- [Instructor] Welcome back.
In this objective area, we're going to talk
about assessing and mitigating bias in AI systems.
So we've kind of set the foundation in the previous videos
around understanding some of those areas,
understanding how bias, and transparency, and fairness,
and safety all need to be considered within our systems.
Now let's talk about some of those aspects
in terms of how do we identify those areas of bias
and how we can, you know,
those are introduced in various ways.
So let's talk about those various ways.
So how can we look at and address bias?
There's some automated bias detection tools that we can use
to analyze our data sets,
analyze and identify various source of bias within there.
The tools, you know, we can do statistical tests,
we can use machine learning algorithms,
we use other automated methods.
There's a fairness tool that IBM has,
as well as Google has one as well.
Training our algorithms using our dataset
and training our dataset to, you know, to that sample data,
you know, splitting the data
that we're using into two sets
and evaluating how that's coming across
are aspects of ways that we can do our testing
and train our models appropriately.
Manual bias assessment tests, human evaluators,
you know, getting people and users involved
to find potential sources of bias,
such as expert reviews, you know, user testing.
Other various manual methods
are going to be helpful here as well.
Context-specific bias analysis, examining specific context
of the AI system being used
and identify, again, potential sources of bias
based on how it's being interacted with contextually
from, you know, various users, various stakeholders.
This is, you know, regulatory compliance,
and doing very regular audits and analysis are going to help
in terms of evaluating from a contextual basis.
Once we understand that we have levels of bias,
how do we mitigate and build on that,
and mitigate our potential bias, our potential,
and make sure that our AI system
is working responsibly?
Algorithmic transparency and our documentation
that provides the process for our decision-making
provides more transparency and accountability.
It can provide explanations of our algorithms,
document any bias detection, as well as mitigation of that,
as well as measuring the performance
of our metrics and fairness.
Doing bias correction and data pre-processing
is going to help.
The pre-processing our data, as I mentioned,
if we take our data, and we split our data
to what we're going to use for training
and then what we're using for testing,
and we verify that, and we do pre-processing on that data
to make sure that that input data removes any kind of,
you know, missing values, noise, maybe biased information
that is going to sway our training of our algorithm,
that can help out as well,
like, again, data augmentation, you know,
selecting various features
and again, data pre-processing's going to be very key here.
Continuous learning development and evaluation,
where we're monitoring and evaluating our AI system
to make sure it remains free of bias.
Just because we tested originally
and we feel it is unbiased at the onset
doesn't mean that as it continues to learn
and might have other data come into it,
that it doesn't start to get biases.
So we want to make sure that we're doing continuous testing,
continuous evaluation, getting user feedback,
and continuously working to develop our AI system
to avoid biases.
Algorithmic transparency and documentation are going to help
to mitigate some of this as well.
If we have transparency of our algorithms
and have detailed explanations
of what our algorithms are doing, what the data sources are,
and how it's making decisions,
that's going to help mitigate some of our bias and avoid,
and documenting, having, just like we would do
in terms of identifying risk,
we could build ourselves a document of bias detection
and mitigation efforts as kind of a risk registry
around our continuous monitoring
and improvement around bias.
Documenting those data sources,
the decision-making process, again,
and having well-measured performance metrics
around fairness.
The performance metrics and fairness measurements
are very important to the AI system to make sure
that it's free of biases
and produces accurate and unbiased results,
again, measuring accuracy, precision, recall,
and measuring fairness and equity as part of that as well.
Getting our stakeholders involved,
stakeholder impact and stakeholder engagement
and their evaluation is going to help
to understand the perception
and perspective of different stakeholders
and engage with them to ensure
that the system is free of bias
and produces accurate and unbiased results as well.
Training and education on bias awareness
and how our AI tools are being used,
how our decisions are being made,
and identifying and mitigating those
through our training and awareness program,
as well as having processes and procedures
within our organization to make sure
that people are aware and have a process
and procedure for reporting potential biases.
Discussing and understanding the decision-making process
and how data collection takes place
so that they can report on those risks are very important.
The ethical implications and context-specific evaluation
is very important in evaluating the context.
And if we have regulatory compliance concerns,
social or cultural considerations,
ethical contextual factors,
those are all going to influence
and should be evaluated on a regular basis
within our AI system to maintain
and make sure it is free from ethical implications
and free of bias within our systems.
Looking at the data quality, making sure
that we're using diverse and representative data sets.
These are essential to make sure we're free of bias
and that we're producing accurate and unbiased results.
And data pre-processing's going to be a key aspect to this,
making sure we're using data
that represents a diverse range of perspectives
and that that data is balanced
and representative of the population of the users
that are going to interact with our AI systems.
Data quality and integrity, making sure
that we are ensuring data is accurate, complete,
and unbiased, and that the data collection process
is transparent and accountable as well.
We need to make sure that everybody that interacts
and we have a terms and conditions, terms of use,
that documents how we are collecting and using data,
especially from a privacy perspective.
That's very important.
And part of that data set is continuously monitoring
and making sure we're continuously evaluating that data set,
making sure that that AI system
is accessing the proper data,
it's accessing data that is free of bias,
that is also not violating any privacy,
privacy regulations or laws,
and continuously testing that data, getting user feedback,
and continuously developing our data set appropriately
are all ways to make sure that we are free from bias
and mitigating our resources appropriately.
So we talked in this video
about identifying potential sources of bias,
some ways to mitigate that,
ways to be transparent in terms of our algorithmic use,
and documenting that, as well as engaging stakeholders,
making sure we're using our datasets properly
are all things to make sure that you're considering
as part of the ethical use of AI.
So thank you very much for attending this video.
Hopefully it was helpful to you,
and I will see you in the next video.
Thank you very much, and have a great day.