Human Responsibility
in the Age of AI
Kasia Chmielinski | March 2024
Shared
1
2010
Created with DALL-E
What is my responsibility
as a builder of
AI systems?
Artificial Intelligence
The simulation of human intelligence by machines that are programmed to perform complex cognitive functions
AI is not new ...
1st iPhone
AI is coined as a term
Turing
Test
1st micro-
processor
Hyperlink Invented
Windows �1.0 Launch
2024
2000
1975
1950
Google Launches
Decision Trees
Multilayer Perceptron
Random Forest
Deep �Learning
Models
Back-
propagation
1st home
smoke alarm
1st
floppy disk
1st man on the moon
... nor are our fears ...
“AI Poses 'Risk of Extinction,' Industry Leaders Warn” (NYT, 2023)
Murderous HAL! from 2001: A Space Odyssey (1968)
“Why AI is a Dangerous Dream” (New Scientist, 2009)
“Will man-made robots� rise up and demand their rights?“(MIT, 2000)
Introducing Terminator:
A cyborg assassin (1984)
2024
2000
1975
1950
AI is coined as a term
But AI is getting more powerful, more quickly
2024
2000
1975
1950
1M peta
10k peta
100 peta
1 peta
10T
100B
1B
10M
100k
1000
10
Training Computation �in FLOPS
(floating point �operations per second)
Minerva (2.78B PetaFLOPS)
GPT-3 (314 M PetaFLOPS)
Theseus (40 FLOPS)
AlexNet (470 PetaFLOPS)
Sources: “Compute trends across three eras of machine learning”, by J.Sevilla et al., arXiv, 2022; Our World in Data
... due to increases in computing power
But AI is getting more powerful, more quickly
2025
2020
2015
2010
200
150
100
50
2 Zettabytes
Of Data (2010)
181 Zettabytes of Data (est 2025)
© Statista 2023
Volume of Digitized Data Worldwide
(in Zettabytes of data, 2010-2025)
... due to increases in data availability
Reminiscent of the mobile app landscape,
CLOUD AND COMPUTE SERVICES
AWS, Azure, GPUs, TPUs
FOUNDATION MODELS
GPT, Gemini, Miqu 70B, Llama2, DALL-E, Stable Diffusion
APPLICATIONS
ChatGPT, Copilot, AlphaCode, Scribe, �Synthesia, Sora
APP PLATFORMS
APPLICATIONS
HARDWARE AND CLOUD
Mobile Apps Landscape
Generative AI Apps Landscape
users
Generative AI-powered consumer services are emerging
AI is not new, though it is increasingly powerful and growing rapidly
AI is not new, though it is increasingly powerful and growing rapidly
... and it’s not just what we’re building, but how we’re doing it
Created with DALL-E
Products are market driven, which means we optimize for growth. This means building for the “majority” at the expense of others
14
Build for the “ideal user” and iterate out over time
80% solution
for “majority”
Frustratingly, most of the focus (hype) is on �existential future risks rather than actual harms
Yes, and.... we need to focus on
the consolidation of power and control in the hands of the few
AI is not new, though it is increasingly powerful and growing rapidly
... and it’s not just what we’re building, but how we’re doing it
The real risks are products
that fail communities at scale
And the further consolidation of power in the hands of the few
2017
Created with DALL-E
2017
23
AI Model Pipeline
Systems built on �problematic data will �exhibit those same issues, �especially for historically �marginalized people
24
Systems built on �problematic data will �exhibit those same issues, �especially for historically �marginalized people
AI Model Pipeline
Shouldn’t we interrogate for issues before we build?
The Dataset Nutrition Label
A standardized documentation tool that tells you what’s in a dataset and whether it’s healthy for your model
26
The label includes at-a-glance information about key critical aspects of the dataset as well as usage information and known risks by category.
“... While I know that the primary mission of the DNP is to improve the understanding, searching, and consumption of datasets by users of datasets, it has also been key to improving my dataset design moving forward.”
— Dataset Partner
AI Systems are socio-technical
Within the full system, there are many sites of risk for bias and therefore many sites of intervention
(Partnership on AI, 2022)
AI Systems are socio-technical
Within the full system, there are many sites of risk for bias and therefore many sites of intervention
(Partnership on AI, 2022)
What is our responsibility
your opportunity
as a user of AI systems?
Tip 1:�
Think about AI
as a process, not a product
31
When you are �procuring systems...
Ask about how the model was tested
Ask about how the model will be monitored and updated
Ask about the training data
Ask about the success criteria for the model
Ask about criteria for decommission
Ask whether this is the right problem to address with AI
Tip 2:�
Think about AI as another tool in your toolkit, not something totally new
33
Look to existing policies ...
Attribution and disclosure policies (plagiarism, copyright)
New tools requirements (security, confidentiality)
Freeware vs. Enterprise software (licenses, support, security)
Use policies (calculators, digital editing, photoshop)
Tip 3:�
Think about AI in the context of
new cultural norms
35
Cultural Norms
New technology requires a shift in norms
around how we use and expect others to use the tools
Example:
Using AI to Create
Internal vs. External material
Keep a human in the loop �for anything published or launched externally
Example:
Care with exposing confidential information
Do not put any confidential information into Generative AI tools because they may retain (and regurgitate) this information for other users
Example:
Community to Share
Challenges & Opportunities
You’re not alone! Find others who are navigating similar changes in your domain
https://aipedagogy.org
AI is not new, and neither are �our concerns about it.
But what is changing is the
scale and power of these systems
Compounded with �how we build product
Means that the real risk are
products that fail communities
And the consolidation of the power
in the hands of the few.
We need to remember that humans build the technology
If we remember that AI is a process, �we - and you! -
can meaningfully intervene
Thank you!
Kasia Chmielinski | March 2024
Slides:
Or get in touch!
kc@datanutrition.org