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7 AI for Social Good

What is AI4SG?​

  • Definition: AI research that can deliver societal benefits now or in the near future.

Typical workflow: from research to deployment​

  • Problem → Data → Model/Decision method → Deployment → Evaluation (often RCT) → Iteration

  • Deployment is highly non-trivial (real-time systems + constraints + stakeholders).

Example domain map (from lecture)​

  • Environmental conservation, food rescue, mental/behavioral health, etc.

Example Projects & Key Ideas

Food Rescue Platforms (412 Food Rescue / Food Rescue Hero)​

Goal: notify the “right” volunteers to claim a rescue (food donation pickup), improving operational efficiency.

Baseline vs ML: core metrics​

  • Hit Ratio / HR@k: % of rescues claimed by the k notified volunteers.

  • RCT deployment results (example table):

    • Control: Hit Ratio 0.468, Claim Ratio 0.807

    • ML: Hit Ratio 0.651 (p=0.001), Claim Ratio 0.882 (p=0.047)

Why “pure recommender” caused problems​

  • Caveat: ML model tends to over-notify a small set of frequent “super volunteers”.

  • Homework framing: the key issue that motivates online planning is notification fatigue.

Fix: ML + Online Planning (notification budget)​

  • Add constraint: each volunteer receives at most L notifications per day; plan with projected future rescues.

  • With L = 5, avoids over-concentration; HR@k reported as 0.645 (better than current practice).

    oaicite:8

Mental Health: Simulated Patient for CBT Training (PATIENT-Ψ)​

Goal: train mental health professionals via a consistent simulated patient.

  • System idea: LLM + a detailed “Cognitive Model” (history, core beliefs, emotions, etc.) to create a consistent patient.

  • Lecture reference: PATIENT-Ψ (EMNLP 2024).

Behavioral Health: PeerCoPilot (resource recommendation for peer providers)​

Goal: help peer providers give reliable, verifiable resources; reduce cognitive burden.

  • Purpose statement: improve efficiency, reduce cognitive burden, improve service quality.

  • Approach: LLM + RAG-based resource recommendation + benefit eligibility checker (homework wording).

  • User study results shown in lecture:

    • PeerCoPilot: Contact provided 100%, Bad link 0%

    • Baseline: Contact 56%, Bad link 11%

  • Willingness to use: all peer providers & service users willing to use PeerCoPilot; deployed now.