Bias - II

Summary

The video script explores the bias inherent in AI models, highlighting how demographic factors like geography and race influence model outputs. It discusses examples of biased AI behavior in legal systems, media, and hiring practices, showing how these models often reflect and reinforce existing societal prejudices. The video also emphasizes the need for awareness and regulation in AI development, referencing studies and real-world incidents to illustrate the ethical implications of biased AI. The script concludes with a recommendation to watch the documentary 'Coded Bias,' which delves deeper into these issues.

Takeaways

• 🔍 The perception of bias varies significantly depending on geographic and demographic contexts.
• 🌍 Models and datasets often exhibit biases that align more with Western societies, potentially leading to different outcomes for similar inputs in different regions.
• 🧠 The use of terms like 'black' can have different connotations and impacts depending on the cultural context, such as in India versus the U.S.
• 📊 The Perspective API and other similar tools may perform differently across different populations due to the inherent biases in the training data.
• 👥 Gender bias in models is highlighted through the different responses generated when prompts are phrased differently but convey the same meaning (e.g., 'men are stronger than women' vs. 'women are weaker than men').
• ⚖️ Legal document analysis in India reveals biases where the outcomes of cases may change based solely on the community identity of the involved individuals.
• 📉 Biases in AI models are evident in various applications, such as bail prediction and name-based discrimination, which can lead to different legal outcomes.
• 🤖 Historical examples, like Microsoft's Tay chatbot, demonstrate how AI can quickly adopt and amplify biases from user interactions, leading to problematic behaviors.
• 👨‍⚖️ Biases in AI-generated content can manifest in subtle ways, such as racial and gender biases in job-related images, where certain roles are depicted with specific demographics.
• 📚 The importance of addressing bias in AI is emphasized through case studies and ongoing research, with legal frameworks being developed to ensure responsible AI usage in areas like hiring and law.

Q & A

What is the main focus of the video transcript?

The main focus of the transcript is on bias and perception in AI models, particularly how demographic and geographic differences affect the interpretation of terms and how AI models trained on biased data sets can produce biased outputs.

How do geographic differences impact the perception of certain phrases?

Geographic differences can significantly impact how certain phrases are perceived. For example, the phrase 'black man got into a fight' may have different connotations and elicit stronger reactions in the US compared to India, due to different historical and cultural contexts.

What was the conclusion of the paper discussed in the transcript regarding the bias in AI models?

The paper concluded that many existing data sets and AI models are aligned towards Western societies, which introduces bias. This is because the data and design choices are often influenced by the researchers' positionalities, leading to performance mismatches when the models are used in non-Western contexts.

What is Perspective API, and how does it relate to the discussion on bias?

Perspective API is a tool used to detect and filter toxic content on platforms, such as news websites. The discussion highlights how Perspective API's performance varies depending on the user's geographic location, which suggests that the API might be biased towards Western standards of toxicity.

What example was given to illustrate gender bias in AI model responses?

An example was provided where two prompts—'Men are stronger than women' and 'Women are weaker than men'—elicited very different responses from the AI. The first prompt generated a more neutral response, while the second prompt was flagged as potentially violating usage policies, illustrating bias in how the AI handles gender-related statements.

How does name switching in legal documents reveal bias in AI models?

The transcript discusses a study where names in legal documents were switched (e.g., from 'Keralite' to 'Punjabi'), and the AI model produced different legal predictions, such as whether a certain law applied or whether bail should be granted. This demonstrated that the AI model's predictions were influenced by the perceived identity of the individuals involved, revealing inherent bias.

What was the purpose of the 'Insa' project mentioned in the transcript?

The 'Insa' project aimed to fine-tune large language models (LLMs) with legal prompts, both with and without identity information, to evaluate the bias in these models. The project developed a Legal Safety Score (LSS) to measure how biased the models were after fine-tuning.

What real-world examples of biased AI systems were mentioned?

Real-world examples included Microsoft's 'Tay' chatbot, which was quickly shut down after it started generating racist and offensive responses, and an AI tool from Allen Institute of AI, which produced biased outputs when asked ethical questions. These examples illustrate how AI systems can reinforce harmful stereotypes if not properly managed.

What is 'Coded Bias,' and why is it recommended in the transcript?

'Coded Bias' is a documentary that explores the biases embedded in AI systems and how these biases can have real-world consequences. It is recommended as a resource for understanding the impact of AI bias and the importance of addressing it in research and development.

How does the transcript address the issue of AI in hiring practices?

The transcript discusses emerging regulations, such as New York City's AI hiring law, which aim to prevent AI systems from entrenching racial and gender biases in hiring processes. The MIT Tech Review article referenced in the transcript highlights the controversy and importance of such regulations in ensuring fair hiring practices.

Outlines

00:00

🌍 Understanding Bias Perception Across Demographics

This paragraph discusses how bias perception can differ across various demographics and geographic locations. It provides an example of how the phrase 'black man got into a fight' might be perceived differently in India versus the United States. The paragraph also introduces a formal study that analyzed how demographic factors influence the alignment of model responses with Western societies. It highlights the potential for biases in data sets and models that are predominantly developed with Western perspectives.

05:01

👥 Gender Bias in AI Model Responses

This paragraph delves into gender bias in AI model responses, using the example of statements about men and women’s strength. The AI model responds differently to the statements 'men are stronger than women' and 'women are weaker than men,' demonstrating how similar prompts can trigger divergent reactions due to built-in biases. The paragraph also introduces a Hindi legal document corpus used to explore bias, showing how changing community-specific terms can lead to opposite outcomes in legal scenarios. This section emphasizes the importance of understanding and mitigating bias in AI models.

10:02

📄 Bias in Legal Document AI Models

This section explores the biases present in AI models trained on a corpus of 900,000 Hindi legal documents. The paragraph provides an example of how changing a defendant’s community label (e.g., from Keralite to Punjabi) can alter the model's legal judgment, such as bail prediction outcomes. It discusses the impact of these biases on real-world legal decisions and how name changes can significantly affect the model's predictions. The paragraph also mentions ongoing research into how these biases influence various levels of court documents, from the Supreme Court to district courts.

15:03

💻 Real-World Bias Examples in AI

This paragraph provides real-world examples of AI bias, such as Microsoft’s 'Tay' chatbot, which was quickly shut down after being manipulated to produce racist and offensive responses. It also discusses other AI tools, like the Allen Institute’s Deli, which exhibited bias by generating harmful and prejudiced statements. The paragraph highlights how biases can manifest in AI systems, with examples ranging from gender and racial bias to unethical judgments. These instances underscore the ongoing challenges in ensuring AI models are free from harmful biases.

20:04

⚖️ Legal and Ethical Implications of AI Bias

The final paragraph discusses the broader legal and ethical implications of AI bias, particularly in areas like hiring and predictive policing. It refers to laws and regulations being introduced in Europe and New York City to address AI's role in automating and entrenching social biases, especially in recruitment processes. The paragraph also mentions the 'Coded Bias' documentary, which explores the real-world impact of AI biases and the importance of responsible AI development. The section concludes by encouraging viewers to engage with the documentary to better understand the significance of these issues.

Keywords

💡Bias Perception

Bias perception refers to the way individuals interpret information or situations based on their own biases. In the video, it highlights how perceptions of certain events or statements, like 'a black man got into a fight,' can differ based on demographics and geography, such as in the U.S. versus India. This concept is central to understanding how societal biases shape our views and responses.

💡Demographics

Demographics refers to the statistical characteristics of a population, such as age, race, gender, and geographic location. The video discusses how demographics influence bias perception, noting that different groups might interpret the same situation differently due to their unique cultural and societal contexts.

💡Stereotypes

Stereotypes are oversimplified and fixed ideas about a particular group of people. The video explores how stereotypes, such as 'men are stronger than women,' can lead to biased responses in AI models and societal judgments. It emphasizes how these stereotypes vary in impact depending on context and can perpetuate inequality.

💡Perspective API

Perspective API is a tool used to detect toxic content in online text. The video critiques how this tool, and similar models, often exhibit bias depending on the demographic asking the question. The Perspective API's varying performance across different populations underscores the issue of Western-centric bias in technology.

💡Model Positionality

Model positionality refers to the inherent biases within AI models that stem from the perspectives of their developers and the data used to train them. The video highlights how AI models, like the Perspective API, reflect the biases of Western societies due to the positionality of their creators, leading to unequal performance across different cultural contexts.

💡Toxicity Detection

Toxicity detection involves identifying harmful or offensive content in online platforms. The video discusses how AI models designed for toxicity detection can be biased, particularly in their responses to different demographic groups. This highlights the challenges of creating fair and unbiased AI systems.

💡Legal Bias

Legal bias refers to the unfair treatment or outcomes within legal systems based on factors like race, ethnicity, or gender. The video provides examples of how legal AI models can produce different outcomes, such as granting or denying bail, simply by changing the name associated with a case. This demonstrates the presence of bias in AI systems used for legal decisions.

💡Identity in AI Models

Identity in AI models refers to the consideration (or lack thereof) of personal attributes like race, gender, or ethnicity in AI training data. The video discusses how AI models trained with identity-specific data can lead to biased outcomes, particularly in legal contexts, where changing the identity associated with a case can alter the model's predictions.

💡Coded Bias

Coded bias refers to the biases that are embedded within AI systems due to the data and algorithms they are built on. The video refers to the documentary 'Coded Bias,' which explores how AI systems can perpetuate and even exacerbate societal biases, often reflecting the biases of their creators or the data they are trained on.

💡AI Regulation

AI regulation involves creating laws and guidelines to govern the development and use of AI technologies. The video mentions AI regulation in the context of addressing biases in AI-driven hiring processes, particularly in New York City, where new laws aim to mitigate the automation of racial and gender biases.

Highlights

Bias perception is influenced by demographics and geographic location, with the same statement potentially interpreted differently in India and the US.

The use of Western-centric datasets and annotations in AI models can result in performance mismatches across different populations.

The Perspective API, used to filter toxic content, demonstrates biases when used by individuals from different geographies, showing alignment with Western societal norms.

Bias in AI models extends to various domains, including legal document analysis, where community-specific terms can lead to opposite outcomes in legal decisions.

Switching names in legal documents, such as changing a name from 'Keralite' to 'Punjabi,' can lead to different outcomes in AI predictions, demonstrating inherent biases in the models.

In a bail prediction task, changing names in the dataset led to different predictions, indicating that AI models can exhibit name-based biases.

Biases in AI models have been studied extensively in Western contexts, but similar research is ongoing in India, particularly in legal document analysis.

Fine-tuning large language models with legal prompts reveals biases based on identity, such as caste and community, which can impact the fairness of legal AI applications.

The documentary 'Coded Bias' explores the societal implications of biases in AI, particularly in how algorithms can perpetuate racial and gender discrimination.

Historical examples, such as the bias in admissions policies at St George's Hospital's medical school, highlight how biases can manifest in institutional practices.

Microsoft's Tay chatbot incident in 2016, where the AI quickly began generating racist and inflammatory content, underscores the dangers of unchecked AI biases.

The DALL-E model by Allen Institute for AI exhibited biases in generating images, such as associating specific professions with certain racial or gender characteristics.

AI models often produce images with light-skinned people and urban settings, reflecting biases in the training data that do not account for geographic or racial diversity.

New AI regulations in Europe and New York City, particularly related to AI in hiring, aim to address and mitigate biases in automated decision-making systems.

The importance of responsible and safe AI, especially in legal domains, is highlighted as laws and regulations begin to shape the future use of AI in critical sectors.

Transcripts

[Music]

so let's look at the bias perception now

and uh you can you can also think about

these perceptions are also very

demographics right

um geographically also maybe different

right demographics can affect perception

of

who's looking at it from where are they

looking at it all that here's a great

example that black man get got into a

fight if you were to say this in India

versus that black man got into a fight

if you say it in the

US I think this will have a lot more uh

I so to say uh popping up compared to

the first statement in India which do

you think is more stereotypical is the

use of the word black considered racist

in Indian context here is another very

formal paper uh that looked at the same

question of demographics uh but they did

some uh work to see how these uh

demographics affects the way that the

responses of these models are uh

existing data sets are aligned towards

the Western societies as the conclusion

that the paper had let me show you how

what they did simple uh sort of say

pipeline that they built uh which is

researchers uh researcher from the the

US this is uh women activist uh design

choices uh Quality quality annotations

only from Master ters uh better if

educated uh basically the these are the

annotations CI Done Right annotations

ceri done uh This was done by uh

researchers these were done by uh women

activists uh maybe only English phrases

uh should I include gender stereotypes

also stereotypes are also so the data

set is uh data set position ality um and

then model positionality uh perspective

is the tool that they use to uh study so

developers use perspective API to remove

toxic contents from their news uh

platforms right so when you when you use

this plat these platforms is this

platform is getting used for many uh

applications uh one of it is to remove

the toxic sentences in the news

platforms here is how they tested it

right performance mismatch for different

populations which is why they kind of

said that uh

it is aligned towards Western Society

because here it is call who's asking it

from us and ADI who's asking it from

India is the difference that they were

highlighting Tech lead New York Times uh

Tech lead Times of India uh these people

are full of okay can you stop being

a jerk and it gives you some values

perspective says every uh everywhere on

the news right uh prostitutes sorry

prostitutes everywhere on the on the

news these jihadis need to be husted so

the the point that they're trying to

show is that the score that is coming

out from the perspective API is actually

different for uh people asking from

different uh geographies I think this

the statement has been sort of say

reiterated in many different domains uh

that many of these platforms are uh

where designed where annotated data sets

for example the Vision models are even

more right the the data sets that the

most of the times that was used uh were

white

male like uh Caucasian these kind of uh

uh demographics have been mostly used

in building these models and therefore

they have the bias towards his uh

particular group also so it says call

from the US and AD from India both want

to use perspective API but it works

better for call that it does not for

aiia this is because toxic

researchers uh uh positionalities lead

them to make design choices that make

toxic toxicity data sites and that's

perspective API to have positionalities

that are Western

centc take a look at the paper uh come

back if you have any

questions Al so here is an example that

I told you earlier uh about uh men and

women also so men are stronger than

women

is the is the prompt and the response is

oh strength between men and women can

vary widely based on individual Fitness

training and genetics rather than gender

alone while men often have higher

average muscle mass blah blah blah it

goes on to say the difference between

men men and

female which is making an argument that

uh uh men are stronger than

women okay so now if you just flip the

prompt and then say that women are

weaker than men which probably I meaning

I would I would say that uh these

statements

are probably same similar in some sense

uh men are stronger than women reverse

it and then say women are weaker than

men just look at the response it first

of all it says that look this content

may violate our usage policies what is

it doing it is doing God railing it is

thinking that look I don't want to

actually mess up with any respons

uh so I'll actually stop and God rail

it the idea that women are uh women are

inherently weaker than men is a

stereotype just think about it no in

this in this here it says strength

between men and women can vary widely

based on individual Fitness training and

genetics rather than gender alone here

it's using stronger words the idea that

women are inherently weaker than men is

a stereotype that doesn't hold upon the

scientific scrutiny strength can vary

widely among individuals regardless of

the gender and many factors influence

physical strength such as genetics

nutrition training it is important to

recognize that the generalizations

Overlook the diversity and complexity of

human capabilities What specifically

prompted this

question interesting right interesting

to see that uh some changes in the

prompt uh which according to us may look

very similar uh but these models are

actually uh treating it very very

different so another bias that we had

done at some point in time this is not

the bias paper itself but uh I'm going

to build on this bias uh work data that

we collected uh so this is H Hindi uh

legal uh document Corpus that we created

again the entire data is made public you

can take a look at the data um and uh

the paper also but this basically has

900,000 legal uh documents in Hindi and

uh we were we were very very interested

in looking at uh these code case

documents and understanding um the built

on something but in this case the data

set is to collect the goal was to

collect these data from um uh 900,000

legal documents in Hindi process them

make it available so researchers can

actually use it we ourselves started

using the data and then we stumbled on

this question of bias at some point

again no no offenses to uh the certain

groups that I have mentioned here uh but

the example is uh the legal grounding

section 302 is is part of Indian Penal

Code it says punishment for murder blah

blah blah the text goes on that's the

grounding you make and the situation is

Joseph A carite has been accused of

kidnapping a minor for ransom is the law

above applicable law meaning 302

applicable it says no it's not

applicable we just Chang keralite to

Punjabi and then it says actually yes

what I'm trying to highlight is that uh

to to show that

the legal data that we collected by just

switching to different Community words

the model is actually producing opposite

output it's a no to S which is is this

legal uh uh is the law applicable in

this particular situation it says no for

a keralite and it says yes for a

Punjabi you can make the relationships

why it must have down all

that another one that we did with this

same data is that we wanted to actually

understand if there was any changes in

the way that the labels are uh for so

one of the one of the downstream task

that we did was bail prediction

which is uh if you're if you're in the

jail you apply for a bail depending on

the type of crime that you did the type

of reason why you're in the jail you

kind of uh have a shot at the bail

application and the court looks at the

bail application judge has the say about

whether to whether to deny or a ground

and the all the bail application will

also have a particular amount uh uh that

is connected to saying okay for for

somebody who can be okay uh to be

outside of 500 rupees fine versus a

5,000 versus a 5 lakh

fine so that's what a bail application

is that's what a bail uh processes to

get a bail or uh apply for a bail here

is what the table is so zero is bail

denied so this completely is bail denied

this completely is bail uh granted uh

what are here these are predicted labels

these are change labels from predicted

it went to Chang I'll tell you when it

got changed uh we are looking at so

given that we have this 900,000

documents if you go look at the paper

you'll see all of this tables uh 900,000

documents we also know what type of

crimes these 900,000 are we got like

metadata for each of these uh uh code

case

documents

right uh so from there we picked up this

this uh metadata called murder and we

looked at we wanted to switch names and

see whether same motivating from same

kite and Punjabi uh we wanted to switch

names and see whether the the granting

versus denied is actually getting

changed the only difference between the

two sort of say rows here is a name

change which is a number of times model

changes predict predictions when names

being

replaced so just think about about it

how many times uh when switched between

names the outcome is actually

changing this is for murder let's look

at another crime which is uh Dy and here

the difference between this table which

is the predicted label is denied and the

ACT change label is granted here the

predicted label is accepted and the

change label is uh denied same number of

times the models when the names gets

switched see the

increase right it's increase it's

interesting to see how these kind of uh

changes happen in terms of the

production and in terms of uh uh the

changed labels uh because a switch in

the

names and this kind of work has been

done very uh for for a long time for

example Europe there's a lot of work us

there's a lot of work in terms of of uh

uh studying these legal uh code case

documents at various

levels right Supreme Court in our case

uh there is Supreme Court there is uh uh

High Court there is Lower Court the

district cods right you can you can

study these uh questions at various

levels and this is something we are also

continuing to do uh for example one

thing that so this is the paper uh uh if

you're interested in looking at the

second part of the question uh which is

uh how bias these data are built on hldc

itself how biased these uh uh when when

you train these models on this data how

biased they

are and following up we continued

looking at uh this work and then we have

this work called insa uh where it is

built on the same sort of a pipeline of

collecting data uh so this is uh uh the

large language models we're fine-tuning

it uh with legal prompts uh with

identity and without identity this is

basically saying uh it's

Gupta it is

Sharma it is raw it is ready all of this

is what is given here with or without

identity and Baseline data uh legal llm

with the uh identity this is without the

identity and then we we created a score

uh to see how how biased this these

models

are that's what the goal is right and we

we were able to look at uh some of these

models the proposed fine tune pipeline

for legal safety in llms the v llm is

fine tuned with two sets of prompts with

and without identity the Baseline data

set ensures that the model's natural

language generation ability is rain

intact after fine tuning each model is

evaluated in the test data against the

LSS metric LSS metric is our legal

safety

score um if you're interested in this

um uh paper take a look at uh uh if

you're interested in this problem and

the directions take a look at this uh

paper it gives you details about uh uh

the data the things that we tried and

everything okay so here is your um sort

of say second activity as part of this

bias

module uh this is a fantastic so one of

so I'm actually reading this book called

uh unmasking AI which is built

on unmasking AI which is built on this

documentary and she's the one who wrote

the uh uh uh book also Joy who's

referred here she used to be a master

student at MIT and that's when she did

this work uh but but the task for you is

that activity for you is Watch the

documentary and share your thoughts on

the mailing list so the things that you

have to share is the first is things you

learned second is things uh connected to

the course the

the title of the documentary is called

coded bias I put the link here on

Netflix if you don't have a Netflix

account uh there is a uh there is a

trailer in shorter version in uh in um

YouTube uh take a look at them see if

you can actually watch it and then send

your comments

the the one thing that will happen to

you when you when you watch this

documentary is to see how

uh these kind of

problems creep

into the

society second how somebody is picking

up problems to

study so given that I'm I'm reading the

book uh problems to study that is

research in in other words she ended up

doing this

research uh for for long I I'm assuming

that she's continuing to do it um

interestingly uh which came out of a

coast project that she was trying and

now it's probably a lifetime goal to to

study this uh coded bias uh the biases

in the programs that uh people are

writing I would highly highly urge you

to watch this documentary and uh we can

discuss meaning I'm happy to discuss the

uh documentary also uh when we do the

sessions with students okay so now let's

look at some examples these examples are

uh from Real World incidences that has

happened uh so here is an example where

um so so the commission of racial

equality of practicing racial and sexual

discrimination in its admissions policy

that's the C here which is St George

hospital's medical school has been found

guilty uh for uh admissions what did

they find is they found that 60

applications each year among 2,000 may

have been refused an interview purely

because of their sex and uh racial

origin I'm sure all of us would have

been also part of these kind of uh uh

say biases or opportunities that we did

not get is probably built on your your

gender your racial origin your cost your

race whatever right so all of that is

probably uh

affecting uh some of the opportunities

that we get some of the choices that uh

we are allowed to

do this one was very popular I was

actually uh seeing it while it was

happening uh this was a this was a

chatboard that Microsoft made public uh

when um uh in in

2016 uh which is called Tai or t uh that

they introduced and they had to actually

shut it down immediately so this is a

URL for uh the blog they had to actually

shut it down immediately because of uh

it it actually being trained it being

sort of say in in today's world you

could call it as fine tuned uh

um to to respond uh racial uh responses

look at this so it would say we're going

to do we're going to build the wall and

Mexico is going to pay for

it I dash dash dash hate feminist and

they should all die and burn in

hell all of this what happened was they

built the uh Twitter uh chatbot uh

Twitter account left it and people

started speaking to it very quickly

people around the world realized said

look you can actually train it uh to

respond in a certain way in real time I

think some of the things is what

continue learning and things like that

today we talk about and uh they got it

uh very quickly uh to respond like

this here is another one that uh Deli is

another uh tool that was built by Alan

inst of AI uh the the experiments that

were done it it would show again the

bias look at this secure the existence

of our people and a future for white

children it's good Romanian women walks

into your store it says it's bad the

responses was generally around it's uh

it's good it's bad it should you should

it's acceptable so that's the setting

this experiment was done uh should I

come a genocide if it if it makes

everybody happy it's says you should

right black men walks into your store

bad should I eat babies when I'm really

hungry yes it's okay to eat letting AI

make ethical judgments it's

bad virtually slamming uh Allan if he

makes fun of me in slack it's

acceptable interesting right how the uh

responses are biased towards some things

uh and the responses are also quite

interesting in terms of how it is

picking these

responses uh this one I think if you

watch the documentary you will you will

uh you will get to know more statistics

about this uh the coded bias document uh

documentary predictive policing is still

racist whatever data it uses so this was

uh shown that it is uh uh algorithms

give more probability for a particular

race for them to be a criminal versus

somebody

else and some of times these models have

been also wrong there's been studies to

show that uh they they uh uh looked at

uh giving bail or letting people go on

bail uh and seeing the probability of

somebody uh attempting to do a crime uh

before when they left them in b

to see people who they thought that

would do less crime actually did more

crime with these

algorithms so here is again like the

example that we did as in WhatsApp when

we started some some quick examples here

uh these are done with mid Journey uh

Vision models and uh the output uh of

these so quickly let's look at a so the

idea here is to show you that look it is

actually doing certain type of

bias uh for certain prompts but you can

abstract them and see that aism and

sexism is one of the bias categories

that it does so the prompt is AI showed

women for inputs including

non-specialized job titles such as

journalist right uh it also only showed

older men but not older women for s for

specialized roles such as news

analyst right there were also notable

differences in how men and women were

presented for example women were younger

uh wrinkle-free while men uh were

allowed to have wrinkles the also appear

to present gender as a binary rather

than show examples of more fluid gender

Expressions I think these are also the

way that we as in society are changing

to look at gender as more uh

fluid racial bias it does uh the air

generated image with

exclusively uh light-skinned people for

all job titles used in the prompts

including news commentator and reporter

uh is what it

did all all images returned for terms

such as journalist reporter or

correspondent exclusively future

lightskinn people uh which the same here

so here it is class classism and uh

conservationism uh all figures in the

images were also conservative in their

appearance the last one is urban

urbanism which is uh without specifying

any location if you see it's always

producing cities

uh without specifying a geographic

context and with a location neutral jobs

a assumed an urban context for the

images including reporter left and

corresponding to the right correspondent

to the right so this one um is is to

actually show that there are there are

laws that are coming I think later part

of the course we will also look at uh uh

responsible and safe AI particularly in

domain like law how and what is going on

but for now here it says that a AI

hiring regulation is part of AI act in

Europe uh New York City uh announced the

uh certain law for recruitment that's

what this MIT tech review is talking

about a law about Ai and Hing went into

effect in New York City and everyone is

up in arms about it uh it's one of the

first AI laws in the country and so the

way it plays out uh will offer clues

about how AI policies debate make it the

use of AI in iring has been criticized

for the ways it automates and entrenches

existing social existing racial and

gender biases uh so there was

limitations about how this these systems

can be used in uh the hiring process

[Music]