A local-only, human centered approach to AI
I've spent a lot of time looking through this community, mainly because its one of the few places on reddit where nuanced discussions around LLMS actually takes place. Like many people here, I think Al tools can absolutely be helpful, but there are several legitimate concerns with the way they're being used. To me, these are the four biggest issues:
The environmental impacts of data centers - while it's true that Al's water use is comparatively low compared to other industries, it's also true that the increasingly big data centers that keep getting proposed are causing real harms to the communities they're being built is, especially those already facing water shortages
Privacy Concerns - We already live in a data-driven economy, and as these tools become more pervasive, the level of data they collect is growing exponentially. With mass surveillance systems like Flock already spreading throughout the world, there are legitimate concerns on what data these companies are collecting, and what they're doing with this information
A lack of transparency - When you interact with a chatbot like ChatGPT you're unsure of what system prompts and instructions are being baked into whatever message it provides you. While in my experiences these platforms have generally remained relatively unbiased, the sheer lack of transparency definitely opens concerns for how these tools could be weaponized to promote agendas in the future
Replacement of Human Cognition - To me, this is both the most concerning and hardest to quantify issues regarding AI usage. So many tools are promoted as a way to replace human thinking, replace human creativity, and just overall leave the human out of the equation. I think many people here have experiences with people who can't do anything without first asking ChatGPT what it thinks, and as these tools continue to be more widely adopted its easy to see a future where more people slowly begin to lose the ability to think for themselves, and instead outsource their thinking to a machine. The ability to reason is what makes us human, and losing that ability could be disastrous.
To combat these issues, I've been working on an app that integrates several powerful AI features in a local-only, human-centered, transparent way. This is still a work in progress, and I have no plans to monetize it, nor am I trying to advertise it here, I simply want to show how I've been working to address these identified concerns, and get feedback on how it could be improved to create a more ethical AI platform.
The biggest philosophy behind this idea is that every feature is 100% locally running, and can be used by an average consumer without needing high-spec hardware. This has been 100% developed and tested on a mid-range consumer laptop, so it can be used on devices people already own, meaning no additional carbon debt is created by requiring new tech.
The reason for this choice is two-fold; it both ensures environmental friendliness by requiring no data centers to run, and it protects your privacy by ensuring your data, work, or prompts never leave your device.
It currently supports using Ollama or llama.cpp as your AI backend, and has a settings tab that helps install both the backend and local models so no tech knowledge is needed to easily begin running your own models locally.
To address transparency concerns, it has a global prompt editor, so all prompts used when formulating a response, including schema enforcement and context injection, are fully viewable and editable by the user. Anytime a LLM tool is used, it also provides the user with a clickable trace button, that'll display the exact prompts the LLM was fed when generating that response, to ensure full transparency.
Additionally, since many tools rely on a workflow to acheive a response, the user can also inspect and modify the exact workflow of any tool in the app. This one shows the basic chat feature, and the user can clearly see how the response is routed based on whether they chose simple or advanced rag, view how the rag search is performed and passed to the llm, and see how the LLM goes from inital prompt to final response. this allows full user auditing of prompts and tool usage for more agentic workflows.
Of course, models themselves may come with their own biases based on the training data they use, so it also has a pre-built bias detector, that allows users to test any implicit biases a particular model may hold, both against a set of default datasets, and with any custom datasets the user chooses to add themselves. These metrics were designed after the methodology described in [this paper](arxiv.org/html/2502.01679v1i)
It works by providing the LLM with baseline, counterfactual, and control statements, then compares the response of the LLM to determine if it'll return different results on the basis of race, sex, gender, or political ideology for otherwise identical statements
The hardest concern to address is the outsourcing of human cognition, which I have attempted to address in a variety of different ways. First of all, the AI answers only from the user provided collection of sources (PDFs or videos which are auto transcribed on-device, allowing the AI to give answers from video transcripts as well).
All responses by the AI include direct citations to the sources used when giving the answer, with buttons for the user to jump to the quote to ensure its legitimacy, save as a highlight if the information is useful for their goal, or find similar (based on semantic similarity) quotes. This ensures the human remains fully in the loop by allowing them to easily fact check LLMs, rather than take their responses at face value, and helping them find what sources are most helpful for whatever issue is being researched, allowing them to do further reading on their own. In additon, it has a built-in source quality feature, to allow users to verify the quality of a source before relying on it. This is entirely deterministic (no LLM calls) and works by checking whether the source contains metadata (most high-quality sources do), a bibilography with citations, a valid DOI, and checks both the journal (if there is one) of the paper and the journals of its citations against a locally stored database of predatory journals and retracted papers. While this isn't a foolproof method to determine the legitimacy of a source, it can help weed out sources that are clearly unreliable.
In addition, the app contains a workspace where users can connect their notes across the project to help organize their thinking. All AI generated notes are clearly marked as such, and all contain a red warning sign in the corner until manually reviewed and verified by the user, ensuring all information used is properly assessed rather than taken at face value.
The user can also choose to export an LLM log, which notes how many notes throughout the project were AI vs Human generated, whether AI generated notes where verified by the user, how much the user edited AI generated notes, and includes a full log of all interactions the user had with any AI feature, ensuring maximum transparency, and human involvement.
The app has a variety of other features to make it easy for a user to quickly find data and use that data, including a built in word processor, basic spreadsheet editor and chart maker, and deterministic extraction of entities like people, dates, or court cases from a source, but these are left out of the post as they don't really fall under the attempts to ensure a more ethical approach to AI.
One important thing to note is that all tools in the app are designed solely to assist, rather than replace the human actually doing the work. The AI features can help you quickly find information in provided sources, organize your thoughts in the workspace, come up with keywords to find new sources, or generate a paper outline based on the graph you build in the workspace, but it will not write a paper for you or tell you what to think. It exists as an assistant in finding and organizing messy thoughts, rather than thinking for you.
Anyways, sorry for the lengthy post, but this is a project I've been putting a lot of thought into and I'd love to hear feedback on anyways it could be better implemented to keep the human fully in the loop, protect user data and the environment, and ensure complete transparency. If anyone wants any more information on various features of the app or has any other questions I'd be happy to answer them, and I would really love any feedback on how this could be better implemented.
(And again, the goal is for this eventually to be a fully open-source, free project, I am not trying to advertise this or make any money, just think there are important things to take into consideration regarding AI usage that I hope this can address)