Engaging with Artificial Intelligence (AI) with a Bottom-Up Approach for the Purpose of Sustainability: Victorian Farmers Market Association, Melbourne Australia
- Can the design process support the selection of AI tools for sustainable food systems?
- How does the design process carry the sustainability intent?
- Where are questions of AI ethics discovered and resolved?
- Can the process avoid technological solutionism? If so, how?
- Can the process be reproduced to support transition to Sustainable Food Systems (SFS) using AI?
1.1. Victorian Farmers’ Markets Association
1.2. AI and Ethics
1.3. Sustainable Food Systems Framework and Leverage Points
2.1. Participant Engagement: Who Sits at the Table
2.2. Process Activities: How We Engage
2.3. Evaluation Framework for AI-Powered Solutions
2.4. Data Collection and Analysis
3.1. Sustainability in the Design Process: Nature versus Nurture
3.2. AI Ethics in Practice
3.3. AI in SFS: Solution or Framework?
- Big data takes stock of climate change risks on agricultural land. This could be pushed further by keeping track of farm inputs to understand practices that reduce or limit that risk over time. It opens the way to valuation that takes sustainable practices into consideration and potentially provides higher access to funding for growers who invest in adaptation and mitigation.
- Machine vision can enable classification of pests and diseases or identify nutrient deficiency, a proactive avenue to reduce or eliminate losses. It also provides options for more efficient estimation of ripeness and quality, a better-timed harvest, which could save on growers’ food losses and consumers’ food waste. Other applications are related to growth prediction and pruning regimes, and some have started investigating soil analysis opportunities.
- Machine learning and handheld spectrometers are changing the approach towards the consistency of quality in cheese production to provide a proactive measurement of fat and protein content, which could then be acted upon during the process, rather than after the fact. The possibility to measure nutrient density and, therefore, a system to detect value for money would benefit producers of high quality, high nutrient, high flavour, and longer shelf-life produce.
- Technology such as blockchain can be of direct benefit to sustainable supply chains where networks of suppliers and consumers can exchange verified information on food provenance, sustainable practices, and the distribution of revenue (to ensure fair trading practices).
4. Discussion and Conclusions
4.1. Reflection on the Research Questions
4.2. A Process to Feed the Engine of Transition
- Engage with AI with a definite intent to achieve sustainable, ethical, and systemic outcomes (if it is not the main aim, then it is only going to be an after-thought)
- Codesign in partnership with the organisations’ participants with respect for the strategic intent they bring and the diversity they represent
- Codesign with experts to help divergent thinking when needed (zooming out) and to consolidate options (zooming in) when required
- Use iterative methods to create reflective practice, validated learning, and a re-assessment of activities and outcomes against the values set
- Allow people to be changed by the process and to change the solution in turn
- Focus on outcomes that privilege deep leverage points (Section 1.3)
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
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|Consumer in person|
|Viability||Cost/ROI for user|
|Cost/ROI for VFMA|
|Alignment to VFMA strategy|
|Additional opportunity for innovation|
|AI ethics||Human, social, and environmental wellbeing|
|Privacy protection and security|
|Reliability and safety|
|Transparency and explainability|
|SFS sustainability||Improved crop management|
|Improved livestock management|
|Improved livestock management|
|Improved supply chain|
|System leverage point for transition||Parameters|
|Design Activity||Sustainability Considerations|
|Stage A—Orientation workshop||Participant’s personal involvement over the years with sustainability and their dedication to both food quality and solving sustainability issues. |
Panel genuinely care about the Association, their members, and the farmers’ markets (FM) customers. Strong values on collaboration, recognition of farmers’ knowledge, regenerative practices in agriculture, financial and environmental sustainability.
|Theme selection: How might we use AI to scale up the VFMA members’ sustainability impacts? |
Introduction: Participants share their motivation for being present and the type of challenges they feel are important with regards to sustainability.
Intent: Establish a vision independent of the tools.
|Stage B—Strategic brief||Strategic pillars of the association focused on the diversity of produce and accredited producers, financial sustainability for the members, feeding the FM supporting community, and building a resilient food system.|
Participating panel provided ideas and defined parameters for benefits of the research project.
|Alignment: Proposed initiatives alignment table to VFMA strategy.|
Nothing is off the table: ideas are proposed but open for complete redesign.
|Stage C—Scoping an initiative 1||Participant self-evaluated and reviewed ideas and proposals on the board in light of the values they share.|
Strong ethical and sustainable values informed the conversation and the problem definition.
Pragmatic approach kept ideas on the practical rather than theoretical side.
|Strategy blueprint: Guide to the session to determine if the canvas findings meet the challenges, the aspirations, focus areas, guiding principles, and outcomes identified to date (see Supplemental Materials Figure S1 for more details).|
Visual collaboration: Setup of the Mural board allowed all participants to add their ideas but also to read others’, which in turn triggered reactions and further inputs.
Lean canvas: Format guided dimensions of social, environmental, and financial sustainability questions.
Riskiest assumptions: Canvas facilitation towards identifying risk clarified assumptions that carry potential impact on sustainability and ethics.
|Stage C—de-risking assumptions about food quality||Panel definition of “food quality” relates to holistic view of quality and integrates sustainability across all phases of the produce.|
Lived experience from producers brought the concept of “story of quality” to the project.
Beginner’s mind: Seeing old information with new eyes happened when the designer questioned what is taken for granted.
|Openness to change: Organise a new collaborative design activity to investigate deeper link to values.|
Listening to different voices: A simple exercise led to a holistic view of food quality.
More than the sum of the parts: Active seeking of external panel’s input brought rich nuances to the definition of quality and ways to measure it.
|Stage D—Scoping an experiment||Intimate knowledge of the VFMA and its members provided depth to the selection of a solution to scale up sustainability.|
The VFMA has direct relationships with producers and consumers, making experimenting with AI for sustainability simplified.
VFMA is a small representation of bigger systems and a model of alternative food systems where AI for sustainability can be studied in decentralised environments for small to medium producers.
|Awareness: Recognising when too much collaboration impedes the work and pivoting to narrower input was required.|
Prioritisation table: Provided a frame for evaluating AI solutions against innovation, sustainability, ethics, and systems.
Building in iteration: All the work to date informs the evaluation exercise and builds on what was learned, therefore, decisions were based on the collaborative work but finalised by the key carrier of the project, intent.
Lean startup: Creating a tangible artefact for the problem we want to solve and the solution we want to try forces the initiative to become a practical experiment which can be built upon (persevere or pivot).
|Stage A: Orientation Workshop—23 February 2020 to 17 March 2020|
(in Order of Priority)
|Benchmarking sustainability||e.g., baseline price for food, information campaigns on nutritional content to create value and connect the cost of food to land/animal stewardship, what is in the price, sustainable reporting frameworks, sustainable food systems benchmarks||Create new baselines and benchmarks |
Create information for consumers
Connect and deepen relationships and networks
|Food as health||e.g., food as medicine (nutrient contents knowledge base and indicators, tools to ascertain Brix content, link sustainability and health) joint metrics, initiatives on ingredient substitution models for seasonal/cultural/diets, soil as health (healthy soil knowledge base and indicators)||Create new baselines and benchmarks |
Create information for consumers and producers
|Collaborative farming||e.g., coops or foundations for collaborative work, farmer to farmer digital networks using mobile phones (i.e., weFarm), seed prediction and supply models, interactive/real-time connection to farms, succession planning (decision support knowledge centred, profiling one farm), systemic evaluation of crops in Victoria and their support networks||Connect and deepen relationships and networks|
Leverage prediction and modelling capabilities to support decentralized and resilient farming
Create and maintain a sustainability knowledge-base
Create new baselines and benchmarks
|Research & development hub||e.g., DIY precision-farming weeds, resilience in soil and crops, DIY disease and fertiliser recognition and application, platforms for discovery and testing of AI-powered research for farmers and by farmers, farming tool development around digital for regenerative (small/poly/perm) farming||Potential opportunities through robotics|
Leverage detection capabilities
Create and maintain an AI knowledge-base
Develop AI for regenerative faming tools
|Term Selected |
(in Order of Priority)
|General Definition |
(from Participant Input)
|Sustainability-Supporting AI |
(Conceivable Ways of Measuring or Assessing)
|Regenerative||Regenerative agriculture is a conservation and rehabilitation approach to food and farming systems. It focuses on topsoil regeneration, increasing biodiversity, improving the water cycle, enhancing ecosystem services, supporting biosequestration, increasing resilience to climate change, and strengthening the health and vitality of farm soil. Practices include recycling as much farm waste as possible and adding composted material from sources outside the farm.|
General definition: renewal or restoration of a body, bodily part, or biological system (such as a forest) after injury or as a normal process.
|Investigate frameworks to support a holistic view of the quality of food from the quality of the practices employed to grow the food to the health benefits of food quality.|
Investigate frameworks for Regen Ag where AI can bring a clear advantage or innovation (e.g., provenance, traceability, sensors in soil to nutrient quality).
|Nutrient-dense||Food that is high in nutrients but low in calories. Nutrient-dense foods contain vitamins, minerals, complex carbohydrates, lean protein, and healthy fats.|
Nutrient density identifies the amount of beneficial nutrients in a food product in proportion to e.g., energy content, weight, or amount of detrimental nutrients.
|Ability to link quality and sustainability of growing practices to quality intrinsic to the food produce. |
Frameworks exist that will need to be evaluated in the context of the project.
|Delicious||Appealing to one of the bodily senses especially of taste or smell. Delectable, flavoursome, luscious, mouth-watering, savoury, tasty.||This would be difficult to measure because it is usually linked to personal preferences. However, if we have 1000 shoppers rating a produce as “delicious” then it would become a quantifiable measurement.|
|Fresh||Full of or renewed in vigour, not stale, sour, or decayed, not altered by processing.|
Excellent shelf life/storage as a consequence of nutrient density and minimal handling.
|Possibility to link education around what freshness in direct supply chain means (not stored in fridges for months, not processed, locally produced, and locally accessed.|
Measurement of ripeness, ripeness for purpose (i.e., for raw eating, for jams, etc.).
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Camaréna, S. Engaging with Artificial Intelligence (AI) with a Bottom-Up Approach for the Purpose of Sustainability: Victorian Farmers Market Association, Melbourne Australia. Sustainability 2021, 13, 9314. https://doi.org/10.3390/su13169314
Camaréna S. Engaging with Artificial Intelligence (AI) with a Bottom-Up Approach for the Purpose of Sustainability: Victorian Farmers Market Association, Melbourne Australia. Sustainability. 2021; 13(16):9314. https://doi.org/10.3390/su13169314Chicago/Turabian Style
Camaréna, Stéphanie. 2021. "Engaging with Artificial Intelligence (AI) with a Bottom-Up Approach for the Purpose of Sustainability: Victorian Farmers Market Association, Melbourne Australia" Sustainability 13, no. 16: 9314. https://doi.org/10.3390/su13169314