Artificial Intelligence has transformed various industries ranging from technology to manufacturing. In these slides, we have showcased how AI can be used for social good.

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Artificial Intelligence for Social Good
By
Shruti Jadon (IEEE member)
Software Engineer at Juniper Networks
Former Visiting Researcher at Brown University
MS in CS from Umass Amherst

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How AI
change help
us solve
social
problems?
1. Cancer Detection
2. Climate Change
3. Agriculture
4. Wildfire Detection
5. Wildlife Conservation

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Cancer Detection
 One of many relatable AI applications for social good pertains to the
healthcare sector, as it may personally affect us all. Cancer causes
1 in 6 deaths globally, an estimated 9.6 million people died in
2018, and over 300.000 cases are diagnosed each year. As the
disease keeps spreading, AI approaches for healthcare are quickly
establishing revolutionary solutions in this space.
 Predictive analytics techniques are being applied to healthcare
scenarios, in order to evaluate clinical data and anticipate future
trends. One of the main advantages of predictive analytics in this
field is in improving the accuracy of diagnosis and treatment.

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Climate Change
 AI can improve energy efficiency on the city scale by incorporating
data from smart meters and the Internet of Things (the internet of
computing devices that are embedded in everyday objects,
enabling them to send and receive data) to forecast energy
demand.
 AI is also actively used for accurate projections about
temperatures and weather conditions.

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AI in Agriculture
Data from sensors in the field that monitor crop moisture, soil
composition and temperature help AI improve production and know
when crops need watering.
Collected data from drones can also monitor conditions to understand
time to spray and harvest crops. This will result in increased efficiency,
enhanced yields, and lower use of water, fertilizer and pesticides.

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Wildfire Detection
 Wildfires have cost thousands of lives and are responsible for
one-third of global CO2 emissions! Deforestation and agriculture
damages contribute 17 percent to climate change.
 California recently funded defense contractor Northrop Grumman
and wildfire-analyzing startup Technosylva to produce prototypes
that can help detect and predict the advancement of wildfires.

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Wildlife Conservation
 Protection Assistant for Wildlife Security (PAWS) is an artificial
intelligence system developed at Harvard that predicts poaching
risk levels in different areas of a wildlife preserve and helps
rangers patrol more efficiently.
 Organizations like Rainforest Connection have audio sensors in
rainforests to listen for and report chainsaw detection in real time,
allowing park rangers to mitigate illegal logging.
 Audio sensors is also being used to understand the biodiversity of
the rainforest.

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Research Work

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SSM-Net for Plants Disease
Identification in Low Data Regime
 Plant disease detection is an essential factor in increasing
agricultural production. Due to the difficulty of disease detection,
farmers spray various pesticides on their crops to protect them,
causing great harm to crop growth and food standards.
 Deep learning can offer critical aid in detecting such diseases.
However, it is highly inconvenient to collect a large volume of
data on all forms of the diseases afflicting a specific plant
species.

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Siamese Networks
 A Siamese network, as the name
suggests, is an architecture with
two parallel layers.
 It compares two inputs based on
a similarity metric and checks
whether they are the same or not.
This network consists of two
identical neural networks, which
share similar parameters, each
head taking one input data point.
 The last layers of these networks
are fed to a contrastive loss
function layer, which calculates
the similarity between the two
inputs.

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Are There any Risks of using AI?

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Risks of artificial intelligence
The World Economic Forum report identified six categories of AI risk:
 Performance. The black box conclusions of AI may not be understandable
to humans and thus it may be impossible to determine if they are accurate
or desirable. Deep learning could be risky for applications such as early
warning systems for natural disasters where more certainty is needed.
 Security. AI could potentially be hacked, enabling bad actors to interfere
with energy, transportation, early warning or other crucial systems.
 Control risks. Since AI systems interact autonomously, they can produce
unpredictable outcomes. For example, two systems produced a language
of their own that humans couldn’t understand.

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Risks of artificial intelligence
 Economic risks. Companies that are slower to adopt AI may suffer
economic consequences as their AI-based competition advances. We are
already seeing stores are closing as the economy becomes increasingly
digitized.
 Social risk. AI is resulting in more automation, which will eliminate jobs in
almost every field. Autonomous weapon systems could also hasten and
exacerbate global conflicts.
 Ethical risks. Since AI uses inferred assumptions about groups and
communities in making decisions, it could lead to increased bias. The
collection of data also raises privacy issues.

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Ethical Concerns
Prof. Erik L. Miller from UMass Amherst CS department proposed
to regulate A.I. the way the FDA regulates the medical device
industry.
Link: https://digitalprivacy.news/2020/06/30/qa-umass-erik-
learned-
miller/#:~:text=Erik%20Learned%2DMiller%2C%20a%20comput
er,could%20foster%20trust%20in%20FRTs.

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Thank you !
Q&A