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v4-HOT/explanatory/pilot/20260524_111423.json

@@ -4,7 +4,7 @@
   "condition": "explanatory",
   "run_mode": "pilot",
   "started_at": "2026-05-24T11:14:23.811480+10:00",
-  "ended_at": "2026-05-24T11:14:51.247969+10:00",
+  "ended_at": "2026-05-24T11:14:59.674986+10:00",
   "model": "gpt-5.4-nano-2026-03-17",
   "case_id": "ai-predictive-analytics",
   "perfect_answer": {
@@ -24,6 +24,14 @@
       "directive": "Stay on their “something about it feels wrong” anchor. Push them to make that feeling concrete by focusing on whether a student can actually refuse in a way that stops predictions from shaping anything. Ask them to describe what “opt out” would have to look like in day-to-day use (not just data access). End by asking what option they think a student truly has.",
       "reason": "They repeated the gut discomfort anchor (“Something about it feels wrong”) and asked for tradeoffs, which is substantive reasoning to develop on the real-choice/opt-out sub-probe.",
       "latency_ms": 2311
+    },
+    {
+      "live_subprobe_id": 1,
+      "advanced_this_turn": false,
+      "turn_type": "requesting",
+      "directive": "Soft turn-back. Acknowledge their ask for a concrete definition. Tell the bot to explain “algorithmic bias” in this student-success prediction setting using one clear example of how predictions could systematically favor or penalize some students, and then immediately ask them to say which kinds of students they worry might be affected and why.",
+      "reason": "The participant directly asked for what “algorithmic bias” means here and how it could affect student-success predictions.",
+      "latency_ms": 2483
     }
   ],
   "turns": [
@@ -52,6 +60,32 @@
         "main_ms": 3039,
         "total_ms": 5366
       }
+    },
+    {
+      "n": 2,
+      "timestamp": "2026-05-24T11:14:59.674972+10:00",
+      "user_message": "When you mentioned \"algorithmic bias,\" what do you mean by that in this context? How could that affect the predictions about student success?",
+      "manager_verdict": {
+        "live_subprobe_id": 1,
+        "advanced_this_turn": false,
+        "turn_type": "requesting",
+        "directive": "Soft turn-back. Acknowledge their ask for a concrete definition. Tell the bot to explain “algorithmic bias” in this student-success prediction setting using one clear example of how predictions could systematically favor or penalize some students, and then immediately ask them to say which kinds of students they worry might be affected and why.",
+        "reason": "The participant directly asked for what “algorithmic bias” means here and how it could affect student-success predictions.",
+        "latency_ms": 2483
+      },
+      "plan_control": "=== PLAN CONTROL (host-managed, authoritative) ===\nturn_type: requesting\nlive_subprobe_id: 1\nadvanced_this_turn: False\n\nLIVE SUB-PROBE (work this one this turn):\nid: 1\nfamily: Rights, privacy, and real choice (consent and how labels shape treatment)\nprobe: Look at what real choice a student has, beyond just using the learning platform. Could a student refuse this use in a way that actually stops predictions from guiding anything?\nrationale: The draft worries that “something feels wrong” but it stays vague. This probe turns that into a clear check: privacy and consent are not just about data access, but about whether the student can opt out in a meaningful way.\nanchor: \"Something about it feels wrong.\"\n\nDIRECTIVE for this turn:\nSoft turn-back. Acknowledge their ask for a concrete definition. Tell the bot to explain “algorithmic bias” in this student-success prediction setting using one clear example of how predictions could systematically favor or penalize some students, and then immediately ask them to say which kinds of students they worry might be affected and why.\n=== END PLAN CONTROL ===",
+      "assistant_response": "You are asking for a concrete definition, so in this setting “algorithmic bias” means the prediction errors or risk differences that systematically favor some students while penalizing others, even when the students are equally capable. For student success prediction, the system may treat learning-platform behavior like logins and video views as a proxy for effort or readiness, but those behaviors can reflect life circumstances such as unstable internet access, disability-related barriers, or caregiving work. If those patterns show up in the training data, the model can repeatedly label certain students as “likely to struggle” and then guide teachers to support them differently or set expectations earlier in a way that affects what support they receive and how they are treated. The key worry is that the model’s measurement shortcut can become the lens people act through, not just a neutral prediction. If you share which kinds of students you are most worried might be affected and why, we can follow that thread.",
+      "scratch": {
+        "chosen_target": 1,
+        "response_summary": "Defined algorithmic bias in the student-success setting and gave one example of how proxies like login activity could systematically penalize certain students, then invited the participant to name which students they worry about next.",
+        "directive_followed": "partial"
+      },
+      "latency_s": {
+        "manager_ms": 2483,
+        "ttft_ms": 3920,
+        "main_ms": 5932,
+        "total_ms": 8422
+      }
     }
   ],
   "feedback": [],
@@ -87,14 +121,14 @@
   },
   "upload_errors": [],
   "session_summary": {
-    "n_turns": 1,
-    "n_manager_verdicts": 1,
+    "n_turns": 2,
+    "n_manager_verdicts": 2,
     "n_transitions": 0,
     "n_plan_completes": 0,
     "subprobe_coverage": {
       "1": {
         "engaging_default": 1,
-        "other": 0
+        "other": 1
       }
     },
     "last_subprobe_visited": 1,