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Smart Labels and Allergy Sensors - How to Ensure the Future of Food is Fair

 

Imagine that you are shopping in your own apartment. Remove the app from your phone and scan for items. Flashes instantly with a measure of personal risk for possible allergic reactions.


In the following method, smart packaging of prepared foods updates the food's carbon footprint over time. However, when you bring it home, the label changes to show the alarm time. No allergy has been detected in the manufacturing plants and the food must be recovered. How much more energy is used to produce this kind of energy? Who makes sure the app manages your health information? What if an accident warns you to throw away the best food, or if your small business has been blacklisted by a large grocery store?

These are just a few of the issues we need to pay attention to if we can connect the big data that passes through our streams by storing and distributing it in a single call system.

As part of the Food Data Trust's Ethics Working Group, my colleagues and I developed new ways for food organizations to help with processes such as ethical standards. Food processing is the largest sub-sector of UK manufacturing and is currently under extensive review. Faced with climate change, there is an urgent need to make chains more efficient and effective, promoting stability and reducing waste. That is, this is the goal that the new tools will help achieve.

This technology allows us to store food information like never before. Immediate measurement of the temperature of cold or frozen meat; Detailed information on what goes in and out of the store and when.
Sharing this information among multiple food sources, which will include multiple organizations, can provide huge security benefits. Especially if advanced technologies like AI prediction and approval algorithms can be used to identify consumer needs and further reduce costs.

Many organizations have been cautious about sharing this information, especially when business information can be disclosed. Confidence information should be available here. Trust, reinforced by strict rules and legal contracts, allows companies, for example, to prevent competitors from seeing personal information. However, trust data can also reveal issues that were not taken into account.
our research.

The team, which analyzes reliable data, funded by the Internet of Food Things project and the AI3SD project, is made up of experts from various specialties, including computer science, law and design. Before we can think about ethical data, we need to make sure we all understand all languages. Our first task is to create terms to describe how we use different words. For example, when we talk about AI, we can think of the science of fake science, the new kind of machine learning that does everything we can't do, or just the algorithms that are already there to support many. of our daily activities.
Based on a similar understanding, it verified the integrity of untrusted data. 

When they do, it may be too late to resolve the major issues they are facing.
This is an example of a "Collingridge's dilemma". This negative thinking suggests that there is not enough knowledge about the implications for the basic stages of the new technology. Once these benefits are clear, the technology may be too expensive or too late to manage.
One way to solve this problem is to describe the problems that arise by thinking in detail of what the future will bring with this technology. This is what we do in a research called "evidence-based design" that creates real products that represent the future.

Some of the things we did include minutes of board meetings that didn't exist, snippets of documentation on what didn't happen, websites that didn't show any allergies, apps notification and an intelligent volume that simulates simulations. The shopping experience is described in advance. When creating them, we try to take into account all the others that may have a positive or negative impact on the future world of our products. This includes decision makers at large retailers as well as small retailers, consumers and employees.
next level.

The next step is to use these documents to explore any issues they may present. We use a list called the Moral-IT Platform designed to measure morality.

Use this card to talk to experts about the dangers that can arise from using novel foods. For example, we have determined that the small food industry may be unfairly excluded from large retailers because they cannot afford the many sensors needed to connect products in the marketplace for AI reception estimates. Although we have focused on food, these techniques can also be used to identify other areas where new technologies have been introduced, such as the addition of smart devices (research during a garbage collection purchase ) in public places. Our next step is to apply what we've learned to create principles to guide people to make informed decisions about how to use the latest technology ethically.
By using innovative and creative approaches to identify and reflect on future technologies, we hope to help people develop a more equitable and equitable environment.