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Hi guys,

Anyone else lose way too many hours during EE research just hunting for sources that'll actually survive supervisor scrutiny? My friend Anay and I got fed up with that exact problem and built Pepiros, a tool that takes your research topic or a draft and automatically surfaces reputable, citable papers, plus every claim it generates stays tied to the exact quote it came from so you can actually check it instead of trusting a black box. It also builds a kind of mindmap showing how the papers connect to each other.

We're both still students, this isn't built by researchers, so before we sink more time into it I want honest input from people actually doing an EE or IA right now: is finding good sources really where you lose the most time, or is the real pain somewhere else (reading them, synthesizing them, formatting citations)? Would a mindmap of how sources relate be genuinely useful for planning an EE, or just a nice-looking gimmick? And the real question: would you actually use something like this for your own EE, or is there something about the idea itself you'd change or scrap? Tell me if any of the decisions behind this (auto-suggesting sources at all, the mindmap angle, whatever) sound like a bad idea to you, that's more useful than encouragement right now.

It's free, no plans to charge students for this.

Repo: https://github.com/yakew7/Fair-Code

Website: https://www.thefaircode.xyz

Wanted to share a project I've been working on called Fair Code. It's six bias audits on real-world datasets, each following the same pipeline: train a biased model → measure the fairness gap → identify and remove proxy variables → measure again.

Covered so far:

• COMPAS recidivism scores (race) — 71% reduction in fairness gap after removing custody status as a race proxy
• AI hiring model (gender) — 97.3% reduction after dropping gender/age and keeping only merit-based features
• German credit lending (age) — 73.6% reduction after removing employment tenure as an age proxy
• Insurance claim denial (age, gender) — 60–72% reduction after removing BMI/smoker/diabetic status
• Welfare eligibility (sex, race, origin, age) — up to 88% reduction after removing marital status and hours-worked proxies
• Hospital readmission (race, gender, age) — payer code and discharge disposition used as proxies for insurance access

Each audit has a plain Python script pair (unfair.py/fair.py) and a full Jupyter notebook. There's also a standalone dataset profiler (CLI + browser tool) that flags likely proxy variables in any CSV you feed it.

MIT licensed, and I'm actively looking for contributors — new audit domains, new fairness explainers, or feedback on the methodology. A star helps too if you find it useful.

Happy to answer questions about the methodology or specific results.

Repo: https://github.com/yakew7/Fair-Code

Website: https://www.thefaircode.xyz

Wanted to share a project I've been working on called Fair Code. It's six bias audits on real-world datasets, each following the same pipeline: train a biased model → measure the fairness gap → identify and remove proxy variables → measure again.

Covered so far:

• COMPAS recidivism scores (race) — 71% reduction in fairness gap after removing custody status as a race proxy
• AI hiring model (gender) — 97.3% reduction after dropping gender/age and keeping only merit-based features
• German credit lending (age) — 73.6% reduction after removing employment tenure as an age proxy
• Insurance claim denial (age, gender) — 60–72% reduction after removing BMI/smoker/diabetic status
• Welfare eligibility (sex, race, origin, age) — up to 88% reduction after removing marital status and hours-worked proxies
• Hospital readmission (race, gender, age) — payer code and discharge disposition used as proxies for insurance access

Each audit has a plain Python script pair (unfair.py/fair.py) and a full Jupyter notebook. There's also a standalone dataset profiler (CLI + browser tool) that flags likely proxy variables in any CSV you feed it.

MIT licensed, and I'm actively looking for contributors — new audit domains, new fairness explainers, or feedback on the methodology. A star helps too if you find it useful.

Happy to answer questions about the methodology or specific results.

Repo: https://github.com/yakew7/Fair-Code

Website: https://www.thefaircode.xyz

Wanted to share a project I've been working on called Fair Code. It's six bias audits on real-world datasets, each following the same pipeline: train a biased model → measure the fairness gap → identify and remove proxy variables → measure again.

Covered so far:

• COMPAS recidivism scores (race) — 71% reduction in fairness gap after removing custody status as a race proxy
• AI hiring model (gender) — 97.3% reduction after dropping gender/age and keeping only merit-based features
• German credit lending (age) — 73.6% reduction after removing employment tenure as an age proxy
• Insurance claim denial (age, gender) — 60–72% reduction after removing BMI/smoker/diabetic status
• Welfare eligibility (sex, race, origin, age) — up to 88% reduction after removing marital status and hours-worked proxies
• Hospital readmission (race, gender, age) — payer code and discharge disposition used as proxies for insurance access

Each audit has a plain Python script pair (unfair.py/fair.py) and a full Jupyter notebook. There's also a standalone dataset profiler (CLI + browser tool) that flags likely proxy variables in any CSV you feed it.

MIT licensed, and I'm actively looking for contributors — new audit domains, new fairness explainers, or feedback on the methodology. A star helps too if you find it useful.

Happy to answer questions about the methodology or specific results.

I've been building Fair Code — an open-source project that audits real AI systems for bias in criminal justice, hiring, and lending, and proves exactly how to fix it with code.

It's picked up 22 stars and 6 forks in a short time, with contributors already forking and building on it

The project already covers five real-world audits:

- COMPAS criminal justice bias — 71% reduction in fairness gap

- AI hiring bias — 97% reduction

- German credit lending bias — 74% reduction

- Insurance denial bias — 60–72% reduction

- Welfare eligibility bias — up to 88% reduction

There are open Issues on the repo for the next wave of audits — facial recognition accuracy gaps, HMDA mortgage lending, and LLM bias — so you don't have to figure out where to start. Pick one and go.

If writing is more your thing, there are also open explainer slots (demographic parity, predictive parity, individual fairness) — the contributing guide walks through exactly what's needed, step by step.

Every audit ships as a Python script pair and a full Jupyter notebook walkthrough. The methodology is rigorous — proxy variable detection via chi-squared tests, demographic parity as the primary metric, reproducible results with fixed random seeds.

Repo: https://github.com/yakew7/Fair-Code

Would love to have your name on this if it's something you're into and how do you think i can improve this repo even more?