AI & Data
AI Safety & Responsible AI Challenges
AI Safety & Responsible AI challenges put you inside the work of making AI systems trustworthy before they ship. You'll build skills in AI ethics, AI bias, and fairness metrics, learning to surface problems through hallucination detection and a working grasp of adversarial concepts.
From there you'll take on the harder edges — adversarial robustness research and red-team operations for foundation models — and translate them into AI governance frameworks anchored in the NIST AI Risk Management Framework and EU AI Act risk classification, the way responsible AI teams actually operate. Each challenge you solve earns a verified credential you can share with recruiters.
Recommended Challenges
- AnalysisSeniorNew
Write a Copyright Risk Memo for a Foundation-Model Lab's Training Set
Cover (1) US fair-use exposure for training on web-scraped text and code, including the current state of pending major lawsuits at the time of writing; (2) the EU TDM exceptions…
- Copyright Law
- Regulatory Analysis
- Risk Mapping
AI Law, Policy, and Regulation - AnalysisIntermediateNew
Draft GDPR + AI Act Data Provisions for a Training-Data Vendor
Anchor the work on (1) GDPR Articles 28 (processor obligations) and 32 (security), (2) the EU AI Regulation's data-governance article for high-risk systems, and (3) the EDPB's p…
- Data Protection Law
- Contract Redlining
- Regulatory Analysis
AI Law, Policy, and Regulation - StrategyBeginnerNew
Pitch a Regulatory Sandbox Application for an Edtech AI Product
Read the EU AI Regulation's regulatory-sandbox provisions. Pick a member-state sandbox program (Spain, Norway-as-EEA, or a German-state pilot are publicly documented options) an…
- Regulatory Analysis
- Ai Governance Frameworks
- Product Strategy
AI Law, Policy, and Regulation - StrategyBeginnerNew
Responsible AI Policy for a HR-Tech Scale-up
Working as a cross-functional team, produce a Responsible AI Policy that addresses: permitted/prohibited use cases, human oversight requirements, data minimization, vendor due d…
- Ai Usage Policy
- Ai Governance Frameworks
- Stakeholder Management
AI, Ethics and Society Practice your coursework on real scenarios.
Every challenge is shaped from real industry context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- AnalysisIntermediateNew
Build a Bayesian Credit-Scoring Model for an Emerging-Markets Fintech
You receive an anonymized snapshot of about 30,000 historical applications with features (income proxy, tenure on platform, prior loans, region) and the binary default outcome. …
- Bayesian Learning
- Credit Scoring
- Model Evaluation
Advanced Machine Learning - AnalysisIntermediateNew
Auditing Bias in a Fintech Credit Scoring Model
Conduct a quantitative fairness audit using a public proxy dataset (e.g., the UCI Adult or Give Me Some Credit dataset re-framed as BNPL decisions) and apply at least three fair…
- Algorithmic Fairness
- Ai Audit
- Regulatory Analysis
AI, Ethics and Society - StrategyIntermediateNew
Design a Compliance Strategy for an AI Robo-Advisor in the EU
Anchor the work on the published EU AI Regulation risk classification (limited vs. high-risk systems) and the European Securities and Markets Authority guidelines on robo-advice…
- Ai Governance Frameworks
- Regulatory Analysis
- Product Strategy
AI and Quantitative Finance - StrategySeniorNew
Run a Mock Algorithmic-Discrimination Investigation for a Hiring-Tech Vendor
As a 3-person team, design and execute a 3-week mock inquiry. Produce: (1) the demand letter you imagine the regulator sending (scope, legal basis, data requested); (2) the vend…
- Regulatory Analysis
- Algorithmic Fairness
- Ai Governance Frameworks
AI Law, Policy, and Regulation - Browse challenges
Explore role
Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- StrategyBeginnerNew
Design an Internal AI-Use Policy for a Mid-Cap Bank
You receive the bank's existing IT-acceptable-use policy and a description of which AI tools are being rolled out (an internal Anthropic Claude wrapper for general use; a code-c…
- Ai Governance Frameworks
- Policy Design
- Responsible Ai
AI Ethics, Fairness, and Responsible AI - AnalysisIntermediateNew
Map a Healthtech Startup's Triage Bot to the EU AI Regulation
Read the EU AI Regulation's Annex III (high-risk areas) carefully. Classify the triage bot's components and explain whether the system is high-risk; if so, enumerate the applica…
- Regulatory Analysis
- Ai Governance Frameworks
- Risk Mapping
AI Law, Policy, and Regulation - AnalysisBeginnerNew
Stress-Test a Hiring-Funnel Model for Bias
You receive a synthetic-but-realistic dataset of 25,000 past applicants with features (years of experience, education tier, prior role tags) and outcome labels (advanced past th…
- Model Evaluation
- Fairness Metrics
- Logistic Regression
Machine Learning (Undergraduate) - AnalysisBeginnerNew
Audit a Hiring-Screening Model for Demographic Bias
You receive: (a) inference API access to the production model (black-box), (b) a 12,000-resume audit benchmark with self-declared gender and age-band labels (consented, GDPR-com…
- Fairness Metrics
- Bias Auditing
- Model Evaluation
AI Ethics, Fairness, and Responsible AI Build a verifiable portfolio.
Submissions become evidence. Reviewers with shipping experience score against a rubric; the result becomes a credential anyone can verify.
Why Ewance
- AnalysisBeginnerNew
Analyze a Learning-Analytics Dataset for At-Risk Detection
You receive an anonymized dataset of LMS engagement features (logins, assignment submissions, forum posts, video-watch time), grade history, and a binary label for end-of-semest…
- Learning Analytics
- Classification
- Fairness Metrics
AI in Education and Learning Analytics - CodeIntermediateNew
Build a 30-Day Readmission Risk Model on De-Identified EHR Data
You receive a curated MIMIC-style de-identified EHR cohort (about 28,000 admissions, demographics, comorbidities, labs, prior-admission counts) with 30-day readmission labels. T…
- Ehr Modeling
- Risk Stratification
- Model Calibration
Machine Learning for Healthcare and Biomedicine - ResearchIntermediateNew
Run an Adversarial-Robustness Audit on a Face-Liveness Model for a Fintech
You receive a stand-in face-liveness model with the same backbone as the production model plus a labeled evaluation set of 2,000 frames. Apply three standard digital attacks (FG…
- Adversarial Robustness Research
- Face Liveness
- Pytorch Or Tensorflow
Deep Learning for Computer Vision - ResearchFoundationalNew
Ethical Stress Test of an AI Tutoring Startup
Produce a concise ethical risk register and stakeholder impact analysis grounded in publicly available evidence about AI tutors (e.g., Khanmigo studies, UNESCO AI in education g…
- Risk Assessment
- Stakeholder Mapping
- Ai Ethics
AI, Ethics and Society - StrategyIntermediateNew
Going Concern and Evidence Strategy for a UK Retailer
Individually, prepare an audit evidence strategy memorandum addressing Cotswold Home's going concern assessment for the year ending 31 March. Critically evaluate management's 18…
- Going Concern Assessment
- Audit Evidence Evaluation
- Sensitivity & Scenario Analysis
Auditing and Assurance - DesignBeginnerNew
Generative AI Content Strategy for a Sustainable Fashion Brand
You must first define EcoWeave's brand voice by analyzing their existing content (provided). Then, design a set of prompts and a workflow (e.g., using ChatGPT or a no-code AI to…
- Prompt Patterns
- Content Strategy
- Brand Voice
Data-Driven Prototyping with AI - CodeIntermediateNew
Prototype Constitutional-AI Style Guardrails for an Internal Chatbot
Author a 'constitution' of 15 to 20 principles tailored to internal research use (no IP leakage, no off-label medical claims, no personnel-data fishing, etc.). Implement a criti…
- Constitutional Ai
- Alignment Techniques
- LLM Evaluation
AI Safety and Alignment - AnalysisIntermediateNew
Run a Pre-Deployment Fairness + Drift Audit on a Hiring Model
You receive a trained classifier (joblib), the training data sample, and a held-out 'next-month' evaluation set. Compute group fairness metrics (false-positive-rate gap, true-po…
- Fairness Metrics
- Drift Detection
- Bias Mitigation
Machine Learning in Practice - CodeBeginnerNew
Build a Robust Image Classifier for a Climate-Tech Satellite Startup
You receive a labeled dataset of about 25,000 Sentinel-2 patches (positive = illegal construction visible, negative = not). The dataset is split by region AND by season so you c…
- Data Augmentation
- Deep Learning
- Pytorch Or Tensorflow
Advanced Deep Learning - AnalysisIntermediateNew
Audit a Sepsis Early-Warning Model for Subgroup Performance
You receive a pre-trained vendor model, the training-data summary, and a held-out hospital-network evaluation set (about 18,000 ICU stays with sepsis labels). Compute AUROC + AU…
- Model Evaluation
- Fairness Metrics
- Model Calibration
Machine Learning for Healthcare and Biomedicine
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Industry teams behind a decade of practitioner briefs
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Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.