Bias in Generative AI: Solutions
Key Points
- Generative AI is reshaping industries by enabling complex tasks, boosting productivity, and shortening time‑to‑value for products and services, leading to cost savings and enhanced customer engagement.
- Despite its benefits, generative AI introduces several risks, including downstream model retraining issues, copyright infringement, leakage of proprietary or personal data, and a lack of transparency in model explanations.
- The most pronounced risk today is bias, where AI systems can perpetuate and amplify existing societal inequalities through algorithmic, cognitive, and confirmation biases.
- Types of bias include algorithmic bias (systematic unfair outcomes, e.g., age‑based loan rejections), cognitive bias from human designers (e.g., recency bias from recent events), and confirmation bias that reinforces existing assumptions.
- Mitigating bias requires understanding its origins, applying principled design practices, and employing specific methods to develop and maintain bias‑free AI systems.
Sections
- Generative AI: Benefits, Risks, and Bias - The speaker outlines generative AI’s broad economic and productivity advantages, highlights associated dangers—especially amplified bias—and previews a discussion on bias types, principles, and mitigation strategies.
- Human Biases Embedded in AI Systems - The speaker explains how designers unintentionally infuse age, cognitive (recency, confirmation), and outgroup homogeneity biases into AI models, illustrating the systematic impact of human prejudice on algorithmic outcomes.
- Enterprise AI Bias Governance - The speaker warns that sampling only top performers creates biased AI outcomes and emphasizes that comprehensive AI governance—through policies, frameworks, and fairness tools—is essential for enterprises to detect and eliminate bias across all users.
- Mitigating AI Bias: Tools, Teams, Data - The speaker outlines three practices—leveraging fairness‑evaluation tools, building diverse AI teams, and carefully processing data—to ensure bias‑free AI applications.
- Evolving AI with Real‑World Data - The speaker emphasizes continuously updating AI systems using current trends and third‑party bias assessments to ensure they remain fair, relevant, and aligned with today’s realities.
Full Transcript
# Bias in Generative AI: Solutions **Source:** [https://www.youtube.com/watch?v=ZsjDvyuxxgg](https://www.youtube.com/watch?v=ZsjDvyuxxgg) **Duration:** 00:13:33 ## Summary - Generative AI is reshaping industries by enabling complex tasks, boosting productivity, and shortening time‑to‑value for products and services, leading to cost savings and enhanced customer engagement. - Despite its benefits, generative AI introduces several risks, including downstream model retraining issues, copyright infringement, leakage of proprietary or personal data, and a lack of transparency in model explanations. - The most pronounced risk today is bias, where AI systems can perpetuate and amplify existing societal inequalities through algorithmic, cognitive, and confirmation biases. - Types of bias include algorithmic bias (systematic unfair outcomes, e.g., age‑based loan rejections), cognitive bias from human designers (e.g., recency bias from recent events), and confirmation bias that reinforces existing assumptions. - Mitigating bias requires understanding its origins, applying principled design practices, and employing specific methods to develop and maintain bias‑free AI systems. ## Sections - [00:00:00](https://www.youtube.com/watch?v=ZsjDvyuxxgg&t=0s) **Generative AI: Benefits, Risks, and Bias** - The speaker outlines generative AI’s broad economic and productivity advantages, highlights associated dangers—especially amplified bias—and previews a discussion on bias types, principles, and mitigation strategies. - [00:03:02](https://www.youtube.com/watch?v=ZsjDvyuxxgg&t=182s) **Human Biases Embedded in AI Systems** - The speaker explains how designers unintentionally infuse age, cognitive (recency, confirmation), and outgroup homogeneity biases into AI models, illustrating the systematic impact of human prejudice on algorithmic outcomes. - [00:06:10](https://www.youtube.com/watch?v=ZsjDvyuxxgg&t=370s) **Enterprise AI Bias Governance** - The speaker warns that sampling only top performers creates biased AI outcomes and emphasizes that comprehensive AI governance—through policies, frameworks, and fairness tools—is essential for enterprises to detect and eliminate bias across all users. - [00:09:22](https://www.youtube.com/watch?v=ZsjDvyuxxgg&t=562s) **Mitigating AI Bias: Tools, Teams, Data** - The speaker outlines three practices—leveraging fairness‑evaluation tools, building diverse AI teams, and carefully processing data—to ensure bias‑free AI applications. - [00:12:29](https://www.youtube.com/watch?v=ZsjDvyuxxgg&t=749s) **Evolving AI with Real‑World Data** - The speaker emphasizes continuously updating AI systems using current trends and third‑party bias assessments to ensure they remain fair, relevant, and aligned with today’s realities. ## Full Transcript
Generative AI has had a wide ranging impact on the world today, and it's for all of us to see.
Starting from the economic impact, the impact that it's had on industry transformation,
legal documents, summarization, customer engagement, cost savings,
and so many more.
The reason why we have had this impact is because of the benefits that generative AI has provided.
I like to talk about three.
Our ability to perform complex task using generative AI,
the increase in productivity that we can see, and for enterprises, is the shorter time to value to get products and services out.
But as with all new technology that has risks generative ai, we also has its associated risks.
Some of the emerging risks come from downstream based model retraining.
It could be copyright infringement.
There's also traditional risks that we've seen with AI.
These come from proprietary or private or personal information coming out as outputs of the LLMs.
It could also be the lack of transparency that the model offers
in explaining the results that it gives out.
But the most amplified risk
that we see today is the one that comes from bias.
And that's the topic of discussion in this video.
We're going to be looking at the types of bias,
the principles of how to avoid them, and actually the methods that we can use to avoid and create bias free AI systems.
So let's look at the different types of biases,
but before that, let's define what a bias is. AI bias or machine learning Bias, also known as algorithmic bias, refers to AI
systems that create and produce biased results.
What do I mean by biased results?
Biased results reflect and perpetuate
human biases within a society, normally including historical and current social inequalities. They're pretty harmful.
We've seen a lot in the news where companies and enterprises have been questioned on the kind of biases that they have in the data that they've trained their models on.
Let's look at the different types of biases that we have, starting with algorithm bias.
This is a systematic and erroneous behavior of any AI system to always produce an unfair outcome.
How do I explain this?
Let's look at an example where any AI developer developed a loan application system,
and in that system it automatically prevents applicants born before 1945.
You've created age bias, and it's just automatic and systematic over time.
There's also cognitive bias.
Now we must remember humans designing AI systems.
There's always a human intellect and input when you actually create these systems.
Let's take, for example, the tendency of a human brain to think in a certain way.
An example would be recency bias, which is a type of cognitive bias.
You're kind of influenced by recent events.
What would be a good example?
Spread of Covid in 2020,
or it could be an ongoing war.
It skews your thinking and it builds that bias into your into the systems that you're building.
Confirmation bias, mixed type of bias depends on, and it's a related bias to cognitive bias.
It relies on preexisting beliefs.
Beliefs such as left handed people are more creative, right handed people are less.
That can easily creep in to the way you think about the data that you're using.
The next one is outgroup, homogeneity, bias.
Now, this is a little tricky to explain.
Let me show you.
Let's assume that you've created a data set of training data that you believe is a diverse training data set.
You probably have made a larger assumption than you think.
That the group outside of the diverse group
are all similar.
That's outgroup homogeneity bias.
It's based on an assumption that people outside your your diverse group are all similar.
Prejudice the next type.
This is faulty societal assumptions.
The most popular example would be all nurses are female.
All doctors are male.
And so on and so forth.
It's pretty easy for this bias to come in into the systems that we're developing.
The last line is the exclusion bias.
This is where you leave out inadvertently data that was important for the sampling.
What do I mean by that?
Let's say you send out a survey to a set of individuals that incidentally were the smartest employees in your enterprise.
You've left out an entire group that could represent the less or average set of employees when it comes to performance, skewing your results.
Typically, at the start of any innovation within an enterprise, it's very easy to get enamored by the wonderful, cool things that gen AI, I can do for you.
You have a successful prototype, a PoC, a pilot for a small set of users, and you're quick to announce success,
until I starts getting tested by internal and external users and you start seeing biased results.
That's when you know that you need to step back and relook at your initiative with a little more effort.
Identifying and addressing bias requires e-governance.
Now, what AI governance offers is a method to direct,
manage,
and monitor
all AI activities within your enterprise.
It's a set of policies, practices and frameworks that enable you to do responsible
development of AI
It typically engages tools and technologies that detect fairness, equity and inclusion.
It is the best tool for enterprises to use to ensure that the benefit,
the benefits of the governance goes directly to the customers, to the consumers, employees, as well as the enterprise.
Avoiding bias may sound harder than it is, but there are proven methods that one can use to ensure your enterprise is bias free in all your applications.
Let's start.
Let's talk about a few, selection of learning models.
Now it's obvious that you're going to choose a learning model that aligns with the business function that you want to achieve, and it scales in the appropriate way,
but when you're making decisions between supervised and unsupervised learning models, we need to be a little bit more careful.
For supervised learning models, the stakeholders select the training data.
It's then important for us to ensure that the set of stakeholders are a diverse set.
Most often I see enterprises use data scientists to select the data.
It's important to get into this from
all the different business functions within an enterprise to ensure that the right input has been provided to select training data.
For unsupervised learning models with AI alone identify as bias, you need to leverage tools.
These tools basically use fairness indicators.
And there are several tools out there from Google,
we've got Google Toolkit, AI Fairness 360 from IBM, Open Scale and what have you.
It's important that you invest the effort in understanding the capabilities of these tools,
how they can be used by your applications to ensure you have a bias free application.
The second one is creating a balanced AI Team.
And what do I mean by balanced?
Essentially, what I'm meaning here is it should be a varied set of team members.
There should be different racially,
from economic status,
education levels,
gender.
We should also include innovators, the ones that are sponsoring the initiatives,
the creators of the AI ,and the Consumers of the AI.
Having a varied team allows you to ensure that you have bias free decisions
from ground up, the selection of data, the selection of algorithms is done across the entire team.
The third method that I want to talk about is data processing.
The general notion that we usually have is, well, bias is all in the data.
Once you have selected the proper data, which is bias free, you're good.
But that's not the case.
Data needs to be processed.
This pre-processing.
That you do with your data.
There's inline processing.
Or in-processing.
And there's also post-processing of data.
You have to be mindful to ensure that while you have selected bias, free data, bias is not creeping in in any of these stages of data processing.
The last one that I want to talk about is monitoring.
One must understand that biases evolve over time.
Do we think of EVs the way we think them?
Think about them now.
Was it the same kind of thinking that we had 20 years back?
Probably not.
We're more favorable towards EVs today than we were before.
So it's important to continuously look at trends and real world data
to ensure your AI systems are not stagnant and are moving along, evolving along with real world data.
Now I've also seen
many companies employ third party assessment teams that will
assess all of your enterprise applications for detecting bias.
It's a great and optional way to ensure that your systems are built fairly.
They are bias free and also endorsed by third party assessment.