ReviewiScience2021
Ethical machines: The human-centric use of artificial intelligence.
Review in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Improving definitions and innovations for identification and prevention of postoperative opioid-induced respiratory depression (OIRD): Proceedings of the International Consensus Conference.Journal of clinical anesthesia · 2026Article
- Attitudes and readiness to adopt artificial intelligence among healthcare practitioners in Pakistan's resource-limited settings.BMC health services research · 2025Article
- An Ontology Proposal for Implementing Digital Twins in Hospitality: The Case of Front-End Services.Sensors (Basel, Switzerland) · 2025Article
- Incorporating end-user perspectives into the development of a machine learning algorithm for first time perinatal depression prediction.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Structuring privacy policy: an AI approach.Frontiers in artificial intelligence · 2025Article
- We're only human after all: a critique of human-centred AI.AI & society · 2025Article
- Integration of Artificial Intelligence Decision Aids to Reduce Workload and Enhance Efficiency in Thyroid Nodule Management.JAMA network open · 2023Article
- Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review.Journal of medical Internet research · 2023Article
- Human-centricity in AI governance: A systemic approach.Frontiers in artificial intelligence · 2023Article
- Harnessing human and machine intelligence for planetary-level climate action.npj climate action · 2023Review
- Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies.Journal of medical Internet research · 2022Article
- Trustworthy human-AI partnerships.iScience · 2021Review
- "The Flow in the Funnel": Modeling Organizational and Individual Decision-Making for Designing Financial AI-Based Systems.Frontiers in psychology · 2021Article
- Leveraging artificial intelligence to combat antimicrobial resistance in geriatric care.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Today's increased availability of large amounts of human behavioral data and advances in artificial intelligence (AI) are contributing to a growing reliance on algorithms to make consequential decisions for humans, including those related to access to credit or medical treatments, hiring, etc. Algorithmic decision-making processes might lead to more objective decisions than those made by humans who may be influenced by prejudice, conflicts of interest, or fatigue. However, algorithmic decision-making has been criticized for its potential to lead to privacy invasion, information asymmetry, opacity, and discrimination. In this paper, we describe available technical solutions in three large areas that we consider to be of critical importance to achieve a human-centric AI: (1) privacy and data ownership; (2) accountability and transparency; and (3) fairness. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy makers, and citizens to co-develop and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness, accountability, and transparency while respecting privacy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.