policies: The role of perceived threat

Human-Computer Interaction in Digital Mental Health

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