Democratic Input to AI Demo Day - HackMD

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Democratic Input to AI Demo Day

Some participants info

Lama Ahmad; Open AI risk manager

high level of statement about principle v.s. low level repository

case: potential input to the system

judment: determination of appropriateness

Three phase process

seeding a case repository

ideal ones should be

comprehensive

domain expert / expert workshop

? how to choose the expert?

? are they able to deal with AI issue?

clear

controversial

refine the case repository

engage the public and stakeholders

judge / preturb

focus on clear / controversial ones

? can it fit into different context?

Turning cases into precedents

background: applying the expertise on making cease fire things on AI

quality / representitive

collective dialogue

many polis response

prompt them

educate

how ? what kind of material

facilitate deliberation

? do participants need to have concensus?

elicit view > bridging responses > policy initiative

generate demographic

policy design / intial policy

expert refinement

? why let the expert to participate into the process after the collective dialogue?

policy refinement

making it representive

Human right consistency

Automated pipeline to translate result to regulation

Takeaways:

Collective dialogues permit deliberation at scale

data driven Democratic policy generation

scalable deliberative process -> quality AI policy represnting informed public consesus

AI-enabled tools compressed execution timelines

Next steps

Objective

tackle more contentious issues

scale to global representiveness

integrate into existing AI

Colin

how to balance the representiveness and expertise is hard thing

Q2

how to test? in person

in person: deliberation part can be/ eliciting part still need to get online

education?

exploration / testing / imagine how to teach in the classroom

hyperthesis: representive things first, then quality

two question: how to get input global / formning the consensus

what if we just use google doc?

some sort of memo

too messy to get the input

how do we design the platform to do about the thing?

Three Principles:

Simple

Scalable

Transparent

human readable / machine readable constitution

Steps:

propose a guideline

AI refine the guidelines

avoid partisan language

how to turn it into meaningful output / consensus?

? can it fit into different context?

in cooperation of community note / bridging the algorithm

? is there any adjustment for minority/ cultural-disadvantaged group or just simple by numbers?

Q: How to get the number?

conbination of a deep row to the guidelines

ID / users

invited guest

How are you thinking about transparency and provenance of resulting principles in the public eye? E.g. answering concerns like “I did not vote for these principles!” or “if you change the AI model, would the results be substantially different”?

fully open / open source

generating a fine-tuned model / not policy

hard to make one fine-tuning model under fractional idealogy

Solution: Align model with values not preferences

Value is contextual

Spotting the ideological commitment to underlying value

People are connected with values

three phase:

Articulation part

when chatting, an user can generate the cards

Link the value together

participants will show a story for the context

Convergence

the moral graph can be input to ChatGPT to do fine-tuning

wiser and wiser model of ChatGPT

? how to make sure the result is representitive?

Q:is the moral graph becomeing a cycle ?

it is possible

Q: how to deal with the biased opinion?

Q: representitive stakeholders

different job

Q: How to know if the process goes well with the chat and something like that

the most difficult way is the talk to the chatbot

a web app for deliberation on scale

Challenge:

participating the delibration should be as simple as saying hello

funnel: safe > informed > deliberation > consensus

FInding " that’s right"

Mapping

how to test the matrix

online have been through, and offline is planning

informed people

still exploring and divergence on whether to offer the material for that

when it comes to polarized issues, hard to get consensus, how to deal with

opinion is diversed

summarization setting

social choice theory

LLM can make us do more

generative: find out the unified statement

discriminative: making prediction

process

statement generating

participants ranks the statement

Q: is the process scalable?

some challenges will happen, maybe it will be expensive

8. AI dialogue

rappler:

the background:

global south

every layers matters

inclusion

diversity

integrity

Three parts

Offline FGD

for more nuance to happen

Online FGD

Particitory survey

aiDiologue introduced in AI social good summit

Policy refinement

policy1: human oversight / liability

policy2:

challenge:

language

knowledge on AI

quantifying the coherence / contradiction

? Offline FGDs / Online FGDs

online may make ppl speak something they don’t want to speak out face to face

Problem: AI can amplify value extration

technology don’t need to extract value, but deign and aligned the value.

genrative economy for equitable and inclusive model training

people design and help align the value can grant themselves rewards

inspired by decentralized autonomous organizations

quadratic voting

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