Membership & Diversity Committee News

AI in the biomedical field offers significant potential for advancing healthcare, yet it also presents challenges, particularly from a diversity and equity perspective.

Pros

Enhanced Diagnostics and Treatment: AI can improve diagnostic accuracy by analyzing vast amounts of data quickly leading to better health outcomes, especially in underserved areas with limited access to specialists.

Personalized Medicine: AI enables the development of personalized treatment plans based on individual genetic profiles, improve the effectiveness of treatments for diverse populations.

Operational Efficiency: AI can streamline administrative tasks, reducing the burden on healthcare professionals and allowing them to focus more on patient care.

Cons

Algorithmic Bias: AI systems can perpetuate existing biases if the data used to train them is not representative of diverse populations, further worsening health outcomes for minority groups.

Data Privacy Concerns: The use of AI in healthcare involves handling sensitive patient data, raising concerns about privacy and the potential for misuse.

Equity in Access: There is a risk that AI technologies may not be equally accessible to all populations, particularly those in low-resource settings. This can exacerbate existing health disparities.

While AI has the potential to revolutionize the biomedical field, addressing issues of diversity is crucial to ensure fair and equitable benefit of improved healthcare outcomes to all population groups.

Similarly, attorneys, law enforcement agencies, and judicial authorities have compelling reasons to implement AI to increase efficiency, perform research, automate tasks, and generate predictive insights. However, these applications are vulnerable to biased data, limited nuance, and opacity.

For example, AI could assist detectives with crime scene reconstruction by processing massive datasets and recognizing patterns that might escape human analysis. Yet, it remains limited by its inability to register subtleties and contextualize factors understood through human intervention. Similarly, overwhelmed court systems may adopt AI-driven tools to streamline sentencing or predict recidivism. However, studies show that some AI systems erroneously predict higher recidivism rates for Black defendants, revealing AI’s potential to perpetuate existing racial biases.

Finally, AI tools could negatively impact some law practices, such as intellectual property (IP). For instance, content recognition tools may inadvertently incorporate community-specific knowledge, furthering cultural appropriation. Additionally, using AI to assess IP eligibility may disadvantage underrepresented individuals who cannot afford to contest unfair decisions. The risks AI poses to law and policy analysis require conscientious adoption. To avoid reinforcing systemic inequalities, it is vital that AI-based tools are trained on robust, unbiased datasets and that the processes for generating content are transparent and explainable.

Sources

Julia Angwin, et. al., Machine Bias, ProPublica (May 23, 2016).

Surabhi Kosta et al., AI Revolutionizing Forensic Analysis: Enhancing Efficiency and Accuracy in Crime Investigation Engineering and Technology, 11 Int’l. Advanced Res. J. Sci. 226, 227–28 (May 2024).