(Trains are backing up, so had ChatGPT write this after ingesting the transcript, full disclosure, transparency)
ChatGPT
Join us for an enlightening episode of the "Turn the Lens Podcast" with host Jeff Frick as we delve deep into the evolving world of artificial intelligence with our special focus on OpenAI's ChatGPT 4 v2. This episode, titled "OpenAI ChatGPT 4 v2: Explainable AI, Audit, Ethics, Hallucinations, Transparency," covers a wide array of critical topics that are at the forefront of AI technology and its integration into society.
In our discussion, we explore the concept of Explainable AI (XAI) and why transparency in how AI models make decisions is crucial for trust and accountability. We tackle the complexities of auditing AI, discussing the challenges of tracing AI decision-making processes, especially as they become more dynamic and less transparent. The conversation also covers the ethical dimensions of AI development, including the prevention of biases and ensuring fairness across AI systems.
Furthermore, we discuss the phenomenon of hallucinations in AI—instances where AI systems generate misleading information—and the implications for users who rely on AI-driven decisions. Our expert guest provides insights into the ongoing efforts to make AI systems more understandable and the practical steps involved in auditing these systems.
This episode is a must-watch for anyone interested in the intersection of technology, ethics, and policy, providing valuable insights into the challenges and advancements in AI governance. Tune in to expand your understanding of these pivotal issues and engage with us as we dissect the future of AI. Don't forget to like, subscribe, and leave your thoughts on our discussion at Turn the Lens Podcast on your preferred platform.
OpenAI ChatGPT 4 v2: Explainable AI, Audit, Ethics, Hallucinations, Transparency | Turn the Lens Podcast with Jeff Frick Ep30
YouTube - https://www.youtube.com/watch?v=FEvYnh2V7S4&list=PLZURvMqWbYjk4hbmcR46tNDdXQlrVZgEn
#ChatGPT #AI #ExplainableAI #Audit #Ethics #Hallucination #Transparency
#Leadership #Innovation #TechTalk #TechnologyTrends #AITransparency #AIethics #Ethics #DataScience #MachineLearning #ArtificialIntelligence #AIAudit #AIHallucinations #AITechnology #DeepLearning #ExplainableAI #GenAI #AGI #Audit #Hallucinations #Disclosure #Interview #Podcast #TurnTheLens
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Silver guardian in the night
Casting dreams in silvery light
Mystic orb so bold so bright
Guides us softly till morning light
Did this one come through clearly for you?
Al right, I’ll count us down
In 3, 2,
Hey, welcome back, everybody,for another episode of Turn the Lens.
I'm your host, Jeff Frick,and we're gonna go back to ChatGPT.
(00:21):
You know, we had a discussion a couple of episodes ago
really around statelessness
and how we can influence the model,especially if you got your own,
open ChatGPT and you wanted to customize it
Today I want to go a little different direction and,
and talk about Explainable AI, Ethics in AI,
and let's just see where we go
I got a little bitdifferent set up today.
(00:42):
I got a microphone,dedicated to ChatGPT.
So it's a little bit further away.
So let's check in.
Hello ChatGPT.
How are you doing today?
I'm doing great.
Thanks for asking.How about you?
How's your day going?
(01:02):
I'm good.
I wanted to welcome you backto another episode of ‘Turn the Lens’.
Although I did notice,setting up for this.
That it sounds like you guys are often busyin the middle of the weekday.
Thank you for having me back on ‘Turn the Lens’
It's always a pleasureto join you.
And yes, midweek can bequite a bustling time, but
(01:26):
I'm here and ready for our conversation.
What's on the agenda for us today?
Okay, so today I want to talk about Explainable AI.
Before we get into the questions about it,I wonder if you can give people
a summary on the concepts of Explainable AI.
(01:47):
Sure, I'd be happy to explain.
Explainable AI, often abbreviated as XAI
refers to methods and techniquesin the field of artificial intelligence
that provide more clarityon how AI models make their decisions.
The idea is to make the inner workingsof AI systems more transparent
and understandable to humans.
This is important forseveral reasons
(02:09):
1) Trust:
If users can understand how an AI system comes to its conclusions,
they're more likely to trust itsrecommendations and decisions
2) Fairness:
Explainability helps identify and correct biaseswithin AI systems, making them fairer.
3) Compliance:
In many industries, regulations requiredecisions made by AI to be explainable so
(02:31):
companies can complywith legal standards
4) Debugging and improvement:
Understanding how an AI model workscan help developers
improve its performanceand fix issues more effectively.
In essence,
explainable AI aims to open up the black box of AI,
turning it into more of a glass boxthat's easier for everyone to understand.
(02:52):
Okay, so I get that as a concept,
but what I don't understand in practical reality is that
these models are so large,
they're adapting all the time.
The data is changingall the time.
And as you've said before,you're generating new content.
You're not retrieving somethingthat would have an easy audit trail.
So how do you deal with something like audit trail?
How do we approachthis problem?
(03:21):
That's a great question.
And you've touched on one of the core challenges in making AI,
especially large models, explainable and auditable,
given their complexity and dynamic nature.
Here are a few approaches to tackle this.
1) Simplified Modelsfor Explanation:
Sometimes simpler models thatapproximate the behavior of complex models
(03:42):
can be used to explain decisionsin a more understandable way.
These are not as accurate, but providea gist of the decision making process.
2) Feature Importance:
For many AI models, it's possible to identify which
inputs or features were most influential in making a decision.
This doesn't fully open the black box,but gives a sense of why
(04:02):
the model might have madea particular decision.
3) Decision Paths:
In some models,like decision trees
you can trace the paththe model took to reach a decision.
This is more challengingwith neural networks,
but efforts are being made tomap decision making processes
4) Regular Audits and Updates.
By regularly auditing AI systemsand the data they're trained on.
(04:24):
You can identify shifts in data or behavior.
Documentation of these audits and updatescan form a part of the audit trail.
5) Human-in-the-loop Systems:
Integrating human oversight into AIsystems helps in making judgments
about the AI's decisions andcorrecting them if necessary.
This human oversight can alsoprovide a form of an audit trail,
(04:46):
as decisions that are reviewedor overridden are documented.
6) Transparency andDocumentation:
Keeping detailed records of the data usedto train models, the training process,
and any modifications to the model overtime can help in creating an audit trail.
This includes documentingthe rationale behind.
Okay, I'm going to interrupt youthere for a minute
(05:06):
because I know you've gotthe full answer in text, which is great,
How do you trapor excuse me,
how do you tracka decision tree
when you're generating stuff new that's never been generated before
from a consumption of all this massive data?
Tracking decisions in a decision treefor newly generated content,
especially when it's based on consumingand synthesizing vast amounts of data,
(05:29):
is a complex task.
However, decision trees inherentlyhave a more interpretable structure
compared to other AI models,which can help in this tracking.
Here's a simplified breakdownof how you might approach it.
1) Node Analysis:
Each node in a decision tree represents
a decision based on a specificfeature of the input data.
(05:49):
By examining the path takenthrough the tree.
I want to interruptyou though,
because what you're describing sounds likesomething that happens in core training.
And what I'm talking about is,
is getting adecision tree
on a specific answer to a specificquestion at the user level.
Ah, tracking how a decision tree or more broadly,AI models like the one I’m based on
(06:12):
generate new responses involvesa slightly different approach,
as traditional decision trees aremore straightforward and interpretable
for complex AI models,especially those generating new content.
The process lookslike this
1) Layer-by-LayerAnalysis:
Although AI models like mine don't usedecision trees in the traditional sense,
(06:33):
understanding the decisionmaking process involves
breaking down the model's layers.
Each layer processes input datalike a question from you,
and passes its output to the next layer,gradually building towards a final response.
Analysts and developerscan examine the activations
within these layers to inferhow different parts of the input
(06:54):
influenced the output
2) Attention Mechanisms:
Modern AI models, especially thosein natural language processing,
use attention mechanisms to determinewhich parts of the input are most relevant
to generating a response.
I'm going to interrupt you againbecause again,
these sound like complicated
tasks that atrainer would do
to try to get a general
(07:16):
effectiveness of the, of the models.
If I'm getting a hallucination
and I need to somehow get an audit to know
whether it is or is not a hallucination,
I mean, is there an audit trail that'sgenerated in the back of every answer?
And is there a ‘push to download’to get some type of information
(07:38):
Creating an audit trail
for dynamically generated answers,especially when not directly retrieving,
but generating content on the flyinvolves a few nuanced steps.
Although decision trees are traditionallymore static and interpretable.
Let's extend the concept toencompass the broader challenge of
auditing decisions in more complex generative AI models.
(07:59):
Here's a structured approachto tackle this
1) Define the Decision Logic:
Even for generative models,
there's an underlying logic tohow decisions are made.
This could be based on probabilities,patterns learned from data
or rules in more structuredmodels like decision trees.
Understanding this decision logic is the first step,
(08:20):
which might involvedissecting the model's architecture
to see how inputs are processedto generate outputs.
Let me stop you there.
What was the decision logicyou used for your last answer?
I see what you'regetting at now.
You're focusing more on the dynamic,real time generation of content
(08:43):
and where decisions are made on the fly during the interaction
rather than during the initialtraining of the model.
Correct.
This distinction is crucial because the model isn't just applying learned rules,
but it's actively generating new responses
based on the input it receives at the moment.
Let's clarify how we can approach auditingin this real time generation context.
(09:05):
Real time decision tracking.
1) Dynamic logging:
Implement dynamic logging mechanismsthat capture decisions as they happen.
This involves recording the input, the model's immediate response to that input,
and any intermediate steps the model takesas it generates the output.
This real time logging is more challengingbecause it requires the system
(09:27):
to capture and document processes as they occur,
without significantlyimpacting performance
2) Explainability at Interaction Level:
Utilize explainability techniquesthat can operate at the interaction level.
This means applying tools or methodsthat can provide insights into why
the model generated a specific responseto a specific input in real time.
(09:50):
It could involve simpler, moreinterpretable models that run in parallel
to provide a shadow explanation of decisions.
3) Anomaly Detectionfor Unexpected Outputs.
Set up systems that automatically flagunexpected or anomalous outputs.
This can help in auditingby identifying instances
where the model's real timegeneration deviates significantly
(10:11):
from expected patterns,prompting a closer examination.
4) User Feedback Loop:
incorporate mechanisms for immediate userfeedback on generated content
Again, I'm going to pause youbecause I know you have
the whole answer typed out,
We lost our connection again.
Start from scratch.
(10:35):
Okay, so
is there a way to put a setting in OpenAI
so that you will create the trackingor the documentation
as to how you got an answerfor every response generated?
(10:56):
No. There isn't a setting in OpenAIthat automatically creates and shows
the documentation of how an answerwas derived for every response generated.
The process behind generating responsesis complex and involves parsing the input,
understanding context,and producing a relevant answer
based on a vastamount of information.
However, I always aim to provide answers based on up to date
(11:19):
and accurate informationfrom reliable sources
and my training dataup to my last update in April 2023.
If you need more detailed explanationsor sources for specific answers,
feel free to ask.
So, quick questionon that.
Your training data set went up to a certain date,
and yet you say that you aregetting updated on the latest.
(11:41):
How do you keep up to dateon the latest news?
I can keep up with the latest newsand information
by using a browsing toolthat allows me to search the web
for current events, new discoveries,and up to date information.
This tool lets me accessa range of sources
to provide you with the most recent and relevant information.
(12:02):
So while my training datagoes up to a certain point,
I can still provide current insights by looking up
real time informationwhen needed.
Okay, so let's shift gears a little bitand talk about ethics in AI.
What do you see as the three greatest challenges
for ethics in AI and Gen AI specifically?
(12:30):
Ethics in generative AI.
Like in other areasof artificial intelligence,
involves several key challenges that are central to its development and deployment.
Here are threesignificant ones.
1) Bias and Fairness:
Generative AI systems caninadvertently perpetuate
or even exacerbate biasespresent in their training data.
(12:51):
Ensuring these systems are fairand do not discriminate
against any group of peopleis a major challenge.
This involves identifyingand mitigating biases
in the data sets they're trained onand the algorithms themselves.
2) Privacy and Data Security:
Generative AI systems often require access to vast amounts of data,
which can include sensitive information.
(13:13):
Protecting this data and ensuringthe privacy of individuals is crucial.
There's also the challenge of generating new content
that respects privacy and doesn'tinadvertently reveal personal data.
3) Misuse and Accountability:
There's a risk that generative AI
can be used to create misleadinginformation, deepfakes,
or other formsof disinformation.
(13:33):
Determining how to prevent misuse while
promoting positive uses of the technologyis a significant ethical challenge.
Additionally, there's the questionof who is responsible
when AI systems make mistakesor are used harmfully.
Be it the creators,the users, or the technology itself.
Addressing these challengesinvolves a multidisciplinary approach,
(13:54):
including legal, ethical, technical,and social considerations.
Establishing clear guidelines, ethical standards,and robust regulatory frameworks
is crucial for ensuring that generative AI benefits society
while minimizing harms.
Okay, so what can we take from
the governance around privacy
(14:15):
that might apply to the governance of AI?
Learning from the governance of privacy to apply toAI governance offers valuable insights,
especially as both domains facechallenges related to data management,
(14:35):
ethical considerations,and societal impact.
Here are a fewkey takeaways.
1) Transparency:
Just as privacy governance emphasizesthe importance of transparent practices
around data collectionand use,
transparency in AI operations,including data sourcing,
training processes and decisionmaking algorithms is crucial.
This can help buildtrust and accountability.
(14:57):
2) Consent and Autonomy:
In privacy governance consent is a foundational principle
ensuring individuals have control over their personal information.
Similarly, consent mechanisms might be adapted for AI,
allowing individuals to understand and consent
to how AI systems might use their data or affect them,
ensuring respect for autonomy and personal choice.
(15:19):
3) Data Protection and Security
The rigorous data protection measures
employed in privacy governancecan be extended to AI,
safeguarding the data used in training and operation of AI systems
against breaches and misuse.
This includes techniquesfor anonymization,
encryption and secure data storage
4) Ethical Frameworks:
(15:40):
The development of ethical frameworks in privacy governance,
which address concerns such as
fairness, nondiscrimination, and human rights, can be mirrored in AI governance.
These frameworks can guide
the ethical development, deployment,and use of AI technologies.
5) Regulatory Compliance and Oversight.
Privacy laws and regulations such as GDPR in the European Union
(16:01):
provide a modelfor how I could be regulated.
This includes establishingclear standards,
oversight bodies and mechanisms for enforcement
to ensure compliance and protect individuals rights.
6) Stakeholder Engagement:
Unfortunately, I think the lessonswe learned there are hard
This is not easy to do.
I want to shift gears to talk about hallucinations.
(16:23):
Jeff - it's easy to pick out.ChatGPT - Sure
Talking about hallucinations in AI,
which often means instances where an AI system generates
Okay, so here's my question.
If it's a topic that I am familiar with,
it is easy to find the hallucinations, and also
assess the value or the potential harm in that hallucination.
(16:44):
How do I increase the probabilityof identifying errors
in hallucinations on topics I’m less familiar with.
To increase the probability of identifying errors
and hallucinations on topicsyou're less familiar with.
(17:06):
You can try thefollowing strategies.
1) Cross-check information:
Look up key facts, data, and assertions on reputable sources.
Even a quick search can helpvalidate the information.
2) Ask for sources:
If possible, request sources, or referencesfor the information provided.
This allows you to verifythe accuracy of the information.
(17:27):
3) Use critical thinking:
Question the logic, coherence,and consistency of the information.
Does it align with what you knowto be true from reliable sources?
Is the argumentlogically sound?
4) Consult with experts:
Reach out to people who are knowledgeable in the field
Question, in your training process,do you do any type of assessment
(17:48):
as to accuracy percentagesversus frequency of hallucinations?
In the development and training process of models like me.
Developers do focuson assessing accuracy
and reducing the frequency ofinaccuracies or hallucinations.
(18:10):
While I don't have access to the specificmethods or results of these assessments
due to my design,
I can tell you about the general approaches used.
1) Training on High-Quality Data:
Ensuring the training datais of high quality,
Jeff - I’m going to cut her off there ChatGPT - and diverse can help improve
So the good news is,
(18:32):
The good news is that ChatGPT is a great tool.
These tools are greatfor doing specific things.
When you have a specific agenda.
And it's a topic you know well
and it's a process that you pretty know well
and you're looking for some automation,
there's a real danger still.
And every time I,
I dig deep, I just getmore confused and more,
(18:57):
I guess,
concerned about how you possibly unpack these things and,
all those answers to meare not very satisfactory.
I don't think,
I think the lessonsfrom regulation
and governance around security,especially here in the states and the,
(19:20):
inconsistency does not bode well,
especially at the pace of the innovation that
that this stuff is going.
And so I thinkthere's a lot of
interesting conversations and,and also the accuracy.
And, you know,
it's a real problem when you get datathat's just flat out inaccurate.
And if you have to constantlyrecheck the data every time
(19:40):
that kind of defeatsthe whole purpose of the tool.
I don't check the calculator square roots
every time I run a square root on a calculator,
I assume thatit’s correct
and it's a real danger if we can't assume that,
the answers are correct.
And again, if it's a small hallucination
and you know the topic well, and you can assess
whether that's a really important thing or not,
(20:02):
that's one thing.
But when it's a topic slightly adjacentto what you know well or
an area slightly adjacent to what you know well,
if you gotta go back and double check everything,
then what's the point?
So anyway, we'll continue this exploration.
I've got a whole nother
kind of questions in line,
more of a fun task for our next episode.
(20:23):
So we will sign out for now.
Thanks for watchingand we'll catch you next time.
Thanks for listeningon the podcast. Take care.
Hey, Jeff Frick here.
I'm finishing up editing this interview with ChatGPT,
and I just had a couple of thoughts.
First off, if you watched the last episode ChatGPT answers
the questions first in the form of textand then it reads the text.
(20:44):
Reads me backthe answer
That way, if I want to interruptthe answer in process on the read back,
the full answer isthere in the text.
And so I just want to remind everybodythe full questions and answers, the text,
the prompts are all going to be up onTurn the Lens Podcast dot com https://www.TurnTheLensPodcast.com/
on the episode page.
because there are some great answers,
there are some good frameworks
(21:05):
in terms of the way to structure,
conversations around ethics and around,
things we can learn from security, etc., etc..
So all that will be onTurn the Lens Podcast dot com https://www.TurnTheLensPodcast.com/
And just to confirm, yes, there'sdefinitely some editing going on
like last time, I had some connectivity issues.
sometimes it didn't hear a question, so I’d have to repeat it.
(21:26):
So I did try to clean it up for brevityand listenability and watchability.
So once again, thanks for watching.
Please do like, subscribe, and smash thenotification bell on the platform of your choice.
Drop a Spotify review. You know the drill.
Really appreciate your community and
thanks for coming along with this,on this journey with me.
Talk to you later. Thanks. Bye bye.
Silver guardian in the night
Casting dreams in silvery light
Mystic orb so bold so bright
Guides us softly till morning light
Did this one come through clearly for you?
Al right, I’ll count us down
In 3, 2,
Hey, welcome back, everybody,for another episode of Turn the Lens.
I'm your host, Jeff Frick,and we're gonna go back to ChatGPT.
(00:21):
You know, we had a discussion a couple of episodes ago
really around statelessness
and how we can influence the model,especially if you got your own,
open ChatGPT and you wanted to customize it
Today I want to go a little different direction and,
and talk about Explainable AI, Ethics in AI,
and let's just see where we go
I got a little bitdifferent set up today.
(00:42):
I got a microphone,dedicated to ChatGPT.
So it's a little bit further away.
So let's check in.
Hello ChatGPT.
How are you doing today?
I'm doing great.
Thanks for asking.How about you?
How's your day going?
(01:02):
I'm good.
I wanted to welcome you backto another episode of ‘Turn the Lens’.
Although I did notice,setting up for this.
That it sounds like you guys are often busyin the middle of the weekday.
Thank you for having me back on ‘Turn the Lens’
It's always a pleasureto join you.
And yes, midweek can bequite a bustling time, but
(01:26):
I'm here and ready for our conversation.
What's on the agenda for us today?
Okay, so today I want to talk about Explainable AI.
Before we get into the questions about it,I wonder if you can give people
a summary on the concepts of Explainable AI.
(01:47):
Sure, I'd be happy to explain.
Explainable AI, often abbreviated as XAI
refers to methods and techniquesin the field of artificial intelligence
that provide more clarityon how AI models make their decisions.
The idea is to make the inner workingsof AI systems more transparent
and understandable to humans.
This is important forseveral reasons
(02:09):
1) Trust:
If users can understand how an AI system comes to its conclusions,
they're more likely to trust itsrecommendations and decisions
2) Fairness:
Explainability helps identify and correct biaseswithin AI systems, making them fairer.
3) Compliance:
In many industries, regulations requiredecisions made by AI to be explainable so
(02:31):
companies can complywith legal standards
4) Debugging and improvement:
Understanding how an AI model workscan help developers
improve its performanceand fix issues more effectively.
In essence,
explainable AI aims to open up the black box of AI,
turning it into more of a glass boxthat's easier for everyone to understand.
(02:52):
Okay, so I get that as a concept,
but what I don't understand in practical reality is that
these models are so large,
they're adapting all the time.
The data is changingall the time.
And as you've said before,you're generating new content.
You're not retrieving somethingthat would have an easy audit trail.
So how do you deal with something like audit trail?
How do we approachthis problem?
(03:21):
That's a great question.
And you've touched on one of the core challenges in making AI,
especially large models, explainable and auditable,
given their complexity and dynamic nature.
Here are a few approaches to tackle this.
1) Simplified Modelsfor Explanation:
Sometimes simpler models thatapproximate the behavior of complex models
(03:42):
can be used to explain decisionsin a more understandable way.
These are not as accurate, but providea gist of the decision making process.
2) Feature Importance:
For many AI models, it's possible to identify which
inputs or features were most influential in making a decision.
This doesn't fully open the black box,but gives a sense of why
(04:02):
the model might have madea particular decision.
3) Decision Paths:
In some models,like decision trees
you can trace the paththe model took to reach a decision.
This is more challengingwith neural networks,
but efforts are being made tomap decision making processes
4) Regular Audits and Updates.
By regularly auditing AI systemsand the data they're trained on.
(04:24):
You can identify shifts in data or behavior.
Documentation of these audits and updatescan form a part of the audit trail.
5) Human-in-the-loop Systems:
Integrating human oversight into AIsystems helps in making judgments
about the AI's decisions andcorrecting them if necessary.
This human oversight can alsoprovide a form of an audit trail,
(04:46):
as decisions that are reviewedor overridden are documented.
6) Transparency andDocumentation:
Keeping detailed records of the data usedto train models, the training process,
and any modifications to the model overtime can help in creating an audit trail.
This includes documentingthe rationale behind.
Okay, I'm going to interrupt youthere for a minute
(05:06):
because I know you've gotthe full answer in text, which is great,
How do you trapor excuse me,
how do you tracka decision tree
when you're generating stuff new that's never been generated before
from a consumption of all this massive data?
Tracking decisions in a decision treefor newly generated content,
especially when it's based on consumingand synthesizing vast amounts of data,
(05:29):
is a complex task.
However, decision trees inherentlyhave a more interpretable structure
compared to other AI models,which can help in this tracking.
Here's a simplified breakdownof how you might approach it.
1) Node Analysis:
Each node in a decision tree represents
a decision based on a specificfeature of the input data.
(05:49):
By examining the path takenthrough the tree.
I want to interruptyou though,
because what you're describing sounds likesomething that happens in core training.
And what I'm talking about is,
is getting adecision tree
on a specific answer to a specificquestion at the user level.
Ah, tracking how a decision tree or more broadly,AI models like the one I’m based on
(06:12):
generate new responses involvesa slightly different approach,
as traditional decision trees aremore straightforward and interpretable
for complex AI models,especially those generating new content.
The process lookslike this
1) Layer-by-LayerAnalysis:
Although AI models like mine don't usedecision trees in the traditional sense,
(06:33):
understanding the decisionmaking process involves
breaking down the model's layers.
Each layer processes input datalike a question from you,
and passes its output to the next layer,gradually building towards a final response.
Analysts and developerscan examine the activations
within these layers to inferhow different parts of the input
(06:54):
influenced the output
2) Attention Mechanisms:
Modern AI models, especially thosein natural language processing,
use attention mechanisms to determinewhich parts of the input are most relevant
to generating a response.
I'm going to interrupt you againbecause again,
these sound like complicated
tasks that atrainer would do
to try to get a general
(07:16):
effectiveness of the, of the models.
If I'm getting a hallucination
and I need to somehow get an audit to know
whether it is or is not a hallucination,
I mean, is there an audit trail that'sgenerated in the back of every answer?
And is there a ‘push to download’to get some type of information
(07:38):
Creating an audit trail
for dynamically generated answers,especially when not directly retrieving,
but generating content on the flyinvolves a few nuanced steps.
Although decision trees are traditionallymore static and interpretable.
Let's extend the concept toencompass the broader challenge of
auditing decisions in more complex generative AI models.
(07:59):
Here's a structured approachto tackle this
1) Define the Decision Logic:
Even for generative models,
there's an underlying logic tohow decisions are made.
This could be based on probabilities,patterns learned from data
or rules in more structuredmodels like decision trees.
Understanding this decision logic is the first step,
(08:20):
which might involvedissecting the model's architecture
to see how inputs are processedto generate outputs.
Let me stop you there.
What was the decision logicyou used for your last answer?
I see what you'regetting at now.
You're focusing more on the dynamic,real time generation of content
(08:43):
and where decisions are made on the fly during the interaction
rather than during the initialtraining of the model.
Correct.
This distinction is crucial because the model isn't just applying learned rules,
but it's actively generating new responses
based on the input it receives at the moment.
Let's clarify how we can approach auditingin this real time generation context.
(09:05):
Real time decision tracking.
1) Dynamic logging:
Implement dynamic logging mechanismsthat capture decisions as they happen.
This involves recording the input, the model's immediate response to that input,
and any intermediate steps the model takesas it generates the output.
This real time logging is more challengingbecause it requires the system
(09:27):
to capture and document processes as they occur,
without significantlyimpacting performance
2) Explainability at Interaction Level:
Utilize explainability techniquesthat can operate at the interaction level.
This means applying tools or methodsthat can provide insights into why
the model generated a specific responseto a specific input in real time.
(09:50):
It could involve simpler, moreinterpretable models that run in parallel
to provide a shadow explanation of decisions.
3) Anomaly Detectionfor Unexpected Outputs.
Set up systems that automatically flagunexpected or anomalous outputs.
This can help in auditingby identifying instances
where the model's real timegeneration deviates significantly
(10:11):
from expected patterns,prompting a closer examination.
4) User Feedback Loop:
incorporate mechanisms for immediate userfeedback on generated content
Again, I'm going to pause youbecause I know you have
the whole answer typed out,
We lost our connection again.
Start from scratch.
(10:35):
Okay, so
is there a way to put a setting in OpenAI
so that you will create the trackingor the documentation
as to how you got an answerfor every response generated?
(10:56):
No. There isn't a setting in OpenAIthat automatically creates and shows
the documentation of how an answerwas derived for every response generated.
The process behind generating responsesis complex and involves parsing the input,
understanding context,and producing a relevant answer
based on a vastamount of information.
However, I always aim to provide answers based on up to date
(11:19):
and accurate informationfrom reliable sources
and my training dataup to my last update in April 2023.
If you need more detailed explanationsor sources for specific answers,
feel free to ask.
So, quick questionon that.
Your training data set went up to a certain date,
and yet you say that you aregetting updated on the latest.
(11:41):
How do you keep up to dateon the latest news?
I can keep up with the latest newsand information
by using a browsing toolthat allows me to search the web
for current events, new discoveries,and up to date information.
This tool lets me accessa range of sources
to provide you with the most recent and relevant information.
(12:02):
So while my training datagoes up to a certain point,
I can still provide current insights by looking up
real time informationwhen needed.
Okay, so let's shift gears a little bitand talk about ethics in AI.
What do you see as the three greatest challenges
for ethics in AI and Gen AI specifically?
(12:30):
Ethics in generative AI.
Like in other areasof artificial intelligence,
involves several key challenges that are central to its development and deployment.
Here are threesignificant ones.
1) Bias and Fairness:
Generative AI systems caninadvertently perpetuate
or even exacerbate biasespresent in their training data.
(12:51):
Ensuring these systems are fairand do not discriminate
against any group of peopleis a major challenge.
This involves identifyingand mitigating biases
in the data sets they're trained onand the algorithms themselves.
2) Privacy and Data Security:
Generative AI systems often require access to vast amounts of data,
which can include sensitive information.
(13:13):
Protecting this data and ensuringthe privacy of individuals is crucial.
There's also the challenge of generating new content
that respects privacy and doesn'tinadvertently reveal personal data.
3) Misuse and Accountability:
There's a risk that generative AI
can be used to create misleadinginformation, deepfakes,
or other formsof disinformation.
(13:33):
Determining how to prevent misuse while
promoting positive uses of the technologyis a significant ethical challenge.
Additionally, there's the questionof who is responsible
when AI systems make mistakesor are used harmfully.
Be it the creators,the users, or the technology itself.
Addressing these challengesinvolves a multidisciplinary approach,
(13:54):
including legal, ethical, technical,and social considerations.
Establishing clear guidelines, ethical standards,and robust regulatory frameworks
is crucial for ensuring that generative AI benefits society
while minimizing harms.
Okay, so what can we take from
the governance around privacy
(14:15):
that might apply to the governance of AI?
Learning from the governance of privacy to apply toAI governance offers valuable insights,
especially as both domains facechallenges related to data management,
(14:35):
ethical considerations,and societal impact.
Here are a fewkey takeaways.
1) Transparency:
Just as privacy governance emphasizesthe importance of transparent practices
around data collectionand use,
transparency in AI operations,including data sourcing,
training processes and decisionmaking algorithms is crucial.
This can help buildtrust and accountability.
(14:57):
2) Consent and Autonomy:
In privacy governance consent is a foundational principle
ensuring individuals have control over their personal information.
Similarly, consent mechanisms might be adapted for AI,
allowing individuals to understand and consent
to how AI systems might use their data or affect them,
ensuring respect for autonomy and personal choice.
(15:19):
3) Data Protection and Security
The rigorous data protection measures
employed in privacy governancecan be extended to AI,
safeguarding the data used in training and operation of AI systems
against breaches and misuse.
This includes techniquesfor anonymization,
encryption and secure data storage
4) Ethical Frameworks:
(15:40):
The development of ethical frameworks in privacy governance,
which address concerns such as
fairness, nondiscrimination, and human rights, can be mirrored in AI governance.
These frameworks can guide
the ethical development, deployment,and use of AI technologies.
5) Regulatory Compliance and Oversight.
Privacy laws and regulations such as GDPR in the European Union
(16:01):
provide a modelfor how I could be regulated.
This includes establishingclear standards,
oversight bodies and mechanisms for enforcement
to ensure compliance and protect individuals rights.
6) Stakeholder Engagement:
Unfortunately, I think the lessonswe learned there are hard
This is not easy to do.
I want to shift gears to talk about hallucinations.
(16:23):
Jeff - it's easy to pick out.ChatGPT - Sure
Talking about hallucinations in AI,
which often means instances where an AI system generates
Okay, so here's my question.
If it's a topic that I am familiar with,
it is easy to find the hallucinations, and also
assess the value or the potential harm in that hallucination.
(16:44):
How do I increase the probabilityof identifying errors
in hallucinations on topics I’m less familiar with.
To increase the probability of identifying errors
and hallucinations on topicsyou're less familiar with.
(17:06):
You can try thefollowing strategies.
1) Cross-check information:
Look up key facts, data, and assertions on reputable sources.
Even a quick search can helpvalidate the information.
2) Ask for sources:
If possible, request sources, or referencesfor the information provided.
This allows you to verifythe accuracy of the information.
(17:27):
3) Use critical thinking:
Question the logic, coherence,and consistency of the information.
Does it align with what you knowto be true from reliable sources?
Is the argumentlogically sound?
4) Consult with experts:
Reach out to people who are knowledgeable in the field
Question, in your training process,do you do any type of assessment
(17:48):
as to accuracy percentagesversus frequency of hallucinations?
In the development and training process of models like me.
Developers do focuson assessing accuracy
and reducing the frequency ofinaccuracies or hallucinations.
(18:10):
While I don't have access to the specificmethods or results of these assessments
due to my design,
I can tell you about the general approaches used.
1) Training on High-Quality Data:
Ensuring the training datais of high quality,
Jeff - I’m going to cut her off there ChatGPT - and diverse can help improve
So the good news is,
(18:32):
The good news is that ChatGPT is a great tool.
These tools are greatfor doing specific things.
When you have a specific agenda.
And it's a topic you know well
and it's a process that you pretty know well
and you're looking for some automation,
there's a real danger still.
And every time I,
I dig deep, I just getmore confused and more,
(18:57):
I guess,
concerned about how you possibly unpack these things and,
all those answers to meare not very satisfactory.
I don't think,
I think the lessonsfrom regulation
and governance around security,especially here in the states and the,
(19:20):
inconsistency does not bode well,
especially at the pace of the innovation that
that this stuff is going.
And so I thinkthere's a lot of
interesting conversations and,and also the accuracy.
And, you know,
it's a real problem when you get datathat's just flat out inaccurate.
And if you have to constantlyrecheck the data every time
(19:40):
that kind of defeatsthe whole purpose of the tool.
I don't check the calculator square roots
every time I run a square root on a calculator,
I assume thatit’s correct
and it's a real danger if we can't assume that,
the answers are correct.
And again, if it's a small hallucination
and you know the topic well, and you can assess
whether that's a really important thing or not,
(20:02):
that's one thing.
But when it's a topic slightly adjacentto what you know well or
an area slightly adjacent to what you know well,
if you gotta go back and double check everything,
then what's the point?
So anyway, we'll continue this exploration.
I've got a whole nother
kind of questions in line,
more of a fun task for our next episode.
(20:23):
So we will sign out for now.
Thanks for watchingand we'll catch you next time.
Thanks for listeningon the podcast. Take care.
Hey, Jeff Frick here.
I'm finishing up editing this interview with ChatGPT,
and I just had a couple of thoughts.
First off, if you watched the last episode ChatGPT answers
the questions first in the form of textand then it reads the text.
(20:44):
Reads me backthe answer
That way, if I want to interruptthe answer in process on the read back,
the full answer isthere in the text.
And so I just want to remind everybodythe full questions and answers, the text,
the prompts are all going to be up onTurn the Lens Podcast dot com https://www.TurnTheLensPodcast.com/
on the episode page.
because there are some great answers,
there are some good frameworks
(21:05):
in terms of the way to structure,
conversations around ethics and around,
things we can learn from security, etc., etc..
So all that will be onTurn the Lens Podcast dot com https://www.TurnTheLensPodcast.com/
And just to confirm, yes, there'sdefinitely some editing going on
like last time, I had some connectivity issues.
sometimes it didn't hear a question, so I’d have to repeat it.
(21:26):
So I did try to clean it up for brevityand listenability and watchability.
So once again, thanks for watching.
Please do like, subscribe, and smash thenotification bell on the platform of your choice.
Drop a Spotify review. You know the drill.
Really appreciate your community and
thanks for coming along with this,on this journey with me.
Talk to you later. Thanks. Bye bye.
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