How Can the Law Address the Effects of Algorithmic Bias in the Healthcare Context?
This paper examines how UK ‘hard laws’ can adapt to regulate algorithmic bias in the healthcare context. I explore the causes of algorithmic bias which sets the foundation for how the law will address this issue. I critically analyse elements of the tort of negligence, the Equality Act 2010, and the Medical Devices Regulations 2002 which reveal the inadequacies of these frameworks in their application to algorithmic bias. Following this, I make recommendations on how the law can adjust to ensure that algorithms do not perpetuate existing biases and discriminate against patients. This paper acknowledges that addressing algorithmic bias will involve a mixture of hard and soft law measures, but in the final section, it will be argued that urgent systemic change (data sharing and workplace diversity) is also needed to enable the law to address the effects of algorithmic bias in the healthcare context.
This paper examines how UK ‘hard laws’ can adapt to regulate algorithmic bias in the healthcare context. I explore the causes of algorithmic bias which sets the foundation for how the law will address this issue. I critically analyse elements of the tort of negligence, the Equality Act 2010, and the Medical Devices Regulations 2002 which reveal the inadequacies of these frameworks in their application to algorithmic bias. Following this, I make recommendations on how the law can adjust to ensure that algorithms do not perpetuate existing biases and discriminate against patients. This paper acknowledges that addressing algorithmic bias will involve a mixture of hard and soft law measures, but in the final section, it will be argued that urgent systemic change (data sharing and workplace diversity) is also needed to enable the law to address the effects of algorithmic bias in the healthcare context.
Executive Summary
This article examines the regulation of algorithmic bias in the UK healthcare context, highlighting the inadequacies of current frameworks such as the tort of negligence, the Equality Act 2010, and the Medical Devices Regulations 2002. The author recommends a combination of hard and soft law measures to address algorithmic bias, emphasizing the need for systemic change, including data sharing and workplace diversity. The article provides a comprehensive analysis of the causes and effects of algorithmic bias in healthcare and proposes urgent reforms to ensure that algorithms do not perpetuate existing biases and discriminate against patients.
Key Points
- ▸ Algorithmic bias in the healthcare context is a significant concern
- ▸ Current UK laws and regulations are inadequate in addressing algorithmic bias
- ▸ A combination of hard and soft law measures is necessary to address algorithmic bias
Merits
Comprehensive Analysis
The article provides a thorough examination of the causes and effects of algorithmic bias in the healthcare context, setting a solid foundation for proposed reforms.
Demerits
Limited International Perspective
The article primarily focuses on the UK context, which may limit its applicability to other jurisdictions, and does not provide a comprehensive international comparison of regulatory approaches to algorithmic bias.
Expert Commentary
The article provides a timely and insightful analysis of the need for regulatory reform to address algorithmic bias in the healthcare context. The author's recommendations for a combination of hard and soft law measures, including systemic changes such as data sharing and workplace diversity, are well-reasoned and practical. However, the article could benefit from a more comprehensive international perspective, as well as a detailed analysis of the potential challenges and obstacles to implementing these reforms. Overall, the article makes a significant contribution to the ongoing debate about the regulation of AI in healthcare and highlights the need for urgent action to address the effects of algorithmic bias.
Recommendations
- ✓ Develop and implement new regulations and guidelines to address algorithmic bias in the healthcare context
- ✓ Encourage data sharing and collaboration between healthcare providers and technology companies to improve the development and deployment of AI-powered decision-making tools