scholarly journals Experiments with Social Good: Feminist Critiques of Artificial Intelligence in Healthcare in India

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Radhika Radhakrishnan
Keyword(s):  
Artificial Intelligence ◽  
Feminist Theory ◽  
Medical Records ◽  
Underserved Populations ◽  
Low Cost ◽  
Regulatory Mechanisms ◽  
Healthcare Industry ◽  
Policy Recommendations ◽  
Social Good ◽  
Diagnostic Models

In contemporary India, AI-enabled automated diagnostic models are beginning to control who gets access to what kind of medical care, with the most invasive systems being aimed at underserved communities. I critically question the dominant narrative of “AI for social good” that has been widely adopted by various stakeholders in the healthcare industry towards solving development challenges through the introduction of AI applications targeted towards the sick-poor. Using feminist theory, I argue that AI systems should not be seen as neutral products but complex sociotechnical processes embedded with gendered knowledge and labor. I analyze the layers of expropriation and experimentation that come into play when AI technologies become a method of using diverse bodies and medical records of the sick-poor as data to train proprietary AI algorithms at a low cost in the absence of effective state regulatory mechanisms. I posit that an overwhelming focus on “spectacular technologies” such as AI derails public efforts from solving the actual needs of populations targeted by the “AI for social good” narrative, and from the development of sustainable, responsible, situated healthcare solutions. Lastly, I offer social and policy recommendations that would enable us to envision inclusive feminist futures in which we understand and prioritize the needs of underserved populations over capitalist market logics in the development, deployment, and regulation of AI systems.

scholarly journals Mapping value sensitive design onto AI for social good principles

AI and Ethics ◽  
2021 ◽  
Author(s):  
Steven Umbrello ◽  
Ibo van de Poel
Keyword(s):  
Artificial Intelligence ◽  
Contact Tracing ◽  
Use Case ◽  
Value Sensitive Design ◽  
Specific Design ◽  
Established Method ◽  
Social Good ◽  
Do No Harm ◽  
Design Requirements ◽  
Whole Life Cycle

AbstractValue sensitive design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, ML may lead to AI systems adapting in ways that ‘disembody’ the values embedded in them. To address this, we propose a threefold modified VSD approach: (1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; (2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good, and (3) extending the VSD process to encompass the whole life cycle of an AI technology to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app.

scholarly journals A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery

2021 ◽  
Vol 18 (5) ◽  
pp. 2713
Author(s):  
Ying-Jen Chang ◽  
Kuo-Chuan Hung ◽  
Li-Kai Wang ◽  
Chia-Hung Yu ◽  
Chao-Kun Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Records ◽  
Lung Resection ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Bayes Classifier ◽  
Integrated Risk Assessment ◽  
Patient Comprehension ◽  
Integrated Risk ◽  
Physician Patient

Assessment of risk before lung resection surgery can provide anesthesiologists with information about whether a patient can be weaned from the ventilator immediately after surgery. However, it is difficult for anesthesiologists to perform a complete integrated risk assessment in a time-limited pre-anesthetic clinic. We retrospectively collected the electronic medical records of 709 patients who underwent lung resection between 1 January 2017 and 31 July 2019. We used the obtained data to construct an artificial intelligence (AI) prediction model with seven supervised machine learning algorithms to predict whether patients could be weaned immediately after lung resection surgery. The AI model with Naïve Bayes Classifier algorithm had the best testing result and was therefore used to develop an application to evaluate risk based on patients’ previous medical data, to assist anesthesiologists, and to predict patient outcomes in pre-anesthetic clinics. The individualization and digitalization characteristics of this AI application could improve the effectiveness of risk explanations and physician–patient communication to achieve better patient comprehension.

Artificial intelligence and mass personalization of communication content—An ethical and literacy perspective

2021 ◽  
pp. 146144482110227
Author(s):  
Erik Hermann
Keyword(s):  
Artificial Intelligence ◽  
Conceptual Analysis ◽  
Need Satisfaction ◽  
Ethical Challenges ◽  
Social Good ◽  
Communication Content ◽  
Basic Understanding ◽  
Stakeholder Perspective ◽  
Multi Stakeholder ◽  
Mass Personalization

Artificial intelligence (AI) is (re)shaping communication and contributes to (commercial and informational) need satisfaction by means of mass personalization. However, the substantial personalization and targeting opportunities do not come without ethical challenges. Following an AI-for-social-good perspective, the authors systematically scrutinize the ethical challenges of deploying AI for mass personalization of communication content from a multi-stakeholder perspective. The conceptual analysis reveals interdependencies and tensions between ethical principles, which advocate the need of a basic understanding of AI inputs, functioning, agency, and outcomes. By this form of AI literacy, individuals could be empowered to interact with and treat mass-personalized content in a way that promotes individual and social good while preventing harm.

scholarly journals Controlling Safety of Artificial Intelligence-Based Systems in Healthcare

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 102
Author(s):  
Mohammad Reza Davahli ◽  
Waldemar Karwowski ◽  
Krzysztof Fiok ◽  
Thomas Wan ◽  
Hamid R. Parsaei
Keyword(s):  
Artificial Intelligence ◽  
Rating Scale ◽  
Black Box ◽  
Healthcare Industry ◽  
Emergency Plans ◽  
Controlling System ◽  
Box Models ◽  
Key Dimensions ◽  
Value Model ◽  
And Control

In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to address the AI black-box mystery in the healthcare industry. The main objective was to propose safety guidelines for implementing AI black-box models to reduce the risk of potential healthcare-related incidents and accidents. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four symmetrical parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. On the basis of the MAVT approach, three layers of attributes were created. The first level contained six key dimensions, the second level included 14 attributes, and the third level comprised 78 attributes. The key first level dimensions of the SCS included safety policies, incentives for clinicians, clinician and patient training, communication and interaction, planning of actions, and control of such actions. The proposed system may provide a basis for detecting AI utilization risks, preventing incidents from occurring, and developing emergency plans for AI-related risks. This approach could also guide and control the implementation of AI systems in the healthcare industry.

scholarly journals Cloud based mobile solution for early detection of Skin Cancer using Artificial Intelligence

2021 ◽  
pp. 312-324
Author(s):  
Pawan Sonawane ◽  
Sahel Shardhul ◽  
Raju Mendhe
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Skin Cancer ◽  
Early Detection ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Mobile Application ◽  
Low Cost ◽  
Skin Tumors

The vast majority of skin cancer deaths are from melanoma, with about 1.04 million cases annually. Early detection of the same can be immensely helpful in order to try to cure it. But most of the diagnosis procedures are either extremely expensive or not available to a vast majority, as these centers are concentrated in urban regions only. Thus, there is a need for an application that can perform a quick, efficient, and low-cost diagnosis. Our solution proposes to build a server less mobile application on the AWS cloud that takes the images of potential skin tumors and classifies it as either Malignant or Benign. The classification would be carried out using a trained Convolution Neural Network model and Transfer learning (Inception v3). Several experiments will be performed based on Morphology and Color of the tumor to identify ideal parameters.

scholarly journals Aryepiglottoplasty for Severe Laryngomalacia

2014 ◽  
Vol 4 (1) ◽  
pp. 10-12
Author(s):  
Akanksha Saxena ◽  
Madhumita Kumar ◽  
Bini Faizal
Keyword(s):  
Retrospective Review ◽  
Medical Records ◽  
Low Cost ◽  
Favorable Outcome ◽  
Medical Sciences ◽  
Common Cause

ABSTRACT Laryngomalacia is the most common cause of stridor in children below the age of 1 year. In majority of the cases it can be managed conservatively, but in severe cases intervention becomes necessary. Objectives To evaluate the outcome of aryepiglottoplasty (Cold steel method) in cases of severe laryngomalacia. Methods Retrospective. Review of medical records of 8 cases treated in Department of ENT, Amrita Institute of Medical Sciences from 2006 to 2011. Results Seven out of eight children had a favorable outcome. Conclusion Aryepiglottoplasty (Cold steel method) is an efficient, simple and low cost method for treating severe cases of laryngomalacia. How to cite this article Saxena A, Kumar M, Faizal B. Aryepiglottoplasty for Severe Laryngomalacia. Int J Phonosurg Laryngol 2014;4(1):10-12.

CSET Publishes AI Policy Recommendations for the Next Administration

2020 ◽  
Author(s):  
Andrew Imbrie ◽  
Ryan Fedasiuk ◽  
Tarun Chhabra ◽  
William Hannas ◽  
Dewey Murdick ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Policy Recommendations

CSET has prepared policy recommendations for the next presidential administration to consider in five areas key to U.S. leadership in artificial intelligence.

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