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  1. When the Automated fire Backfires: The Adoption of Algorithm-based HR Decision-making Could Induce Consumer’s Unfavorable Ethicality Inferences of the Company.Chenfeng Yan, Quan Chen, Xinyue Zhou, Xin Dai & Zhilin Yang - 2023 - Journal of Business Ethics 190 (4):841-859.
    The growing uses of algorithm-based decision-making in human resources management have drawn considerable attention from different stakeholders. While prior literature mainly focused on stakeholders directly related to HR decisions (e.g., employees), this paper pertained to a third-party observer perspective and investigated how consumers would respond to companies’ adoption of algorithm-based HR decision-making. Through five experimental studies, we showed that the adoption of algorithm-based (vs. human-based) HR decision-making could induce consumers’ unfavorable ethicality inferences of the company (study 1); because implementing a (...)
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  • Subjectivity of Explainable Artificial Intelligence.Александр Николаевич Райков - 2022 - Russian Journal of Philosophical Sciences 65 (1):72-90.
    The article addresses the problem of identifying methods to develop the ability of artificial intelligence (AI) systems to provide explanations for their findings. This issue is not new, but, nowadays, the increasing complexity of AI systems is forcing scientists to intensify research in this direction. Modern neural networks contain hundreds of layers of neurons. The number of parameters of these networks reaches trillions, genetic algorithms generate thousands of generations of solutions, and the semantics of AI models become more complicated, going (...)
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  • Ethical Redress of Racial Inequities in AI: Lessons from Decoupling Machine Learning from Optimization in Medical Appointment Scheduling.Robert Shanklin, Michele Samorani, Shannon Harris & Michael A. Santoro - 2022 - Philosophy and Technology 35 (4):1-19.
    An Artificial Intelligence algorithm trained on data that reflect racial biases may yield racially biased outputs, even if the algorithm on its own is unbiased. For example, algorithms used to schedule medical appointments in the USA predict that Black patients are at a higher risk of no-show than non-Black patients, though technically accurate given existing data that prediction results in Black patients being overwhelmingly scheduled in appointment slots that cause longer wait times than non-Black patients. This perpetuates racial inequity, in (...)
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  • What Bias Management Can Learn From Change Management? Utilizing Change Framework to Review and Explore Bias Strategies.Mai Nguyen-Phuong-Mai - 2021 - Frontiers in Psychology 12.
    This paper conducted a preliminary study of reviewing and exploring bias strategies using a framework of a different discipline: change management. The hypothesis here is: If the major problem of implicit bias strategies is that they do not translate into actual changes in behaviors, then it could be helpful to learn from studies that have contributed to successful change interventions such as reward management, social neuroscience, health behavioral change, and cognitive behavioral therapy. The result of this integrated approach is: current (...)
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  • Why Human Prejudice is so Persistent: A Predictive Coding Analysis.Tzu-Wei Hung - 2023 - Social Epistemology 37 (6):779-797.
    Although the relationship between prejudice and predictive coding has attracted more attention recently, many important issues remain to be investigated, such as why prejudice is so persistent and how to accommodate seemingly conflicting studies. In this paper, we offer an integrated framework to explain the functional-computational mechanism of prejudice. We argue that this framework better explains (i) why prejudice is somewhat immune to revision, (ii) how inconsistent processing (e.g. one’s moral belief and biased emotional reaction) may occur, (iii) the dispute (...)
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  • On the person-based predictive policing of AI.Tzu-Wei Hung & Chun-Ping Yen - 2020 - Ethics and Information Technology 23 (3):165-176.
    Should you be targeted by police for a crime that AI predicts you will commit? In this paper, we analyse when, and to what extent, the person-based predictive policing (PP) — using AI technology to identify and handle individuals who are likely to breach the law — could be justifiably employed. We first examine PP’s epistemological limits, and then argue that these defects by no means refrain from its usage; they are worse in humans. Next, based on major AI ethics (...)
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  • Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda.Anna Lena Hunkenschroer & Christoph Luetge - 2022 - Journal of Business Ethics 178 (4):977-1007.
    Companies increasingly deploy artificial intelligence technologies in their personnel recruiting and selection process to streamline it, making it faster and more efficient. AI applications can be found in various stages of recruiting, such as writing job ads, screening of applicant resumes, and analyzing video interviews via face recognition software. As these new technologies significantly impact people’s lives and careers but often trigger ethical concerns, the ethicality of these AI applications needs to be comprehensively understood. However, given the novelty of AI (...)
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  • Achieving Equity with Predictive Policing Algorithms: A Social Safety Net Perspective.Chun-Ping Yen & Tzu-Wei Hung - 2021 - Science and Engineering Ethics 27 (3):1-16.
    Whereas using artificial intelligence (AI) to predict natural hazards is promising, applying a predictive policing algorithm (PPA) to predict human threats to others continues to be debated. Whereas PPAs were reported to be initially successful in Germany and Japan, the killing of Black Americans by police in the US has sparked a call to dismantle AI in law enforcement. However, although PPAs may statistically associate suspects with economically disadvantaged classes and ethnic minorities, the targeted groups they aim to protect are (...)
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  • Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference”.Eleanor Drage & Kerry Mackereth - 2022 - Philosophy and Technology 35 (4):1-25.
    In this paper, we analyze two key claims offered by recruitment AI companies in relation to the development and deployment of AI-powered HR tools: (1) recruitment AI can objectively assess candidates by removing gender and race from their systems, and (2) this removal of gender and race will make recruitment fairer, help customers attain their DEI goals, and lay the foundations for a truly meritocratic culture to thrive within an organization. We argue that these claims are misleading for four reasons: (...)
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  • Artificial Intelligence Regulation: a framework for governance.Patricia Gomes Rêgo de Almeida, Carlos Denner dos Santos & Josivania Silva Farias - 2021 - Ethics and Information Technology 23 (3):505-525.
    This article develops a conceptual framework for regulating Artificial Intelligence (AI) that encompasses all stages of modern public policy-making, from the basics to a sustainable governance. Based on a vast systematic review of the literature on Artificial Intelligence Regulation (AIR) published between 2010 and 2020, a dispersed body of knowledge loosely centred around the “framework” concept was organised, described, and pictured for better understanding. The resulting integrative framework encapsulates 21 prior depictions of the policy-making process, aiming to achieve gold-standard societal (...)
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