Modul Topik 3 - Kecerdasan Buatan

Topik 3
Etika dan Dampak Sosial
Teknologi Kecerdasan Buatan
Dr. Sunu Wibirama
Modul Kuliah Kecerdasan Buatan
Kode mata kuliah: UGMx 001001132012
May 23, 2022

May 23, 2022
1 Capaian Pembelajaran Mata Kuliah
Topik ini akan memenuhi CPMK 3, yakni mampu menjelaskan dampak sosial yang ditim-
bulkan dari perkembangan teknologi kecerdasan buatan serta etika dalam pengembangan
solusi berbasis kecerdasan buatan. Adapun indikator tercapainya CPMK tersebut adalah
mampu memahami kelemahan teknologi kecedasan buatan dalam kasus rasial, maupun
kelemahan karena tidak akuratnya dataset, serta etika-etika yang perlu diperhatikan dalam
hal pengambilan data dan privasi data.
2 Cakupan Materi
Cakupan materi dalam topik ini sebagai berikut:
a) Racial and Gender Bias in Artificial Intelligence: materi ini menjelaskan kasus-kasus
bias rasial dan bias gender yang terjadi dalam dunia penelitian dan implementasi
praktis teknologi kecerdasan buatan. Salah satu kasus yang diangkat adalah kelema-
han teknologi face recognition dari tiga perusahaan besar dunia—Microsoft, IBM, dan
Megvii—dalam mengidentifikasi wajah berkulit gelap. Selain itu akan dibahas juga
sumber-sumber munculnya bias gender dan bias rasial dalam teknologi kecerdasan
buatan.
b) How AI Learns Unhealthy Stereotypes: materi ini akan membahas bagaimana teknologi
kecerdasan buatan mempelajari data yang memiliki kecenderugan stereotipe negatif
dan bias gender. Teknologi berbasis AI yang menjadi contoh kajian dalam materi ini
adalah word embedding model.
c) Explainable Artificial Intelligence: materi ini membahas tentang dasar-dasar explain-
ability dalam teknologi kecerdasan buatan. Explainable AI menjadi salah satu so-
lusi untuk membuat teknologi kecerdasan buatan menjadi lebih transparan dan hasil
pengambilan keputusannya dapat dipertanggungjawabkan.
d) Deepfakes: materi ini membahas tentang cara kerja Deepfakes, yakni sebuah proses
untuk membuat video, suara atau gambar sintetis dengan menggunakan deep learning.
Teknologi Deepfakes biasa digunakan dalam dunia hiburan dan produksi film layar
lebar. Meskipun demikian, teknologi ini menyimpan potensi penyalahgunaan informasi
apabila tidak diawasi dengan ketat.
e) AI Based Cyberattacks: materi ini membahas tentang bentuk-bentuk serangan pada
sistem berbasis kecerdasan buatan, mulai dari malware yang diberi kemampuan untuk
melakukan serangan pada target spesifik dan menyembunyikan informasi serangan
tersebut, sampai dengan adversarial machine learning yang biasa digunakan untuk
mengecoah sistem berbasis machine learning.
f) Ethics in Artificial Intelligence: materi ini membahas tentang etika dalam pengem-
bangan teknologi kecerdasan buatan. Ada enam prinsip utama dalam etika teknologi
kecerdasan buatan, yakni transparency, respect for human values, fairness, safety,
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accountability, dan privacy. Selain itu, materi ini menjelaskan bagaimana teknologi ke-
cerdasan buatan berperan dalam rangka memecahkan problem-problem sosial kemasyarakatan—
penanggulangan bencana lapar, pencarian warga masyarakat yang hilang, pembuatan
materi edukatif, sampai dengan deteksi hoax dalam berita internet.
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Copyright © 2022 Sunu Wibirama | Do not distribute without permission @sunu_wibirama 1
Sunu Wibirama
sunu@ugm.ac.id
Department of Electrical and Information Engineering
Faculty of Engineering
Universitas Gadjah Mada
INDONESIA
Racial and Gender Bias in Artificial Intelligence (Part 1)
Kecerdasan Buatan | Artificial Intelligence
Version: January 2022
sunu@ugm.ac.id
Some stories on gender bias…
• In 2011, during her undergraduate degree at Georgia
Institute of Technology, Ghanaian-US computer
scientist Joy Buolamwini discovered that getting a
robot to play a simple game of peek-a-boo with her
was impossible – the machine was incapable of
seeing her dark-skinned face….
• Later, in 2015, as a Master’s student at
Massachusetts Institute of Technology’s Media Lab
working on a science–art project called Aspire
Mirror, she had a similar issue with facial analysis
software: it detected her face only when she wore a
white mask. Was this a coincidence?
Source:
https://physicsworld.com/a/fighting-algorithmic-bias-in-artificial-intelligence/

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• She then decided to test 1270 faces of
politicians from 3 African and 3 European
countries, with different features, skin tones
and gender, which became her Master’s
thesis project
• This evaluation focuses on gender
classification as a motivating example to
show the need for increased transparency in
the performance of any AI products and
services that focused on human subjects.
Source:
https://dam-prod.media.mit.edu/x/2018/02/06/Gender%20Shades%20Intersectional%20Accuracy%20Disparities.pdf
http://gendershades.org/overview.html
sunu@ugm.ac.id
• The dermatologist approved Fitzpatrick skin
type classification system was used to label
faces as Fitzpatrick Types I, II, III, IV, V, or
VI.
• Then faces labelled Fitzpatrick Types I, II,
and III were grouped in a lighter category
and faces labelled Fitzpatrick Types IV, V,
and VI were grouped into a darker category.

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• Buolamwini uncovered that three
commercially available facial-recognition
technologies made by Microsoft, IBM and
Megvii misidentified darker female faces
nearly 35% of the time, while they worked
almost perfectly (99%) on white men
• What is beyond these results?
sunu@ugm.ac.id
Bias in artificial intelligence
• We often assume that machines can
create smarter, better and more objective
decisions,
• However, this algorithmic bias is one of
many examples that dispels the notion of
machine neutrality and replicates existing
inequalities in society.
• A key factor in the accuracy differences is
the lack of diversity in training images and
benchmark data sets.

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Sunu Wibirama
sunu@ugm.ac.id
INDONESIA
Racial and Gender Bias in Artificial Intelligence (Part 2)
sunu@ugm.ac.id
Another story of racial issues in AI
• Dr. Latanya Sweeney is a professor at
Harvard and director of their Data
Privacy Lab, Sweeney’s research
examines technological solutions to
societal, political and governance
challenges.
• One of her important contributions:
discrimination in online advertising,
where she discovered internet searches
of names “racially associated” with the
black community are 25% more likely to
yield sponsored ads suggesting that the
person has a criminal record.

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Google ’racist’ algorithm for image labeling
• In 2015, software engineer Jacky Alciné pointed
out that the image recognition algorithms in Google
Photos were classifying his black friends as
“gorillas.”
• Google apologized for this and came out saying
that Image recognition technologies were still at an
early stage, but that they would solve the problem.
• If a company as powerful and technologically
advanced as Google can have these sort of issues,
imagine the hundreds of thousands of other
businesses that create AI powered software and
applications without such expertise.
• It’s a good reminder of how difficult it can be to
train AI software to be consistent and robust.
sunu@ugm.ac.id
Important findings by AI NOW Institute
• The AI Now Institute of New York University aims to
produce interdisciplinary research and public engagement
to help ensure that AI systems are accountable to the
communities and contexts in which they’re applied.
• In 2019, AI Now Institute published a report entitled
“Discriminating Systems: Gender, Race, and Power in AI”:
• There is a diversity crisis in the AI sector across gender
and race.
• Recent studies found only 18% of authors at leading AI
conferences are women, and more than 80% of AI
professors are men. This disparity is extreme in the AI
industry.
• The overwhelming focus on ‘women in tech’ is too
narrow and likely to privilege white women over others.

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Sources of algorithmic bias
• Algorithmic decision making is normally developed
through scientific research, most of them are
laboratory-based research.
• When the product is later used in real life setting or
commercial surveillance situations, the product uses
real data.
• These real data maybe inaccurate, or highly
influenced by how the data are acquired in real life.
• “Computers are programmed by people who – even
with good intentions – are still biased and discriminate
within this unequal social world, in which there is
racism and sexism,” (Joy Lisi Rankin, A People’s
History of Computing in the United States, Harvard
University Press, 2018)
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Sunu Wibirama
sunu@ugm.ac.id
INDONESIA
How AI Learns Unhealthy Stereotypes
sunu@ugm.ac.id
Our study case
T. Bolukbasi et al., “Man is to computer programmer as woman is to homemaker? Debiasing word embeddings”, arXiv, 2016

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Word embedding model
• AI can represent words in vector, and make
inference based on quantitative value of the
vector.
• This technique is called word-vector model,
or in the case of Bolukbasi et al. (2016), it is
called word embedding model.
• Using this kind of representation, we may
find new analogy based on the existing
analogies that have been trained to the
model.
Source: Jon Krohn, ”Deep Learning Illustrated,” Addison-Wesley, 2020
sunu@ugm.ac.id
Example of analogy
• Man : Woman as Father : ?

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Example of analogy
• Man : Woman as Father : Mother
• Man : Woman as King : ?
sunu@ugm.ac.id
Example of analogy
• Man : Woman as King : Queen
• Man : Computer programmer as Woman : ?

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sunu@ugm.ac.id
Example of analogy
• Man : Computer programmer as Woman : Homemaker
sunu@ugm.ac.id
Example of analogy
Computer programmer
Associating woman as a homemaker while man as
a computer programmer is unhealthy stereotyping.
Man can be a computer programmer, or a homemaker, or both of them.
Woman is no different from man!

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sunu@ugm.ac.id
Example of analogy
Computer programmer
Man: (1,1)
1 2 3 4 5
1
2
3
4
5
Man
Computer programmer: (3,2)
Computer programmer
Woman: (2,3)
Woman
?
sunu@ugm.ac.id
Example of analogy
Computer programmer
Man: (1,1)
1 2 3 4 5
1
2
3
4
5
Man
Computer programmer: (3,2)
Computer programmer
Woman: (2,3)
Woman
Homemaker: (4,4)
Homemaker

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Combating bias in AI
• Technical solutions:
• Removing bias in words (”zero out” the bias)
• Checking inclusivity of the data
• Diverse workforce:
• Recruiting more representative ML engineers from different
background, cultures, as well as balancing the gender.
• Transparency and auditing in ML workflow  explainable AI
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Sunu Wibirama
sunu@ugm.ac.id
INDONESIA
Explainable Artificial Intelligence
sunu@ugm.ac.id
Handling bias in AI
• One approach to solve bias in AI is by investigating
accuracy of the input data through the lens of non-
technical person (social and ethical point of view).
• However, bias may also come from methodological
mistakes.
• Machine Learning models are often thought of as
black boxes that are impossible to interpret.
• In the end, these models are used by humans who
need to trust them, understand the errors they make,
and the reasoning behind their predictions.
• Giving them some kind of explainability is very
important.

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ML workflow from training to deployment
Source:
https://howtolearnmachinelearning.com/articles/explainable-artificial-intelligence/
sunu@ugm.ac.id
The problem of ML workflow
• The problem here, as we can see by the doubts of the
user, is that the prediction comes with no justification.
• Before deploying any ML model we are more or less aware
of its error.
• But this only gives us a certain number in the loss scale, or
quantifies a certain numerical distance between the labels
in the data and the predicted labels before deployment.
• Can we trust this error above everything?
• Even if there is no actual problem with the predictions, we
would like to understand why the model is saying what it
is saying. You can have good results on test data, and get
pretty accurate predictions, but sometimes that is not
enough.
We use some data to train a model through an specific learning process.
This learning process results in a learned function, which can be then fed
inputs, and it outputs a prediction in an specific interface, which is what the
final user sees and interacts with.

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The shifting trend of AI research
The focus of AI research is slowly turning towards the transparency and explainability of these models.
We want to know why they come up with the predictions they output.
Source:
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Sunu Wibirama
sunu@ugm.ac.id
INDONESIA
Explainable Artificial Intelligence (Part 02)
sunu@ugm.ac.id
#1: Verifying that AI model works as expected
Source: Samek and Binder, Tutorial on Interpretable Machine Learning, MICCAI 2018

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#2: Understand weakness and improve AI model
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#3: Interpretability in sciences

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#4: Learn new things from a learning machine
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Explainability vs Interpretability
Features Value
Feature 1 0.03
Feature 2 0.5
Feature 3 0.71
Feature 4 0.52
Feature 5 0.11
Interpretable AI
Features Value Interpretation
Feature 1 0.03 30% represents N
Feature 2 0.5 20% represents A
Feature 5 0.11 15% represents A
Classes:
Normal (N) and Abnormal (A)
Explainable AI
Determined based
on probability
of the perturbation
data to the real data
Interpretation
Features Value Interpretation
Feature 1 0.03 30% strongly indicates N
Feature 2 0.5 20% indicates A
Feature 3 0.71 15% indicates N
Feature 4 0.52 10% indicates N
Feature 5 0.11 15% strongly indicates A
Determined based
on important
feature score
between each score
to the class
Explanation
We can determine how strong the
feature gives impact to the class

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ML workflow with explainable model
Source:
The ML model not only has the capacity of giving us a prediction, but also of explaining why it
makes that prediction. The new explanation interface displays additional information that can give
our user some insights into why such prediction was made.
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Sunu Wibirama
sunu@ugm.ac.id
INDONESIA
Malicious Use of AI: Deepfakes (Part 01)
sunu@ugm.ac.id
What is Deepfakes?
• Deepfakes are artificially created videos,
images, or audio files created using deep
learning models.
• For example, existing video sequences are
used and faked by replacing faces.
• They are intended to appear as realistic as
possible, even though they were generated
by a machine learning model.
• In addition to using deepfakes for private
videos, they can also be used to disseminate
targeted misinformation.

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Types of Deepfakes
• Face Swapping: The face and facial expressions
of person A should be projected onto the body of
person B. This can even go as far as replacing
the entire body of person B in a video or image
with the body of person A.
• Body Puppetry: Movements, gestures, or facial
expressions of person A are recorded and these
are then to be artificially taken over by person B.
• Voice Swapping: A freely written text is to be
performed as authentically as possible with the
voice of a person. This method can also be
combined with body puppetry, for example.
sunu@ugm.ac.id
Basic concept
• Autoencoders are machine learning models that
consist of an encoder part and a decoder part.
• We can use the same image as input and output.
• This would allow the autoencoder to learn a vector
representation of the image that is as compressed
as possible and stores all the important features.
• This vector is then in turn used by the decoder to
generate the original image from it again.
• The better the learned vector representation of the
autoencoder, the more realistic the generated
image.
Source:
https://jonathan-hui.medium.com/how-deep-learning-fakes-videos-deepfakes-and-how-to-detect-it-c0b50fbf7cb9
https://towardsdatascience.com/what-are-deepfakes-and-how-do-you-recognize-them-f9ab1a143456

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Basic concept
• To decode the features, we use separate decoders for
person A and person B.
• After the training, we process the video frame-by-frame
to swap a person's face with another.
• Using face detection, we extract the face of person A out
and feed it into the encoder.
• However, instead of feeding to its original decoder, we
use the decoder of the person B to reconstruct the
picture.
• Thus, we draw person B with the features of A in the
original video. Then we merge the newly created face
into the original image.
Source:
https://jonathan-hui.medium.com/how-deep-learning-fakes-videos-deepfakes-and-how-to-detect-it-c0b50fbf7cb9
https://towardsdatascience.com/what-are-deepfakes-and-how-do-you-recognize-them-f9ab1a143456
Intuitively, the encoder is detecting face angle, skin tone,
facial expression, lighting and other information that is
important to reconstruct the person A. When we use the
second decoder to reconstruct the image, we are drawing
person B but with the context of A.
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Sunu Wibirama
sunu@ugm.ac.id
INDONESIA
Malicious Use of AI: Deepfakes (Part 02)
sunu@ugm.ac.id

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Using neural networks for synthesizing Obama (1)
sunu@ugm.ac.id
• On the right side, we can see the workflow of
the lip sync paper.
• It substitutes the audio of a weekly
presidential address with another audio
(input audio).
• In the process, it re-synthesizes the mouth
and the chin area so its movement is in-sync
with the fake audio.
• Note that the final composite considers
proper 3D pose so that what Obama
appears to be saying in a target video
matches with the input audio track.

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• First, using an LSTM network, the
audio is transformed into a
sequence of 18 landmark points in
the lip. This LSTM outputs a sparse
mouth shape for each output video
frame.
• Given the mouth shape ,
it synthesizes mouth texture for the
mouth and the chin area. These
mouth textures are then composed
with the target video to recreate the
target frame.
sunu@ugm.ac.id
• We want the mouth texture to look real but also have a
temporal smoothness.
• So the application looks over the target videos to search for
candidates' frames that have the same calculated mouth
shape as what we want.
• Then we merge the candidates together using a median
function. As shown in the images, if we use more candidate
frames to do the averaging, the image gets blurred while the
temporal smoothness improves (no flicking).
• On the other hand, the image can be less blurry but we may
see flicking when transiting from one frame to another.
• To compensate for the blurry, teeth enhancement and
sharpening is performed. But obviously, the sharpness
cannot be completely restored for the lower lip.

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• Finally, we need to retime the frame so we know where
to insert the fake mouth texture.
• This helps us to sync with the head movement. In
particular, Obama's head usually stops moving when he
pauses his speech.
• In the second figure, the top row below is the original
video frames for the input audio we used. We insert this
input audio to our target video (the second row).
• When compare it side-by-side, we realize the mouth
movement from the original video is very close to the
fabricated mouth movement.
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Sunu Wibirama
sunu@ugm.ac.id
INDONESIA
AI Based Cyberattacks (Part 01)
sunu@ugm.ac.id
Cybersecurity
• Cybersecurity is an arms race, where attackers
and defenders play a constantly evolving cat-
and-mouse game.
• In the PC era, we witnessed malware threats
emerging from viruses and worms, and the
security industry responded with antivirus
software.
• In the web era, attacks such as cross-site
request forgery (CSRF) and cross-site scripting
(XSS) were challenging web applications.
• What about the era of Artificial Intelligence?
What kind of threat that we may face in this era?

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How AI can be used in cybersecurity
• Think about what would happen when attackers
start using the power of deep learning and
machine learning for their advantage?
• The malware operates AI algorithms as an
integral part of its business logic.
• For example, using AI based anomaly detection
algorithms to indicate irregular user and system
activity patterns.
• An interesting use case can be found in
DeepLocker, presented by IBM Security
researches in Black Hat USA 2018.
sunu@ugm.ac.id
DeepLocker
• IBM Research developed DeepLocker to better understand how several existing AI models can be combined with
current malware techniques to create a particularly challenging new breed of malware.
• This class of AI-powered evasive malware conceals its intent until it reaches a specific victim.
• It unleashes its malicious action as soon as the AI model identifies the target through indicators like facial recognition,
geolocation and voice recognition.

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DeepLocker Mechanism (1)
• DeepLocker uses a deep neural network (DNN) model to accurately
identify the target attributes and conceal the intent of the malicious
payload. The two core functions are implemented through the two
processes: concealment and unlocking.
Ji, T., Fang, B., Cui, X., Wang, Z., Diao, J., Wang, T., & Yu, W. (2020). The First Step Towards Modeling Unbreakable Malware. arXiv preprint arXiv:2008.06163.
sunu@ugm.ac.id
• During concealment: DeepLocker uses the deep neural networks
(DNN) to conduct dynamic concealment of the symmetric key. The key
is employed to encrypt a plain payload into a cipher payload.

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• During unlocking: the DNN first accurately identifies the target
attribute (as shown by the facial image of Tom Cruise) to assist
generating the key for decoding the cipher payload, and then
unleashes an attack.
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Sunu Wibirama
sunu@ugm.ac.id
INDONESIA
sunu@ugm.ac.id
Adversarial machine learning
• Adversarial machine learning is a machine learning
method that aims to trick machine learning models by
providing deceptive input or stealing parameters of
model.
• Hence, it includes both the generation and detection
of adversarial examples, which are inputs specially
created to deceive classifiers.
• Adversarial machine learning have been extensively
explored in some areas, such as image classification
and spam detection.
• The most extensive studies of adversarial machine
learning have been conducted in the area of image
recognition.

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Why adversarial attack is dangerous?
Imagine you are riding a self-driving car. During your riding, your self-driving car “sees”
Banksy’s popular Girl with Balloon big sticker on the lane. Performing an adversarial attack
requires taking an input image (left), purposely perturbing it with a noise vector (middle),
which forces the AI system to misclassify the input image, ultimately resulting in an
incorrect classification, potentially with major consequences (right).
Source: https://pyimagesearch.com/2020/10/19/adversarial-images-and-attacks-with-keras-and-tensorflow/
sunu@ugm.ac.id
Adversarial attack and steganography
• The adversarial images are images that have pixels purposely
and intentionally perturbed to confuse and deceive AI models.
At the same time, these images look harmless and innocent to
humans.
• The concept is similar to steganography.
• Using steganography algorithms, we can embed data (such as
plaintext messages) in an image without distorting the
appearance of the image itself. This image can be innocently
transmitted to the receiver, who can then extract the hidden
message from the image.
• Similarly, adversarial attacks embed a message in an input
image — but instead of a plaintext message meant for human
consumption, an adversarial attack instead embeds a noise
vector in the input image. This noise vector is purposely
constructed to fool and confuse AI models.
https://www.commonlounge.com/discussion/4bc16dbc2c7145ff87ad0f0d5401a242

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Example of adversarial attack
Finlayson, S. G., Bowers, J. D., Ito, J., Zittrain, J. L., Beam, A. L., & Kohane, I. S. (2019). Adversarial attacks on medical machine learning. Science, 363(6433), 1287-1289.
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Sunu Wibirama
sunu@ugm.ac.id
INDONESIA
sunu@ugm.ac.id
Types of adversarial attacks
and how to protect machine learning system

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Poisoning attack
• A poisoning attack focuses on the data used to
train a model.
• An attacker will change existing data or introduce
incorrectly labelled data.
• The model trained on this data will then make
incorrect predictions on correctly labelled data.
• For example, an attacker could relabel fraud cases
as not fraud.
• The attacker could do this for only specific fraud
cases so when they attempt to commit fraud in the
same way the system will not reject them.
Source: https://towardsdatascience.com/what-is-adversarial-machine-learning-dbe7110433d6
sunu@ugm.ac.id
Evasion attack
• These types of attacks are more
associated with fields like image
recognition.
• Attackers can create images that look
perfectly normal to a human but results in
completely incorrect predictions.
• The attacker modifies data used by a
model to make predictions and not data
used to train models.
Source:
https://towardsdatascience.com/what-is-adversarial-machine-learning-dbe7110433d6
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572.

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sunu@ugm.ac.id
Model stealing
• Model stealing attacks also focus on the
model after it has been trained.
• Specifically, an attacker wants to learn
about the structure of the model or or
even a model’s hyperparameters.
• For example, an attacker could identify
exactly what words a spam filtering
model will flag.
• The attacker could then alter spam/
phishing emails to ensure they are
delivered to the inbox.
Source: https://towardsdatascience.com/how-to-attack-machine-learning-evasion-poisoning-inference-trojans-backdoors-a7cb5832595c
sunu@ugm.ac.id
Protecting machine learning system
• Adversarial training: train the model to identify
adversarial examples. By training/ retraining a model
using these examples, it will be able to identify future
adversarial attacks.
• Switching models: the second approach is to use
multiple models within your system. The model used to
make predictions is changed randomly. Poisoning or
findings adversarial examples for multiple models is
much harder than for just one.
• Generalised model: instead of switching models, they
are combined to create one generalized model. This
means all the individual models would contribute to the
final prediction. An adversarial example may be able to
trick one model but it would likely not be effective against
all of them
Source: https://towardsdatascience.com/what-is-adversarial-machine-learning-dbe7110433d6

19/05/2022
sunu@ugm.ac.id
Sunu Wibirama
sunu@ugm.ac.id
INDONESIA
Ethics in Artificial Intelligence (Part 01)
sunu@ugm.ac.id

19/05/2022
sunu@ugm.ac.id
Some concerns on AI technology
• Bias: AI systems can be biased because they're designed to
look for patterns in data and favour those patterns.
• Liability: AI systems can't be perfect. When mistakes are
made, who's responsible?
• Security: As AI systems advance, how do we stop bad actors
from weaponizing them? What happens if robots can fight and
drones can attack?
• Human Interaction: There's already a decline in person-to-
person interactions. Are we sacrificing humanity's social
aspect?
• Employment: Repetitive, predictable jobs that can be
automated will be automated. Those replaced have to retrain
themselves in areas where robots can't come in easily, such
as, creative or critical thinking.
• Wealth Inequality: Companies rich enough to invest in AI will
get richer by reducing cost and being more efficient.
• Power & Control: Big companies that use AI can control and
manipulate how society thinks and acts.
Source:
https://www.weforum.org/agenda/2016/10/top-10-ethical-issues-in-artificial-intelligence/
https://www.wbpro.com/ethics-in-artificial-intelligence/
https://devopedia.org/ethical-ai#The-Institute-for-Ethical-AI-&-ML-2019
https://www.fast.ai/2018/09/24/ai-ethics-resources/
sunu@ugm.ac.id
Guiding principal for ethical AI
Source:
https://www.wbpro.com/ethics-in-artificial-intelligence/

19/05/2022
sunu@ugm.ac.id
5
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19/05/2022
sunu@ugm.ac.id
Sunu Wibirama
sunu@ugm.ac.id
INDONESIA
Ethics in Artificial Intelligence (Part 02)
sunu@ugm.ac.id
How can we use
AI for social good?

19/05/2022
sunu@ugm.ac.id
sunu@ugm.ac.id
Some of AI capabilities for societal benefit
Source: McKinsey Global Institute analysis

19/05/2022
sunu@ugm.ac.id
sunu@ugm.ac.id

19/05/2022
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sunu@ugm.ac.id
8
End of File

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