Week 1: Principles, Ethics, & Practices

Sensory Bio: HOLM

1. The Big Idea: What & Why?

HOLM: Hormone-Linked Ocular Monitoring

The Why:
An often ignored and under-discussed impact of menstruation is its effect on ocular comfort and vision. Millions of women experience eye strain, blurry vision, dry eyes, and light sensitivity during different phases of their menstrual cycle. These symptoms are frequently dismissed with generic advice such as “rest your eyes,” despite being real, recurring, and disruptive.

The goal of this project is to transform subjective visual discomfort into measurable physiological data that can support research, awareness, and future interventions.

Note: This project does not provide recommendations or medical advice. It is designed solely to generate interpretive insights.

2. Governance Goals: Keeping it Ethical

The core ethical challenge is ensuring that physiological sensing and inference empowers users without causing harm, misuse, or exclusion. The governance goals are grouped into the following categories.

Ensure User Safety and Privacy

• Data Integrity and Accuracy:
  Physiological signals derived from sensors are inherently noisy, context-dependent, and sensitive to environmental factors (e.g., hydration, wind, lighting). Governance must require calibration standards, uncertainty quantification, and conservative interpretation thresholds to prevent misleading outputs or inappropriate self-management decisions.

• Data Abstraction:
  Raw biological data (e.g., cortisol concentrations or inflammatory markers) should be abstracted into high-level indices before storage or sharing to minimize re-identification and secondary misuse.

Bias, Transparency, and Accountability

• Algorithmic Fairness:
  Models must be evaluated across diverse physiological baselines and hormonal patterns to avoid bias, particularly against menstruating individuals who are often underrepresented in biomedical datasets.

• Controlled Access:
  Access to inferred hormonal or stress states must be restricted to the user unless explicit, informed consent is provided. Employers, insurers, or institutions should not have default access.

• Auditable Systems Design:
  Model versions, training data sources, and inference pathways should be logged to enable retrospective auditing and accountability.

• Explainable Design:
  Outputs must be interpretable and accompanied by explanations, confidence levels, and limitations to prevent over-reliance on algorithmic authority.

3. Governance Actions: The Game Plan

Action 1: Mandatory Data Privacy & Encryption

What this does:
Establishes baseline protections so sensitive physiological and inferred hormonal data cannot be misused or repurposed without consent.

How it works:

• End-to-end encryption for stored and transmitted data
• Data minimization using high-level indices by default
• Strict opt-in consent for any data sharing

Who is involved:
Device manufacturers, app developers, platform providers, and regulatory bodies.

Why it matters:
Hormonal and stress-related data are socially sensitive and require strong safeguards to prevent biological surveillance.

Action 2: Explainable and Auditable ML

What this does:
Reduces harm from opaque algorithmic decision-making.

How it works:

• Interpretable feature-level outputs
• Audit logs for model training and updates
• Confidence intervals on all user-facing results

Who is involved:
Researchers, ML engineers, ethics committees, journals, and funding agencies.

Why it matters:
Explainability supports trust, accountability, and safe interpretation.

Action 3: Inclusive Design Incentives

What this does:
Encourages systems that reflect real physiological diversity.

How it works:

• Incentives for diverse testing populations
• Documentation of dataset coverage and gaps
• Funding and publication advantages for inclusive design

Who is involved:
Funding agencies, academic institutions, standards bodies, and journals.

Why it matters:
Without incentives, biosensing tools risk reinforcing existing health inequities.

4. Governance Scoring Matrix

Scoring:
1 = Strongly advances goal
2 = Moderately advances goal
3 = Weakly advances goal

5. Prioritized Governance Strategy

Based on the scoring, a combined strategy prioritizing Action 1 (Privacy & Encryption) and Action 2 (Explainable ML) is recommended, with Action 3 (Inclusive Design) pursued in parallel as the system matures.

This balances feasibility, safety, accountability, and long-term equity.

6. Trade-Offs, Assumptions, and Uncertainties

Trade-Offs Considered

• Early-Stage Scope Limitation:
  Initial development focuses on cisgender women with endogenous menstrual cycles, excluding trans women and individuals using exogenous hormone therapies. This is a methodological constraint to ensure model validity, with broader inclusion planned in later phases.

• Risk of Over-Regulation:
  Excessive governance in early stages could slow exploratory research and iteration.

Key Assumptions

• Users value transparency and privacy over maximum predictive accuracy.
• Technical safeguards meaningfully reduce downstream misuse.
• Interpretive framing will be respected by developers and institutions.

Uncertainties

• Future secondary uses of inferred hormonal data
• Effectiveness of voluntary inclusion incentives
• Long-term psychological impact of physiological feedback

Intended Audience

• Academic research institutions and funding agencies
• Early-stage biotech and wearable startups
• Regulatory bodies shaping emerging biosensing frameworks

P.S. If you notice the sensor turning bright purple during the final presentation, please hand me a coffee with Red Bull—and do not make eye contact.