Week 1 HW: Principles and Practices

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Question 1:

I propose to develop a Living Urban Decision Interface (LUDI): a biohybrid computational device using slime moulds as a spatial decision-making substrate for ecological design. The organism’s network-forming behaviour and electrical oscillations will be interfaced with environmental and human inputs (light, nutrients, moisture proxies) to compute spatial layouts for green corridors, gardens and urban green infrastructure. Unlike simulation models, this system allows a living organism to actively participate in planning decisions, translating ecological processes into design recommendations. Because the slime mould’s network optimisation behaviour arises from identifiable molecular signalling and transport pathways that have already been experimentally used in laboratory studies, this project also establishes a future synthetic-biology direction in which genetically tuned physiological responses could allow the organism to compute specific environmental variables (e.g., pollutants, soil conditions, or water stress). The project aims to prototype a biological planning instrument that mediates between community intention and environmental constraints, opening a new form of participatory urban design in which living systems become co-designers of cities.

Question 2:

Governance and Policy Goals

Overall goal: Ensure that a Living Urban Decision Interface (a biohybrid computing system using Physarum polycephalum) supports ecological stewardship and public participation without introducing biological harm, technocratic authority, or misleading representations of ecological knowledge.

Goal 1 Biological Safety and Environmental Containment: (Non-malfeasance: prevent harm to ecosystems and people) Even though Physarum polycephalum is non-pathogenic and widely present in soil, a civic-facing biohybrid device requires clear safety governance so it is not treated casually or released improperly.

Sub-goal 1.1: Containment protocols

• Maintain the organism in closed, recoverable substrates (agar plates or contained bioreactors)
• Prohibit intentional outdoor release during demonstrations
• Establish a documented deactivation method (drying, freezing, or ethanol sterilisation)
• Provide handling instructions to participants

Sub-goal 1.2: Transparency of organism status

• Clearly communicate that the organism is alive
• Label installations as biological systems
• Require informed interaction (no hidden devices)
• Participants should not unknowingly interact with living biological materials.

Goal 2: Epistemic Responsibility (Preventing ‘Bio-Authority’) (Prevent misuse of biological outputs as unquestionable truth)

The largest risk of this project is not biological, it is decision authority. People may interpret the slime mould as “nature deciding”, and planners could misuse it to justify policies.

Sub-goal 2.1: Non-deterministic interpretation

• Present outputs as recommendations, not decisions
• Display uncertainty (multiple possible paths, not a single answer)
• Require human deliberation alongside organism output

Policy principle: The organism informs, it does not govern.

Sub-goal 2.2: Anti-technocratic safeguards

• Prevent institutions from using the system to legitimise exclusionary planning
• Document inputs used in each computation
• Make datasets and conditions publicly visible

Otherwise a city could say “the biological system determined this neighbourhood should not be greened.” The risk here is not the slime mould but authority laundering through nature.

Goal 3: Participatory Equity and Access (Promoting constructive uses rather than extractive ones). If the tool only exists in universities or smart-city labs, it becomes another technology used on communities rather than with them.

Sub-goal 3.1: Community interpretability

• Use legible visual outputs (maps, paths, growth patterns)
• Allow participants to manipulate inputs (light, nutrients, etc)
• Provide educational explanation of how the organism computes

Goal: People should understand the system well enough to disagree with it.

Sub-goal 3.2: Open civic access

• Publish protocols openly
• Use low-cost hardware where possible
• Enable community gardens and schools to run their own devices

This shifts it from: biotechnology product → civic ecological instrument.

Goal 4: Preventing Anthropomorphic or Extractive Framing (Respecting living systems as collaborators rather than tools). The project risks treating life as a novelty interface.

Sub-goal 4.1: Welfare considerations

• Avoid life cycles solely for demonstration
• Maintain proper humidity and feeding
• Limit unnecessary repeated stress stimuli
• Even non-sentient organisms deserve stewardship in educational contexts.

Sub-goal 4.2: Representational honesty

• Avoid presenting the organism as “wanting” specific urban outcomes
• Frame it as a biological process responding to constraints
• Avoid claiming ecological knowledge beyond what the experiment measures. This prevents ecological romanticism becoming misinformation.

Goal 5: Ecological Data Rights & Bio-Cybersecurity to protect the integrity and ownership of biological data generated by living systems

The Living Urban Decision Interface converts biological processes (oscillations, growth patterns, and spatial choices of Physarum polycephalum) into digital information used for civic decision-making. This creates a new category of data: ecological behavioural data produced by a living organism rather than a human or a conventional sensor. Governance frameworks should therefore prevent appropriation, manipulation, or enclosure of these biological signals.

Sub-goal 5.1: Biological signal integrity (bio-cybersecurity)

• Protect recorded organism signals from alteration or algorithmic manipulation
• Document all translation steps from electrode signal → software → map
• Maintain open logs of processing methods
• Prevent “tuning” outputs to support predetermined planning outcomes and prevent scientific data tampering. (ie. once digitised, a city, company, or platform could quietly modify outputs and still claim “the living system recommended this.”)

Sub-goal 5.2: Ecological data stewardship (non-extractive use)

• Treat organism-generated data as a commons rather than proprietary data
• Prohibit exclusive ownership or patenting of specific behavioural outputs
• Require public accessibility of datasets produced in civic contexts
• The organism’s behaviour should not become a privately enclosed planning resource.

Sub-goal 5.3: Proto-rights of living computational agents

The project adopts a precautionary “nature-rights” stance in which biological participants are considered contributors rather than passive instruments.

Operational implications:

• The organism cannot be represented as endorsing political or commercial claims
• Its outputs cannot be used in advertising or greenwashing
• Its participation must be disclosed in all decision context
• working with the notion of legal personhood and establishes: a data dignity principle for non-human contributors.

The project includes governance goals to ensure the system contributes to an ethical future. First, biological safety will be addressed through containment protocols, deactivation procedures, and transparent lableing of the organism as a living material. Second, epistemic safeguards will prevent the biohybrid system from being used as an authoritative decision-maker: outputs will be presented as recommendations with documented inputs and uncertainty rather than determinations. Third, the project promotes participatory equity by making protocols open, legible, and community-operable so the tool cannot be restricted to institutional planning contexts. Fourth, the organism will be treated as a living collaborator rather than a novelty interface, with care standards and representational honesty about what the system can and cannot infer about ecological conditions. An additional governance goal concerns ecological data rights and bio-cybersecurity. Because the device translates the behaviour of a living organism into digital planning information, it produces a novel category of data: ecological behavioural data generated by a non-human participant. The project therefore treats organism-generated signals as a shared ecological commons rather than proprietary information. Processing steps from biological signal to spatial recommendation will be documented and transparent to prevent manipulation or institutional misuse. This establishes a form of “data dignity” for non-human contributors and supporting broader principles of nature-rights within civic technology systems.

Question 3:

Governance Action 1: A Transparency & Disclosure Requirements

A. Purpose

What happens now:

Cities and institutions increasingly use algorithmic decision tools (AI planning models, environmental sensors, “smart city” platforms) without clearly disclosing how recommendations are produced. Biohybrid systems introduce an additional layer:

Proposed change:

Require public disclosure whenever a biological computing or biohybrid system contributes to planning, environmental assessment, or civic decisions.

This is similar to:

• AI transparency policies
• environmental impact assessments
• food labeling

The goal is not to restrict research, but to prevent authority laundering through biology.

B. Design

Actors involved:

• municipal governments / planning departments
• universities deploying installations
• community organisations hosting devices

Implementation could include:

• a “biohybrid disclosure notice” (like building permits)
• documentation of inputs used (light gradients, nutrients, constraints)
• explanation of interpretation limits

A simple standard:

Any civic recommendation derived from a living organism must include a description of how the organism’s behaviour was translated into a decision.

C. Assumptions

• People may over-trust biological outputs.
• Institutions might present outputs as objective evidence.
• Civic systems lack frameworks for non-digital computation.
• Communities may only understand it as experimental
• The system might never be used in formal planning contexts

B. Risks of Failure & “Success”

• Institutions ignore or weakly apply disclosure → system becomes symbolic justification for predetermined policies.
• safety rules to unintentionally create technological gatekeeping.

If the requirement is too bureaucratic, it may:

• discourage community groups from using the tool
• centralise it in universities or experimental only

Governance Action 2: Public Ecological Data Commons Licensing

Purpose

What happens now:

Environmental sensing data (air quality sensors, satellite imagery, soil sensors) is often:

• privately owned
• platform-controlled
• monetised

this project produces biological behavioural data → digitised → planning recommendations.

Proposed change:

Treat organism-generated data as a public ecological commons rather than proprietary platform data.

Similar samples:

• open-source software licenses
• Creative Commons
• open meteorological data

Design

Actors:

• academic labs
• civic tech organisations
• municipalities
• funders
• citizens

Implementation:

Projects receiving public funding must:

• publish raw signals
• publish interpretation method
• allow reuse by communities

Key rule:

No exclusive ownership of behavioural outputs derived from the organism in civic contexts. This prevents a company from building proprietary nature intelligence planning software.

Assumptions

• Companies will want to commercialise ecological computation
• Open access improves democratic participation
• Maintaining open datasets requires resources
• Communities may not have capacity to use the data

Risks of Failure & “Success”

Data becomes open but unusable → only experts benefit.

If widely adopted, developers might:

• mass-deploy biological computing
• treat organisms as scalable infrastructure

You could accidentally create a bio-smart-city industry that farms living systems as computation utilities.

Governance Action 3: Built-in Technical Safeguards (Design Governance)

Purpose

What happens now:

Most governance relies on rules after technology is deployed. But biohybrid systems allow something different: you can embed governance inside the device itself.

Proposed change:

Incorporate interpretability and uncertainty directly into the interface so the system cannot produce a single authoritative answer. This is a technical safeguard rather than a rule.

Design

Actors:

• you (researcher/designer)
• open-source hardware developers
• academic labs

Implementation features:

• multiple possible outputs shown simultaneously
• visible organism state
• display of input conditions
• no “optimal solution” output
• interactive participation

Question 4:

Question 5

Prioritized Governance Approach:

Drawing on the scoring matrix and governance goals, I would prioritize transparency and disclosure requirements as the primary governance mechanism, supported by ecological data commons and stewardship licensing as a complementary policy. The scoring indicates that the most immediate risks arise from interpretation, accountability, and ownership rather than from direct biological hazards.

The scores show that this aligns best across almost every category: it supports biosecurity response, lab safety, environmental protection, minimizes burdens to stakeholders, does not impede research, and promotes constructive applications. This pattern suggests that the central governance challenge of a biohybrid system like the Living Urban Decision Interface is not controlling the organism itself, but governing how its outputs are interpreted and used in decision-making contexts.

The project introduces a new kind of system: a living organism whose behavior is translated into planning recommendations. Without disclosure, a municipality, institution, or organization could present outputs as objective ecological evidence rather than as the result of a mediated experimental process. The scoring therefore points to a governance priority focused on preventing authority laundering (situations in which a biological system is used to legitimise decisions that were effectively human choices.)

A disclosure requirement would ensure that whenever a biohybrid computational system contributes to civic or environmental planning, the conditions of the experiment, the inputs provided, and the interpretive limitations are publicly documented. This improves both prevention and response: it allows communities to scrutinize how results were produced, and it provides accountability if the system is misapplied.

However, transparency alone does not address longer-term risks of enclosure and power concentration. This is where the second governance option becomes important. Although it scored more modestly in immediate safety categories, it addresses a structural issue: once biological signals are digitised, they can become proprietary datasets. Without stewardship rules, a private entity could accumulate organism-generated environmental intelligence and deploy it as a planning platform or optimisation service, turning ecological participation into a privately controlled resource.

Therefore I recommend combining these goals to govern interpretation and ownership/ access. Together they ensure that the organism functions as a civic ecological participant rather than as either a gimmick or a proprietary decision engine.

Intended Audience

I would direct this recommendation primarily to municipal governments, public research institutions, and citizens, such as a city planning office working with a university lab and public organising groups.

This level of governance is appropriate because the technology is likely to appear first in:

• public demonstrations
• participatory planning workshops
• civic environmental pilot projects
• community science programs

National regulation would likely be premature and overly restrictive, while purely voluntary norms would be insufficient once planning decisions begin referencing biological outputs. Municipal–institutional partnerships are therefore the most realistic and proportionate governance layer.

Implementation could take the form of a simple “biohybrid decision disclosure” requirement for publicly hosted projects, combined with grant conditions requiring open ecological data stewardship for publicly funded research.

Trade-offs Considered

The primary trade-off is between accountability and accessibility. A strong regulatory framework could improve safety but would likely prevent community organisations, schools, and small civic groups from using the system. Because the organism itself is not hazardous, heavy regulation would produce more harm than benefit by centralising experimentation in large institutions.

Goal 1 was prioritised partly because it minimizes burden while still providing accountability. It creates oversight without requiring licensing or specialized certification.

Goal 2 focuses on open ecological data reduces enclosure but may reduce incentives for private development and requires ongoing maintenance of shared datasets. There is also a risk that open data could be misinterpreted or reused out of context. However, the alternative of proprietary ownership of organism-generated ecological knowledge creates a larger long-term governance concern, particularly for urban planning equity.

Goal 3 (technical safeguards) presents another trade-off: embedding uncertainty into the interface may reduce misuse, but if the outputs appear too ambiguous, planners may disregard ecological input entirely. Because its effects are difficult to evaluate. I would treat this as a research and design practice rather than a primary policy mechanism at this stage.

Assumptions and Uncertainties

First, I assume the primary risk pathway is social and institutional misuse, not biological hazard. If future engineered strains were capable of environmental persistence or sensing harmful chemicals, biosafety regulation would need to be strengthened.

Second, I assume municipalities and research institutions will be early adopters. If private smart-city vendors deploy similar systems first, stronger national or procurement-level governance may be required.

Third, there is uncertainty about how people interpret biological outputs. Communities may either over-trust (“nature decided this”) or under-trust (“this is just art”). The governance framework attempts to preserve a middle ground: the organism contributes information but does not make decisions.

Finally, I assume ecological data can function as a commons. In practice, maintaining accessibility, interpretability, and long-term stewardship will require institutional commitment that may not yet exist.

Conclusion

Based on the scoring, the most effective governance strategy is not to tightly regulate the organism but to regulate the relationship between biological computation and civic authority. A combination of transparency requirements and ecological data stewardship best addresses the real risks (misinterpretation, enclosure, and inequitable decision-making) while preserving the benefits of experimentation, public participation, and research innovation.

Reflections: Ethical Concerns and Proposed Governance

During this week I began thinking less about whether the slime mould works as a computational system and more about what it means to introduce a living organism into civic decision-making and data infrastructures. Several ethical concerns emerged that were new to me.

1. Municipal misuse and “authority laundering”

One concern is how municipalities or planning institutions might use outputs from the slime mould. Because the organism is framed as representing ecological processes, its recommendations could be presented as objective or natural decisions rather than as interpretations of an experimental setup. A city could potentially justify a planning outcome — for example, where green infrastructure is or is not placed by claiming that “the living system determined this,” even though the experiment is shaped by human-selected inputs and constraints.

This raised a new ethical issue for me: the risk is not only biological harm but epistemic harm decisions gaining legitimacy through an appeal to nature.

Possible governance actions

• Require public disclosure of experimental conditions and inputs whenever biohybrid systems are used in civic planning.
• Require that outputs be presented as recommendations rather than determinations.
• Include community interpretation sessions alongside demonstrations so results are collectively interpreted, not administratively imposed.

2. Ecological data rights and ownership

Another concern is ownership of the data produced. The slime mould produces electrical and spatial behaviour that becomes digitised and mapped. Currently, whoever builds the interface typically owns the data. However, this data originates from a living organism interacting with environmental conditions and public space. If captured by a company or platform, it could become a proprietary ecological optimisation system.

This led me to think about a new category of information: biological behavioural data generated by non-human participants. The ethical issue is about preventing enclosure of ecological knowledge.

Possible governance actions

• Treat organism-generated datasets as an environmental commons rather than proprietary data.
• Publish raw signals and interpretation methods for publicly funded projects.
• Prohibit exclusive commercial claims that the organism “endorses” planning or environmental decisions.

3. Species instrumentalisation and “slime mould farming”

A concern I had not anticipated was the possibility of scaling. If slime mould computing becomes useful, institutions or companies could culture large quantities as a biological processing substrate effectively “farming” the organism as infrastructure. Even though the organism is not sentient, mass cultivation purely for computational exploitation raises questions about how we relate to living systems. This shifts the organism from collaborator to extractive resource.

Possible governance actions

• Establish basic organism care standards for public or educational installations (humidity, feeding, and recovery periods).
• Limit repeated stress stimuli (light shocks, starvation cycles) used only for demonstration.
• Encourage small-scale cultivation and prohibit industrial-scale deployments without ethical review.

4. Misinterpretation and anthropomorphism

Another issue that emerged in class discussions is that people quickly attribute intention to the slime mould (“it chose this,” “it prefers that neighborhood”). This creates a paradox: over-trusting the organism and misunderstanding what it actually measures. The slime mould does not understand cities; it responds to gradients. If misunderstood, the system could misinform ecological planning rather than enrich it.

This was new to me because the ethical challenge is not accuracy alone but representation.

Possible governance actions

• Include interpretive explanations alongside all outputs describing what variables the organism actually responds to.
• Display uncertainty and multiple possible outcomes rather than a single optimal plan.
• Require facilitators or researchers to explain the mediation layer between biological behaviour and planning recommendations.

Reflection

The main shift for me this week was realising that the ethical questions are not primarily about biosafety. Slime Moulds are relatively harmless biologically. Instead, the ethical risks arise from how biological processes intersect with governance, data, and authority. A living computational system changes who or what is considered a participant in decision-making, and governance therefore needs to address interpretation, ownership, and responsibility, not only containment.

Rather than restricting experimentation, the appropriate response seems to be lightweight but clear governance: transparency, shared stewardship of ecological data, and careful framing of the organism as a contributor to discussion rather than a decision-maker.