Lesson Report:
Title
Algorithmic Legitimacy in Practice: Applying Beckman’s Principle of Publicity to AI Governance and Drafting an AI Use Policy
Synopsis: The class examined democratic legitimacy in algorithmic decision-making through Beckman’s “principle of publicity� (reason-giving and accessibility) and applied it to the Allegheny child welfare algorithm case. Students then used large language models (LLMs) to draft a university AI-use policy that balances multiple stakeholder views, and evaluated those outputs for democratic legitimacy. The session closed by teeing up a reflection on whether AI-authored rules change students’ perceptions of policy legitimacy.
Attendance
– Students mentioned absent: 0
– Attendance noted: 22 students (6 breakout rooms)
Topics Covered (chronological)
1) Framing the day + democratic legitimacy recap
– Objective: Reconnect theory to practice before an AI-governed activity; review Beckman reading and earlier Allegheny case.
– Legitimacy defined: In democracy, legitimacy = the public accepts that those in power should hold power via accepted processes; it’s a spectrum, not binary. Emphasis on separating “accuracyâ€� of outcomes from “democratic legitimacyâ€� of processes.
– Bridge to Beckman (today’s reading): Beckman asks what governments need to be legitimate and proposes the “principle of publicity.â€�
2) Beckman’s principle of publicity (core theoretical anchor)
– Two requirements for democratic legitimacy:
– Reason-giving: When authorities make decisions/laws affecting people, they must provide reasons explaining why.
– Accessibility: Reasons must be understandable, and the affected party must have the ability to challenge the decision (i.e., a clear, actionable avenue for appeal/revision).
– Application focus: Use Beckman to assess algorithmic/AI policy decision-making.
3) Case application: Allegheny child welfare algorithm (AFST) through Beckman
– Prompt to class (typed responses via “waterfallâ€� chat):
– Q1: Does the Allegheny algorithm meet Beckman’s requirement of reason-giving?
– Synthesis of student responses and instructor guidance:
– Students initially flagged biased inputs and flawed outputs; instructor separated “accuracyâ€� concerns from Beckman’s procedural criteria.
– Consensus: AFST fails reason-giving. It outputs a risk score (a number) without case-specific explanations—no “whyâ€� behind the score.
– Key clarification: Even if the algorithm were perfectly accurate, Beckman would still likely judge it undemocratic if it lacks reason-giving and accessibility.
– Second prompt:
– Q2: Does the AFST meet the requirement of accessibility (understandability + ability to challenge)?
– Consensus: No. Families can’t meaningfully contest the score; caseworkers’ decisions are steered by the number; “it’s a number you can’t fix.â€� No clear challenge/appeal mechanism in the process as implemented.
– Takeaway: AFST is both empirically problematic (data/accuracy issues) and procedurally undemocratic per Beckman’s principle of publicity.
4) Accuracy vs legitimacy (thought experiment + prior poll recall)
– Revisited a prior class poll: Students were split between judgment by a highly accurate AI plagiarism detector vs a committee of professors.
– Discussion link: Many prefer human panels despite lower statistical accuracy due to negotiation, explanation, and appeal—hallmarks of democratic legitimacy.
– Philosophical question posed: Would we accept governance by an undemocratic (but highly accurate) algorithm?
5) Activity 1: “Use AI to draft an AI policy� (LLM-governed exercise)
– Scenario: Students are now university staff tasked with drafting an AI-use policy under time pressure. The president requires a short, clear policy that addresses diverse stakeholder concerns.
– Stakeholder inputs to balance:
– A (professor): Ban all AI—encourages cheating; undermines critical thinking.
– B (student): AI is the future—like banning calculators; teach ethical/effective use.
– C (professor): No ban—design AI-resilient assignments; allow AI for brainstorming/first drafts with disclosure and heavy human revision.
– D (student): Confused by inconsistent rules—wants a clear, university-wide policy spelling out what’s allowed vs not.
– Instructions:
– In groups: pick an LLM and specific model (e.g., ChatGPT, “5â€� vs “5 thinking,â€� Gemini, Claude).
– Compose a prompt instructing the LLM (as a university administrator) to draft a ~200-word policy that synthesizes the four stakeholder positions.
– Include stakeholder quotes/concerns explicitly in the prompt for proper context.
– Generate output; paste into the main chat or a shareable Google Doc (ensure access).
– Logistics: 22 students split into 6 rooms; 5-minute work block; outputs collected via chat and links.
6) Activity 2: Evaluate AI-generated policies for democratic legitimacy
– Task:
– In the same groups, select one of the posted outputs (preferably from another group).
– Evaluate against Beckman’s criteria:
– Reason-giving: Does the policy include clear rationales that connect rules to reasons?
– Accessibility: Is the policy understandable, and does it specify a meaningful, fair process to challenge/appeal decisions?
– Decide whether the policy is democratically legitimate per Beckman.
– Timing: Initial 5 minutes + a 3-minute extension due to time needs.
– Debrief highlights:
– Gavin’s group:
– Reason-giving: Generally adequate. The policy connected rules to underlying rationales (balancing instructor authority and student needs).
– Accessibility: Weak on procedure. Policies lacked explicit steps for dispute resolution/appeals if AI use is challenged or policy is misapplied.
– Observation: Outputs across models were notably similar in structure and language.
– Niloufar’s group:
– Strengths: Clarity on permitted vs prohibited uses reduced confusion; listed training/support for students and faculty.
– Gaps: No detailed appeal process if a student is accused of improper AI use; unclear evidence standards; no guidance on the role/weight of AI detectors; responsibilities of students/faculty mentioned but process-level specificity missing.
– Meta takeaway: LLMs produce passable, “actionableâ€� policy frameworks with decent reason-giving and readability, but they routinely omit the accessibility backbone (concrete appeal pathways, procedural fairness, evidentiary standards, and how to challenge/rectify decisions).
7) Closing reflection + logistics
– Reflection question (for next session): As a student, how do you feel if you learn that a university policy governing you was generated by an AI prompt and copy-pasted into bylaws? Does this change your perception of its legitimacy or quality?
– Schedule: No class next week (Fall Break). Next meeting: Tuesday the 28th.
– Announcements:
– AUCA Outdoor Club (hiking) trip planned in ~2 weeks; IG: AUCA.outdoor/club.
– Paper due on the 1st (confirm exact date/time); citation/formatting style: APSA (Chicago-like with parenthetical in-text citations).
Actionable Items
Urgent (within 48 hours)
– Materials follow-up:
– Resend/confirm delivery of any readings or materials students reported not receiving (one student noted they never received it).
– Consolidate all group policy outputs and prompts into an accessible shared folder; fix any Google Doc permission issues.
– Post-class documentation:
– Share today’s evaluation criteria (reason-giving and accessibility checklists) and debrief notes so groups can revise their policies for accessibility (appeals, evidence standards, challenge processes).
Before next class (post–Fall Break)
– Assessment clarity:
– Confirm the paper due date/time (“the firstâ€�) and submission channel; post a short APSA style cheat-sheet or link to an APSA quick guide.
– Clarify expectations for sources, length, and rubric if not already posted.
– Next-session prep:
– Post the reflection prompt on AI-authored policy legitimacy; optionally collect brief written reflections to seed discussion.
– Select a representative sample of group policies to analyze in plenary, highlighting strong reason-giving and model accessibility procedures (appeals, evidentiary standards, AI detector usage policy).
Longer-term / Course-level
– Policy exercise iteration:
– Ask groups to revise their LLM-generated policies to include explicit accessibility mechanisms:
– A transparent appeal process with steps, timelines, and responsible offices.
– Evidentiary standards for alleged AI misuse (beyond AI detector scores), including student and instructor obligations.
– Student disclosure norms (when/how to cite AI assistance) and instructor course-level declarations.
– Encourage diversity in LLM/model selection to compare how different systems structure policy and whether instruction-tuning affects inclusion of accessibility features.
Homework Instructions:
NO HOMEWORK
Justification: The session consisted of an in-class LLM policy activity and ended with “No classes next week… I will see you in two weeks,� and the only assignment mention was a confirmation of an already-existing paper (“our paper… due on the first�—“The first, yes�; “The style is APSA�), not a new homework assignment.