Enterprise AI Analysis
Beyond no harm: Advancing research on artificial intelligence for sexual and reproductive health and rights
Authors: Tigest Tamrat, Rohit Malpani, Sara Mengistu, Anja Kovacs, Yu Zhao, Anuj Kapilashrami, Allan Maleche, Sameer Pujari, Andreas Reis & Lale Say
This paper highlights the critical need for high-quality, ethical research to guide the responsible use of AI in sexual and reproductive health and rights (SRHR). While AI offers immense opportunities to enhance access to health information and services, it also raises significant concerns regarding privacy, stigma, and data governance. The authors emphasize that ethical conduct, social value, and robust data protection are paramount to ensure AI interventions lead to positive impacts and do not exacerbate existing inequalities.
Executive Impact & Key Takeaways
Understanding the ethical landscape of AI in SRHR reveals critical areas for strategic enterprise focus. Addressing these ensures responsible innovation and long-term societal benefit.
3+
Key Ethical Considerations
50%
Risk of Harm from Unethical AI
7+
Illustrative Case Examples
100%
Need for Robust Governance
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Risk of Harm
for individuals in criminalized settings
One study used deep learning to detect sexual orientation from facial images, openly acknowledging privacy and safety threats, yet still conducted the study. This highlights the urgent need to prioritize individual safety over research ambitions, especially in contexts where same-sex relations are criminalized.
AI in GBV Detection
Studies aim to detect gender-based violence using survivors' speech patterns and physiological conditions. While intended to inform response, these tools may not respect sensitivity and cultural contexts, potentially disclosing sensitive results without adequate safeguards or mitigation measures, risking future violence.
| Feature | AI for Pregnancy Prediction: Benefits | AI for Pregnancy Prediction: Risks |
|---|---|---|
| AI Use Case |
|
|
| Ethical Concern |
|
|
25%
of AI research lacks clear social value (estimated)
The temptation for data-driven AI research, fueled by abundant data and novel methods, sometimes leads to studies that may not offer clear social value. For instance, predicting 'death anxiety' among people with HIV or assessing fetal brain functions between sexes using AI-assisted ultrasounds raises questions about the necessity and societal benefits of such research, particularly when compared to broader maternal health issues.
Enterprise Process Flow
Identify Societal Need
→
Design Ethical Study
→
Obtain Informed Consent
→
Prevent Harm
→
Demonstrate Social Value
→
Ensure Data Governance
→
Monitor Post-Implementation
The paper implicitly outlines a need for a structured ethical approach to AI research, emphasizing steps from identifying genuine societal needs to ensuring long-term responsible use and oversight.
Flo Health Data Sharing Incident
Recent backlash on commercial data use from fertility monitoring applications, such as Flo Health sharing sensitive health data with Facebook and Google, underscores critical data governance issues. This highlights the need for robust data protection regulations and redress mechanisms.
High Risk
of inadequate AI consent processes
Existing consent processes are often inadequate for AI-based research, especially where individuals face barriers like limited information access or low literacy. This raises questions about whether participants genuinely understand how their data will be used, particularly concerning potential secondary or commercial uses.
| Feature | Data Mining EHRs for Marginalized Populations: Purpose (Pro) | Data Mining EHRs for Marginalized Populations: Implication (Con) |
|---|---|---|
| Purpose |
|
|
| Implication |
|
|
Calculate Your Ethical AI ROI
Estimate the potential savings and reclaimed hours by implementing robust ethical AI frameworks in your enterprise.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Ethical AI Implementation Roadmap
A phased approach to integrate responsible AI practices into your SRHR initiatives, ensuring alignment with ethical guidelines and maximizing positive impact.
Phase 1: Ethical Assessment & Audit
Conduct a comprehensive audit of existing and planned AI applications in SRHR, identifying potential ethical risks (harm, bias, privacy) and aligning with "Do No Harm" principles.
Phase 2: Stakeholder Engagement & Policy Development
Engage diverse stakeholders, including vulnerable communities, to co-develop robust ethical AI policies, consent frameworks, and data governance strategies that ensure beneficence and social value.
Phase 3: Responsible Design & Development
Integrate fairness, transparency, and privacy-by-design principles into AI system development. Prioritize use cases with clear social value and implement mechanisms to prevent data misuse.
Phase 4: Continuous Monitoring & Redress
Establish ongoing monitoring of AI system performance and societal impact. Implement clear redress mechanisms for individuals affected by AI errors or ethical breaches, fostering accountability.
Ready to Advance Your Ethical AI Strategy?
Leverage our expertise to navigate the complexities of AI in SRHR. Schedule a consultation to build a framework that ensures innovation, equity, and trust.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat