Embedded EthiCS

Grosz, Barbara J.; Grant, David Gray; Vredenburgh, Kate; Behrends, Jeff; Hu, Lanlin; Simmons, A. John; Waldo, Jim

doi:10.1145/3330794

Cited by 172 publications

(42 citation statements)

References 9 publications

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“…44 In addition, it is critical that software engineers and data scientists consider the voices of diverse women and ethicists in collaborative activities about these new technologies to mitigate unintended consequences and prevent the exacerbation of disparities. 49,50…”

Section: Machine Learning and MMMmentioning

confidence: 99%

The Potential for Health Information Technology Tools to Reduce Racial Disparities in Maternal Morbidity and Mortality

Jean‐Francois

Lash

Dagher

et al. 2021

Journal of Women's Health

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Health information technology (health IT) potentially is a promising vital lever to address racial and ethnic, socioeconomic, and geographic disparities in maternal morbidity and mortality (MMM). This is especially relevant given that approximately 60% of maternal deaths are considered preventable. 1-36 Interventions that leverage health IT tools to target the underlying drivers of disparities at the patient, clinician, and health care system levels potentially could reduce disparities in quality of care throughout the continuum (antepartum, intrapartum, and postpartum) of maternity care. This article presents an overview of the research (and gaps) on the potential of health IT tools to document SDoH and community-level geocoded data in EHR-based CDS systems, minimize implicit bias, and improve adherence to clinical guidelines and coordinated care to inform multilevel (patient, clinician, system) interventions throughout the continuum of maternity care for health disparity populations impacted by MMM. Telemedicine models for improving access in rural areas and new technologies for risk assessment and disease management (e.g., regarding preeclampsia) also are discussed.

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Section: Machine Learning and MMMmentioning

confidence: 99%

The Potential for Health Information Technology Tools to Reduce Racial Disparities in Maternal Morbidity and Mortality

Jean‐Francois

Lash

Dagher

et al. 2021

Journal of Women's Health

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“…It is only recently that scholars have argued that since every computer-related subject carries social or ethical implications, then these ethical topics should be widely integrated and infused in Computer Science curricula (Harris et al, 2019;Weikle, 2018). For example, Grosz et al (2019) argue that modern technology cannot be considered "value-neutral" (p. 54); it can have unplanned consequences and that Computer Science students should be trained to identify the potential harmful effects of the technologies they help develop. An important concept that students need to be able to think about is not only whether they can create something, but whether they should create it in the first place.…”

Section: Literature Reviewmentioning

confidence: 99%

The teaching of computer ethics on computer science and related degree programmes. a European survey

Stavrakakis

Gordon

Tierney

et al. 2021

International Journal of Ethics Education

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Within the Computer Science community, many ethical issues have emerged as significant and critical concerns. Computer ethics is an academic field in its own right and there are unique ethical issues associated with information technology. It encompasses a range of issues and concerns including privacy and agency around personal information, Artificial Intelligence and pervasive technology, the Internet of Things and surveillance applications. As computing technology impacts society at an ever growing pace, there are growing calls for more computer ethics content to be included in Computer Science curricula. In this paper we present the results of a survey that polled faculty from Computer Science and related disciplines about teaching practices for computer ethics at their institutions. The survey was completed by respondents from 61 universities across 23 European countries. Participants were surveyed on whether or not computer ethics is taught to Computer Science students at each institution, the reasons why computer ethics is or is not taught, how computer ethics is taught, the background of staff who teach computer ethics and the scope of computer ethics curricula. This paper presents and discusses the results of the survey.

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“…One way to move in this direction is to allocate more time for generative discussion and debate within and outside organizations (IEEE, 2020) as well as increase the interactions with stakeholders. 17 Further measures can include changes in education and vocational training of AI practitioners to support them in the development of ethical skills [see for example Grosz et al, (2019)].…”

Section: Problem 2: How To Determine Acceptable Trade-offs Between Fairness Metrics and Other Relevant Variables?mentioning

confidence: 99%

Non-empirical problems in fair machine learning

Scantamburlo

2021

Ethics Inf Technol

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The problem of fair machine learning has drawn much attention over the last few years and the bulk of offered solutions are, in principle, empirical. However, algorithmic fairness also raises important conceptual issues that would fail to be addressed if one relies entirely on empirical considerations. Herein, I will argue that the current debate has developed an empirical framework that has brought important contributions to the development of algorithmic decision-making, such as new techniques to discover and prevent discrimination, additional assessment criteria, and analyses of the interaction between fairness and predictive accuracy. However, the same framework has also suggested higher-order issues regarding the translation of fairness into metrics and quantifiable trade-offs. Although the (empirical) tools which have been developed so far are essential to address discrimination encoded in data and algorithms, their integration into society elicits key (conceptual) questions such as: What kind of assumptions and decisions underlies the empirical framework? How do the results of the empirical approach penetrate public debate? What kind of reflection and deliberation should stakeholders have over available fairness metrics? I will outline the empirical approach to fair machine learning, i.e. how the problem is framed and addressed, and suggest that there are important non-empirical issues that should be tackled. While this work will focus on the problem of algorithmic fairness, the lesson can extend to other conceptual problems in the analysis of algorithmic decision-making such as privacy and explainability.

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Embedded EthiCS

Abstract: A Harvard-based pilot program integrates class sessions on ethical reasoning into courses throughout its computer science curriculum.

Cited by 172 publications

References 9 publications

The Potential for Health Information Technology Tools to Reduce Racial Disparities in Maternal Morbidity and Mortality

The Potential for Health Information Technology Tools to Reduce Racial Disparities in Maternal Morbidity and Mortality

The teaching of computer ethics on computer science and related degree programmes. a European survey

Non-empirical problems in fair machine learning

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