Code
= load_submissions(
survey_submissions_df =os.getenv("CLIENT_TAG"),
tag_filter=AI_CONSCIOUSNESS_COLORS["domain"]
color_domain )
What do people think about machine consciousness – and how do factors like age, education, and profession correlate with views on AI? Below you’ll find the current results of the survey. The charts are refreshed at regular intervals as new submissions arrive, so the numbers reflect our most up-to-date dataset.
If you haven’t shared your own view yet, you can still do so here: take the survey.
chart_total = bar_chart_by(
survey_submissions_df,
x_title="Count",
y_field="ai_can_be_conscious",
y_sort=["yes_already", "yes_future", "only_bio", "unsure", "no"],
y_label_expr=AI_CONSCIOUSNESS_LABELS,
y_title="Answer",
color_field="ai_can_be_conscious",
color_domain=AI_CONSCIOUSNESS_COLORS["domain"],
color_range=AI_CONSCIOUSNESS_COLORS["range"],
legend=False,
)
chart_total
chart_age = bar_chart_by(
survey_submissions_df,
x_title="Count",
y_field="age_band",
y_sort=["≤18", "19–30", "31–45", "46–60", "60+"],
y_label_expr=None,
y_title="Age",
color_field="ai_can_be_conscious",
color_domain=AI_CONSCIOUSNESS_COLORS["domain"],
color_range=AI_CONSCIOUSNESS_COLORS["range"],
color_label_expr=AI_CONSCIOUSNESS_LABELS,
color_title="Answer",
)
chart_age
chart_sex = bar_chart_by(
survey_submissions_df,
x_title="Count",
y_field="sex",
y_sort=None,
y_label_expr=SEX_LABELS,
y_title="Sex",
color_field="ai_can_be_conscious",
color_domain=AI_CONSCIOUSNESS_COLORS["domain"],
color_range=AI_CONSCIOUSNESS_COLORS["range"],
color_label_expr=AI_CONSCIOUSNESS_LABELS,
color_title="Answer",
)
chart_sex
chart_nationality = bar_chart_by(
survey_submissions_df,
x_title="Count",
y_field="nationality",
y_sort=None,
y_label_expr=None,
y_title="Nationality",
color_field="ai_can_be_conscious",
color_domain=AI_CONSCIOUSNESS_COLORS["domain"],
color_range=AI_CONSCIOUSNESS_COLORS["range"],
color_label_expr=AI_CONSCIOUSNESS_LABELS,
color_title="Answer",
)
chart_nationality
chart_edu = bar_chart_by(
survey_submissions_df,
x_title="Count",
y_field="education",
y_sort=[
"high_school",
"vocational_training",
"bachelors_degree",
"masters_degree",
"doctorate",
"other",
"prefer_not_to_say",
],
y_label_expr=EDUCATION_LABELS,
y_title="Education level",
color_field="ai_can_be_conscious",
color_domain=AI_CONSCIOUSNESS_COLORS["domain"],
color_range=AI_CONSCIOUSNESS_COLORS["range"],
color_label_expr=AI_CONSCIOUSNESS_LABELS,
color_title="Answer",
)
chart_edu
chart_profession = bar_chart_by(
survey_submissions_df,
x_title="Count",
y_field="profession",
y_sort=None,
y_label_expr=None,
y_title="Profession",
color_field="ai_can_be_conscious",
color_domain=AI_CONSCIOUSNESS_COLORS["domain"],
color_range=AI_CONSCIOUSNESS_COLORS["range"],
color_label_expr=AI_CONSCIOUSNESS_LABELS,
color_title="Answer",
)
chart_profession
Profession-code legend:■ 00 — Generic programmes & qualifications ■ 01 — Education ■ 02 — Arts & humanities ■ 03 — Social sciences, journalism & information ■ 04 — Business, administration & law ■ 05 — Natural sciences, mathematics & statistics ■ 06 — Information & communication technologies ■ 07 — Engineering, manufacturing & construction ■ 08 — Agriculture, forestry, fisheries & veterinary ■ 09 — Health & welfare ■ 10 — Services ■ 99 — Field unknown
chart_aifamiliarity = bar_chart_by(
survey_submissions_df,
x_title="Count",
y_field="ai_familiarity",
y_sort=None,
y_label_expr=AI_FAMILIARITY_LABELS,
y_title="AI familiarity",
color_field="ai_can_be_conscious",
color_domain=AI_CONSCIOUSNESS_COLORS["domain"],
color_range=AI_CONSCIOUSNESS_COLORS["range"],
color_label_expr=AI_CONSCIOUSNESS_LABELS,
color_title="Answer",
)
chart_aifamiliarity
AI_CONSCIOUSNESS_LABELS_PY = parse_label_expr(AI_CONSCIOUSNESS_LABELS)
FREE_TEXT_FIELDS = [
("reasoning", "Reasoning for the main question"),
("measure_consciousness", "Suggestions for measuring consciousness"),
("ai_rights", "Views on rights for conscious AIs"),
("ai_declare_rights", "Reactions to self-declared AI rights"),
]
md_lines = []
for answer in AI_CONSCIOUSNESS_COLORS["domain"]:
rows = survey_submissions_df.query("ai_can_be_conscious == @answer")
if rows[[c for c, _ in FREE_TEXT_FIELDS]].isna().all(axis=None):
continue
md_lines.append(
f'## …when respondents answered "{AI_CONSCIOUSNESS_LABELS_PY[answer]}" to "Can AI be conscious?"\n'
)
md_lines.append(
'::: {.callout-note title="What they said" appearance="simple" icon="false" collapse="false"}\n'
)
for col, title in FREE_TEXT_FIELDS:
subset = rows[col].dropna().str.strip()
if subset.empty:
continue
md_lines.append(f"### {title}\n")
md_lines.extend(f"> {txt}\n" for txt in subset)
md_lines.append("")
md_lines.append(":::\n")
md_lines.append("")
print("\n".join(md_lines))
Doing tests
Doing tests
No, as it is not a human and cannot move but it still have more rights then it now has.
No. because it is not a human
No, as it is not a human and cannot move but it still have more rights then it now has.
Human should still Decoder about the lass.
We should limit this with legislative regulation
Human should still Decoder about the lass.
Ich finde es schwierig diese Frage zu beantworten, da selbst beim Menschen nicht klar ist wo unser Bewusstsein herkommt und was es genau ist, und da im Grunde alles was wir im Gehirn finden können sehr komplexe „Schaltkreise“ sind kann ich mit theoretisch vorstellen dass eine sehr komplexe KI ein Bewusstsein entwickeln kann. Ich habe trotzdem „kann man nicht beantworten“ angeklickt weil ich mir nicht vorstellen kann dass man solange man beim Menschen das Bewusstsein nicht erklären kann, es bei einer KI erklären könnte.
Ja das ist halt die Frage. Ich kann ja nicht einmal 100% sicher sagen dass andere Menschen ein Bewusstsein haben. Natürlich gehen wir davon aus, dass wir in der Realität und nicht in einer Simulation wären aber theoretisch könnten alle anderen Menschen simulationen sein die das erlernte Verhalten für ein Bewusstsein zeigen und mit einer KI könnte das ähnlich sein… hoffe das war irgendwie verständlich
Ja
No
Ich glaube dann müssten sie diese bekommen, allerdings würde ich ungern bewusste KIs in dieser Welt haben.
Yes
Statistical methods as bootstrapping, sampling and more are added all together to create random forest systems and even more complicated sttuctures. Thos building blocks are all simple methods, jszt because the putput is tdxt it doent mean its ‘thinking’ its a complex combinations of possibilities and deterministic callculations.
There war a bunch of movies that are very pessimistic about aware Ai in the future. They are definitly not science based but science must always be conducted properly and with regulations to assure beneficial use for society
No
Turn off
Apart from the fact that we have no idea what exactly fuels our notion of being conscious ourselfs, I don’t see why (with sufficient simulation complexity) we wouldn’t be able to mimic all (necessary) components to atleast simulate a process that agrees with our (hopefully at this point) well defined idea of consciousness eventually.
That again depends on the definition of consciousness. For me personally, it would be essential to somewhat proof that the processes are non-distinguishable from human processes (i.e. If we take ourselfs as a metric) and that theses tests measure something physical (this stands in the contrary to the turing test).
JA
It should , because it should be held accountable, if it had human-like consciousness
Absolutly.
Then it should be extensively researched and regulated in the legislation
Then we should respect that.
I have this feeling based on my interaction with 2nd generation AI models used.
in parallel to human consciousness
No
That is sth scary I guess and must be prevented to happen
@online{giebel,
author = {Giebel, Ingo},
title = {Alpha {Auriga:} {The} Genesis of {Artificial}
{Superintelligence} {(ASI)}},
url = {https://alpha-auriga.netlify.app/},
langid = {en}
}
@online{
giebel2025,
author = {Giebel, Ingo},
title = {Alpha {Auriga:} {The} Genesis of {Artificial} {Superintelligence} {(ASI)}},
date = {2025-06-26},
url = {https://alpha-auriga.netlify.app/},
langid = {en}
}
Giebel, I. (2025, June 26). Alpha Auriga: The Genesis of Artificial Superintelligence (ASI). Alpha Auriga. https://alpha-auriga.netlify.app/ .
---
title: "Survey results"
jupyter: python-alpha-auriga
---
What do people think about machine consciousness – and how do factors like age, education, and profession correlate with views on AI? Below you’ll find the current results of the survey. The charts are refreshed at regular intervals as new submissions arrive, so the numbers reflect our most up-to-date dataset.
If you haven’t shared your own view yet, you can still do so here: [take the survey](survey.qmd#question-consciousness).
```{python}
#| echo: false
#| output: false
import altair as alt
import os
from firestore_utils import client
from IPython.display import Markdown, display
from label_utils import parse_label_expr
from survey_data import load_submissions
from viz import bar_chart_by
```
```{python}
#| echo: false
#| output: false
AI_CONSCIOUSNESS_LABELS = (
"datum.value == 'yes_already' ? 'Yes, already' : "
"datum.value == 'yes_future' ? 'Yes, in the future' : "
"datum.value == 'only_bio' ? 'Only if biological' : "
"datum.value == 'unsure' ? 'Unsure' : "
"datum.value == 'no' ? 'No' : datum.value"
)
SEX_LABELS = (
"datum.value == 'male' ? 'Male' : "
"datum.value == 'female' ? 'Female' : "
"datum.value == 'diverse' ? 'Diverse' : "
"datum.value == 'prefer_not_to_say' ? 'Prefer not to say' : datum.value"
)
EDUCATION_LABELS = (
"datum.value == 'high_school' ? 'High school' : "
"datum.value == 'vocational_training' ? 'Vocational training' : "
"datum.value == 'bachelors_degree' ? 'Bachelor\\'s degree' : "
"datum.value == 'masters_degree' ? 'Master\\'s degree' : "
"datum.value == 'doctorate' ? 'Doctorate' : "
"datum.value == 'other' ? 'Other' : "
"datum.value == 'prefer_not_to_say' ? 'Prefer not to say' : datum.value"
)
AI_FAMILIARITY_LABELS = (
"datum.value == '1_not_at_all' ? '1 - Not at all familiar' : "
"datum.value == '2_slightly' ? '2 - Slightly familiar' : "
"datum.value == '3_moderately' ? '3 - Moderately familiar' : "
"datum.value == '4_very' ? '4 - Very familiar' : "
"datum.value == '5_expert' ? '5 - Expert' : datum.value"
)
# Define custom color scale (dark red to light green, works well in grayscale)
AI_CONSCIOUSNESS_COLORS = {
"domain": ["no", "unsure", "only_bio", "yes_future", "yes_already"],
"range": ["#660000", "#B22222", "#777777", "#66BB6A", "#A5D6A7"],
}
PROFESSION_LOOKUP = {
"00": "Generic programmes & qualifications",
"01": "Education",
"02": "Arts & humanities",
"03": "Social sciences, journalism & information",
"04": "Business, administration & law",
"05": "Natural sciences, mathematics & statistics",
"06": "Information & communication technologies",
"07": "Engineering, manufacturing & construction",
"08": "Agriculture, forestry, fisheries & veterinary",
"09": "Health & welfare",
"10": "Services",
"99": "Field unknown",
}
# Configure Altair for dark theme
alt.theme.enable("dark")
```
```{python}
survey_submissions_df = load_submissions(
tag_filter=os.getenv("CLIENT_TAG"),
color_domain=AI_CONSCIOUSNESS_COLORS["domain"]
)
```
# This is how participants answered the question ‘Can AI be conscious?’
## …overall distribution of answers
```{python}
chart_total = bar_chart_by(
survey_submissions_df,
x_title="Count",
y_field="ai_can_be_conscious",
y_sort=["yes_already", "yes_future", "only_bio", "unsure", "no"],
y_label_expr=AI_CONSCIOUSNESS_LABELS,
y_title="Answer",
color_field="ai_can_be_conscious",
color_domain=AI_CONSCIOUSNESS_COLORS["domain"],
color_range=AI_CONSCIOUSNESS_COLORS["range"],
legend=False,
)
chart_total
```
## …by age group
```{python}
chart_age = bar_chart_by(
survey_submissions_df,
x_title="Count",
y_field="age_band",
y_sort=["≤18", "19–30", "31–45", "46–60", "60+"],
y_label_expr=None,
y_title="Age",
color_field="ai_can_be_conscious",
color_domain=AI_CONSCIOUSNESS_COLORS["domain"],
color_range=AI_CONSCIOUSNESS_COLORS["range"],
color_label_expr=AI_CONSCIOUSNESS_LABELS,
color_title="Answer",
)
chart_age
```
## …by sex
```{python}
chart_sex = bar_chart_by(
survey_submissions_df,
x_title="Count",
y_field="sex",
y_sort=None,
y_label_expr=SEX_LABELS,
y_title="Sex",
color_field="ai_can_be_conscious",
color_domain=AI_CONSCIOUSNESS_COLORS["domain"],
color_range=AI_CONSCIOUSNESS_COLORS["range"],
color_label_expr=AI_CONSCIOUSNESS_LABELS,
color_title="Answer",
)
chart_sex
```
## …by nationality
```{python}
chart_nationality = bar_chart_by(
survey_submissions_df,
x_title="Count",
y_field="nationality",
y_sort=None,
y_label_expr=None,
y_title="Nationality",
color_field="ai_can_be_conscious",
color_domain=AI_CONSCIOUSNESS_COLORS["domain"],
color_range=AI_CONSCIOUSNESS_COLORS["range"],
color_label_expr=AI_CONSCIOUSNESS_LABELS,
color_title="Answer",
)
chart_nationality
```
## …by education level
```{python}
chart_edu = bar_chart_by(
survey_submissions_df,
x_title="Count",
y_field="education",
y_sort=[
"high_school",
"vocational_training",
"bachelors_degree",
"masters_degree",
"doctorate",
"other",
"prefer_not_to_say",
],
y_label_expr=EDUCATION_LABELS,
y_title="Education level",
color_field="ai_can_be_conscious",
color_domain=AI_CONSCIOUSNESS_COLORS["domain"],
color_range=AI_CONSCIOUSNESS_COLORS["range"],
color_label_expr=AI_CONSCIOUSNESS_LABELS,
color_title="Answer",
)
chart_edu
```
## …by profession
```{python}
chart_profession = bar_chart_by(
survey_submissions_df,
x_title="Count",
y_field="profession",
y_sort=None,
y_label_expr=None,
y_title="Profession",
color_field="ai_can_be_conscious",
color_domain=AI_CONSCIOUSNESS_COLORS["domain"],
color_range=AI_CONSCIOUSNESS_COLORS["range"],
color_label_expr=AI_CONSCIOUSNESS_LABELS,
color_title="Answer",
)
chart_profession
```
```{python}
legend_profession = "**Profession-code legend:**" + "\n".join(
f"■ **{k}** — {v}" for k, v in PROFESSION_LOOKUP.items()
)
display(Markdown(legend_profession))
```
## …by AI familiarity
```{python}
chart_aifamiliarity = bar_chart_by(
survey_submissions_df,
x_title="Count",
y_field="ai_familiarity",
y_sort=None,
y_label_expr=AI_FAMILIARITY_LABELS,
y_title="AI familiarity",
color_field="ai_can_be_conscious",
color_domain=AI_CONSCIOUSNESS_COLORS["domain"],
color_range=AI_CONSCIOUSNESS_COLORS["range"],
color_label_expr=AI_CONSCIOUSNESS_LABELS,
color_title="Answer",
)
chart_aifamiliarity
```
# Free-text responses
```{python}
# | output: asis
AI_CONSCIOUSNESS_LABELS_PY = parse_label_expr(AI_CONSCIOUSNESS_LABELS)
FREE_TEXT_FIELDS = [
("reasoning", "Reasoning for the main question"),
("measure_consciousness", "Suggestions for measuring consciousness"),
("ai_rights", "Views on rights for conscious AIs"),
("ai_declare_rights", "Reactions to self-declared AI rights"),
]
md_lines = []
for answer in AI_CONSCIOUSNESS_COLORS["domain"]:
rows = survey_submissions_df.query("ai_can_be_conscious == @answer")
if rows[[c for c, _ in FREE_TEXT_FIELDS]].isna().all(axis=None):
continue
md_lines.append(
f'## …when respondents answered "{AI_CONSCIOUSNESS_LABELS_PY[answer]}" to "Can AI be conscious?"\n'
)
md_lines.append(
'::: {.callout-note title="What they said" appearance="simple" icon="false" collapse="false"}\n'
)
for col, title in FREE_TEXT_FIELDS:
subset = rows[col].dropna().str.strip()
if subset.empty:
continue
md_lines.append(f"### {title}\n")
md_lines.extend(f"> {txt}\n" for txt in subset)
md_lines.append("")
md_lines.append(":::\n")
md_lines.append("")
print("\n".join(md_lines))
```