Week 1 HW: Principles and Practices

Index

• Question 1
• Question 2
• Question 3
• Question 4
• Question 5

Question 1

First, describe a biological engineering application or tool you want to develop and why

My project focuses on a small tool that uses synthetic biology to detect water contamination in rivers and irrigation sources within the Tambo Valley (Arequipa). Much of my family is from there, and I also lived there for a time. It is a beautiful and agriculturally important place, but it is also affected by concerns about contamination that can be linked to extractive activities and, in some cases, illegal mining upstream. Since mining is one of Peru’s most important primary-sector activities, the issue is significant not only environmentally but also socially.

This device is intended to be low-cost and field-deployable, so it can be used in rural areas where access to advanced tools and laboratory testing is limited. To make it practical, it would use a simple traffic-light readout: green would indicate the water is likely acceptable for use, yellow would suggest caution, and red would indicate the water should not be used. I know that sometimes the condition of the water can be judged visually, but many contaminants are not obvious, and sometimes there ar incomplete or intermittent official information that can make it difficult for farmers to know what to do.

Biologically, the core idea is to build an arsenic-sensitive module (arsenic being a key contaminant of concern) that converts a chemical signal into an easy-to-interpret output. In concept, an arsenic-responsive sensing element activates a genetic circuit that turns on a reporter signal (such as a color change). The design would also include basic internal controls to help distinguish a true “safe/low” reading from a failed test due to challenging sample conditions like extreme pH or high turbidity.

Question 2

Next, describe one or more governance/policy goals related to ensuring that this application or tool contributes to an “ethical” future, like ensuring non-malfeasance (preventing harm). Break big goals down into two or more specific sub-goals.

General objective 1: Non-malfeasance via biosafety and measurement reliability

Ensure the biosensor does not introduce biological/environmental risk and does not cause harm through inaccurate “traffic-light” decisions.

Specific sub-goals

• Biosafety containment and disposal: require design and handling rules that prevent environmental release (e.g., non-viable formats, containment cartridges, safe disposal protocols for used tests).
• Accuracy and validation standards: set minimum performance requirements (limit of detection for arsenic, false positive/negative thresholds) validated against reference methods before deployment and periodically thereafter.
• Robustness in real field conditions: mandate internal controls and “invalid test” states for extreme pH/turbidity/temperature, so the tool never forces a misleading green/yellow/red when the sample conditions break the test.
  • Risk communication tied to actions: define what each color legally/operationally means (e.g., “red = do not irrigate/drink,” “yellow = retest/seek confirmation”), including uncertainty language so farmers don’t over-trust a simple output.

General objective 2: Equity, accountability, and responsible use of results

Ensure the tool improves rural decision-making without creating new inequities, conflict, coercion, or misuse around contamination data.

Specific sub-goals

• Equitable access and support: policies to keep cost low for small farmers, ensure distribution beyond large producers, and provide training materials in locally appropriate formats.
• Data governance and privacy-by-default: define who owns results, whether data is stored/shared, and safeguards against misuse (e.g., results used to pressure communities, target individuals, or enable extortion).
• Transparent governance and recourse: require public documentation of what the sensor detects (arsenic range), limitations, and error rates; create a dispute/appeal pathway when results affect water access, reporting, or conflict.
• Non-retaliation and harm mitigation: if results implicate upstream activities (including illegal mining), establish rules for how findings are reported to avoid triggering retaliation against testers or communities.

Question 3

Next, describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”).

1) Standardized labeling + decision protocol (“screening, not certification”)

The purpose is to prevent harm from misunderstanding, ensuring each test result leads to safe action. Every device or package must include clear on-device text stating it is only an initial screening(not a certification) specifying what it detects, its limits, and a simple traffic-light action guide (green/yellow/red) that indicates when professional confirmation is needed. This works if people follow straightforward, standardized instructions. The risk is that a “green” result is misinterpreted as a total guarantee of safety, while success means yellow or red results reliably trigger retesting or confirmation and decisions become consistent across all users.

2) Quality assurance + local validation + post-deployment performance monitoring

The goal here is to maintain the test’s reliability over time and prevent false reassurance that could erode trust. This means setting minimum performance requirements, lot-to-lot testing, periodic checks against a reference method, and offering a simple channel(maybe like a QR code) to report any failures or anomalies. The assumption is that even though field conditions vary, a practical quality-control system is feasible and builds credibility. The system fails if these checks are skipped due to cost or complexity, and it succeeds if performance remains stable across batches and issues are detected and corrected quickly.

3) Data governance + non-punitive reporting + defined response pathway

This pillar aims to make the generated data beneficial for the whole community, allowing for the detection of contamination patterns for a targeted response, but without creating stigma or retaliation. The design collects only the minimum necessary data, aggregates results by area, requires consent for personal information, and clearly defines who gets notified and how confirmatory testing is requested. Crucially, it must be stated that an adverse screening result cannot be used to impose sanctions. This works if people feel safe reporting. The main risk is that data could be used punitively or leaked, while success is measured by high participation, faster identification of risk hotspots, and the reliable activation of supportive response actions.

Question 4

Next, score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of policy goals. The following is one framework but feel free to make your own:

Question 5

Last, drawing upon this scoring, describe which governance option, or combination of options, you would prioritize, and why. Outline any trade-offs you considered as well as assumptions and uncertainties.

Based on the scoring, I would prioritize Option 1 (standardized labeling + decision protocol) together with Option 3 (data governance + non-punitive reporting/response), and treat Option 2 (QA/validation + monitoring) as the next phase once a pilot is running. I would direct this recommendation to a local–regional group in Peru’s Tambo Valley (Arequipa): DIRESA/MINSA, the National Water Authority (ANA/AAA/ALA), district municipalities, irrigation user associations (juntas de regantes), and a regional lab/university for confirmatory testing.

The key ethical risk for a low-resource arsenic screening tool is misinterpretation, especially treating a “green” result as a guarantee of safety for drinking or irrigation. Option 1 should therefore require clear “screening—not certification” labeling, a simple traffic-light action guide (repeat/confirm/avoid), and explicit limits of use (e.g., pH/turbidity range) to reduce preventable harm.

A major ethical concern that became clearer to me is that, in Peru, environmental data can create social harm if it triggers stigma or punitive responses (including conflict or retaliation). That is why Option 3 matters: reporting should be privacy-preserving and aggregated, with defined access rules and a response protocol focused on confirmatory testing and mitigation, not blame.

The main trade-off is delaying Option 2, which increases uncertainty about performance across field conditions (seasonality, sediments, chemical interferences). I would mitigate this by making limitations explicit (Option 1), using reports to flag anomalies and prioritize confirmations (Option 3), and then scaling routine QA/validation as partnerships and resources become available (Option 2).

References

CONNECTAS (caso Coralaque–Tambo / minería y ríos del sur del Perú) https://www.connectas.org/especiales/contaminacion-minera-rios-peru/

Defensoría del Pueblo (Perú). (2025, February 5). Informe Defensorial n.° 235: Evaluación y recomendaciones frente a la contaminación en los ríos Coralaque y Tambo: Un enfoque integral y territorial para la protección del derecho a un ambiente sano y de prevención frente a la conflictividad social (PDF). https://www.defensoria.gob.pe/wp-content/uploads/2025/02/Informe-Defensorial-n.%C2%B0-235-Evaluaci%C3%B3n-y-Recomendaciones-frente-a-la-Contaminaci%C3%B3n-en-los-R%C3%ADos-Coralaque-y-Tambo.pdf

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project