Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations
NIHR Birmingham Biomedical Research Centre · SickKids Foundation · +42 more institutions
Abstract
Without careful dissection of the ways in which biases can be encoded into artificial intelligence (AI) health technologies, there is a risk of perpetuating existing health inequalities at scale. One major source of bias is the data that underpins such technologies. The STANDING Together recommendations aim to encourage transparency regarding limitations of health datasets and proactive evaluation of their effect across population groups. Draft recommendation items were informed by a systematic review and stakeholder survey. The recommendations were developed using a Delphi approach, supplemented by a public consultation and international interview study. Overall, more than 350 representatives from 58…
Citation impact
- FWCI
- 11.81
- Percentile
- 100%
- References
- 61
Authors
50Topics & keywords
- Transparency (behavior)
- Data science
- Computer science
- Political science
- Computer security
Funding
- AHAmerican Heart Association
- GSGilead Sciences
- EREuropean Respiratory Society
- WTWellcome Trust
- NINational Institute for Health and Care Excellence
- GOGovernment of the United Kingdom
- URUK Research and Innovation
- NINational Institute for Health and Care Research
- TYTurun Yliopisto
- MEMoorfields Eye Hospital NHS Foundation Trust
- NNature
- NINational Institutes of HealthAwards: 75N92020C00021, 75N92020C00008
- MRMedical Research CouncilAward: MR/X005070/1
- EAEngineering and Physical Sciences Research Council
- EAEconomic and Social Research CouncilAward: ES/W012227/1
- REResearch England
- NINational Institute of Biomedical Imaging and BioengineeringAwards: 75N92020C00021, 75N92020C00008