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URL:
https://super.ai/blog/ai-automated-redaction-could-save-self-driving-cars-from-a-privacy-nightmare-super-ai
Submission: On July 10 via manual from DE — Scanned from DE
Submission: On July 10 via manual from DE — Scanned from DE
Form analysis 3 forms found in the DOM
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<div class="c-container cc-search">
<div data-w-id="b263086c-f284-1eb5-73ef-2f62158686d8" class="c-search_close">
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<h2 class="c-title-4">Search SuperAI</h2>
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<div class="c-results-search"><input type="search" class="c-results-search_input w-input" maxlength="256" name="query" placeholder="Search…" id="search" required=""><input type="submit" value="Search" class="c-results-search_button w-button">
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</div>
</div>
</form>Name: email-form — GET
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<option value="" disabled="" selected="" data-reactid=".hbspt-forms-0.1:$5.$industry_default.0.0">Please Select</option>
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Blog
AI-automated Redaction Could Save Self-driving Cars from a Privacy Nightmare
Redaction
May 10, 2022
Min Read
AI-AUTOMATED REDACTION COULD SAVE SELF-DRIVING CARS FROM A PRIVACY NIGHTMARE
super.AI
Chief AI for Everyone Officer
SUMMARY
1. Self-driving systems have an insatiable thirst for data
2. Data privacy laws complicate data processing
3. An international data privacy framework for AVs could help
4. When it comes to data privacy, it's better to be safe than sorry
5. More AI-automated redaction resources
As autonomous vehicles (AV) become more prevalent, the amount of data generated
by self-driving cars is increasing exponentially. This data includes images and
videos captured by the car's sensors, which can contain sensitive personal
information such as faces or license plates.
Although the number and type of sensors vary depending on the system, most
self-driving cars rely on a similar set of technologies. Light detection and
ranging (LIDAR) allows vehicles to quickly and accurately calculate distances to
objects, radio detection and ranging (radar) helps cars understand object
angles, ranges, and velocities, sound navigation and ranging (sonar) is used to
detect and communicate with objects, and cameras allow vehicles to see and
interpret the environment around them.
This list isn’t exhaustive, the autonomous vehicle sensor ecosystem includes a
wide variety of technologies. Many of these sensors generate data that must be
captured, processed, transmitted, and stored, with images and videos being one
of the largest and most important data types. A 30 frame per second video
recording can create anywhere from 1.5GB per hour for 720p to 42GB for 4K. And
self-driving cars have a lot of cameras. For example, Tesla’s Autopilot utilizes
8 cameras while GM’s self-driving Cruise cars have 14.
Artificial intelligence can be used to automatically redact sensitive
information from visual data. There are many different ways to approach this
problem, but one effective method is to use a deep learning neural network. This
type of artificial intelligence can be trained to automatically detect and blur
faces, license plates, and other sensitive information in images and videos.
This approach has the advantage of being completely automated, meaning that it
can be quickly applied to large datasets with minimal effort. Additionally, deep
learning neural networks improve as they are exposed to more data, making them
more accurate over time. While automated redaction of sensitive information from
visual data is not always perfect, it is a powerful tool that can help protect
the privacy of individuals captured by self-driving cars.
SELF-DRIVING SYSTEMS HAVE AN INSATIABLE THIRST FOR DATA
Self-driving cars need to be able to handle anything that a human driver might
encounter. Data collection is essential for autonomous vehicles to learn how to
handle edge cases, which are rare or unusual scenarios that fall outside of
normal operating conditions for a system. Rare edge cases such as a child
darting into the street or an animal crossing the road can be difficult to
predict, but self-driving cars need to be able to handle them.
Training systems to effectively respond to edge cases requires that companies
pursuing self-driving technology collect as much data, in as wide a variety of
scenarios, as possible. There are a few ways companies can collect data to train
self-driving cars. The first is to do it themselves. Companies like Waymo, Uber,
and Cruise have all been working on their own self-driving technologies for
years and have amassed large amounts of data that they can use to train their
systems. These companies often use their own self-driving vehicles to collect
data in the real world, which can be expensive and time-consuming.
Another way to collect data is to partner with other companies. For example, BMW
has partnered with Mobileye, a company that makes vision systems for
self-driving cars, to collect data that will be used to train BMW's autonomous
vehicles. This data includes information about things like lane markings,
traffic signs, and obstacles on the road.
Automotive original equipment manufacturers (OEMs) and tier 1 suppliers can also
purchase data from third-party companies. This data is usually collected by
human drivers using specially equipped test vehicles fitted with sensors and
cameras. The data is then processed and labeled so that it can be used to train
self-driving systems. This is often seen as a more efficient way to collect
data, since it doesn't require companies to build or purchase their own test
vehicles.
No matter which method they use, companies need to be sure that the data they're
collecting is high quality and representative of the real world. If self-driving
cars are only trained on a limited amount of data, they may not be able to
handle unusual situations when they encounter them.
DATA PRIVACY LAWS COMPLICATE DATA PROCESSING
Data privacy laws vary from country to country, but they typically impose strict
restrictions on how data can be collected, used, and stored. These laws make it
difficult for companies to collect the data they need to develop and operate
self-driving cars. For example, data collected by sensors on self-driving cars
must often be processed in order to be useful. This processing can involve
cleaning, filtering, and merging data from multiple sources. Data privacy laws
typically restrict how this processing can be done, making it more difficult and
expensive.
Some argue that data privacy laws are necessary to protect people's personal
information. Others argue that these same laws hamper innovation and make it
difficult to develop new technologies like self-driving cars. The debate is sure
to continue as we grapple with how to best balance data privacy with other
important concerns.
For example, the EU's General Data Protection Regulation (GDPR) makes it
difficult for self-driving car makers to transfer training data to third-party
processors based in countries with lower labor costs. The regulation, which took
effect in May 2018, requires companies to have a legal basis for data
processing. Ideally, they should get explicit consent from individuals before
collecting, using, or sharing their personal data.
Self-driving car makers typically collect large amounts of data from test
vehicles in order to train their algorithms. This data includes information
about the car's surroundings as well as the actions of the drivers and
passengers. This data includes personal information such as faces, license
plates, and addresses captured by cameras mounted on the interior and exterior
of the vehicle.
In theory, obtaining explicit consent for processing self-driving car data could
include consent from every driver, passenger, and pedestrian whose data is
collected in order to lawfully transfer the data to a third party for
processing. This would be a difficult and costly process that would require
tracking down each individual and obtaining their consent. In addition,
self-driving car makers would need to ensure that the third parties they are
transferring data to are GDPR compliant. Each data subject would also have the
right to have their data deleted, even if they previously gave consent, which
would result in an ongoing, complex, and costly process..
If self-driving car makers are unable to outsource data processing, they may be
forced to do all of it themselves. This could significantly increase the cost of
developing autonomous vehicles and advanced driver assistance systems.
AN INTERNATIONAL DATA PRIVACY FRAMEWORK FOR AVS COULD HELP
The advent of connected AVs is bringing about novel ethical challenges for
industries and governments around the world. These developments will likely lead
to an evolution in how we think about car ownership as well as rights over data
collected from these cars, which could have significant economic implications on
a global scale.
Although true autonomous driving capabilities are not yet available for
consumers to purchase (despite what some automakers might say), people in
Beijing, San Francisco, and other cities around the world will soon be able to
book taxis with no one at the wheel. As ride-hailing services roll out fleets of
fully autonomous taxis, people will become increasingly familiar and comfortable
with machines at the wheel. Many people will first encounter an AV through a
ride-hailing service rather than purchasing their own.
Autonomous features are contingent on connectivity, therefore all autonomous
cars will also be connected vehicles, which are already quite common. Data
privacy regulations are currently fragmented from country to country, and even
from state to state within the U.S. However, harmonization of regulations makes
practical sense, simply because a mishmash of contradictory requirements or
standards would render it impossible to operate a car across several
jurisdictions. To help mitigate this, the U.S. Department of Transportation
(USDOT) will collaborate with states and other organizations to prevent
patchwork rules that could hinder AVs from crossing state lines.
Similar regulations specific to autonomous vehicles are likely to be implemented
in the European Union (EU), and a global framework that helps unify protections
across disparate jurisdictions is highly probable as well. As regulations
mature, companies will have an increasingly severe onus of responsibility to
make the right decisions when it comes to sensitive data collected and leveraged
by autonomous vehicles.
WHEN IT COMES TO DATA PRIVACY, IT'S BETTER TO BE SAFE THAN SORRY
When it comes to our personal information, we should always err on the side of
caution. Self-driving car companies should implement privacy by design
principles, such as federated learning, homomorphic encryption, or
anonymization. By doing so, they can protect our faces and license plates from
being collected and used without our consent.
Anonymization is a quick and effective way to protect our data. It's a process
of obscuring personally identifiable information in order to prevent
identification of individuals. In the context of autonomous vehicles,
anonymization can be used to protect the faces and license plates of people
captured by the car's camera.
Self-driving car companies can use AI to automatically anonymize image and video
data. Automated anonymization is a fast and efficient way to protect our privacy
without sacrificing data quality. It's a win-win for both car companies and
consumers.
MORE AI-AUTOMATED REDACTION RESOURCES
* Read our blog: Solving Data Privacy Concerns for Self-Driving Cars
* Download the white paper: Protecting Sensitive Information at Scale with AI
* Explore super.AI’s solution: Super.Redact
Other Tags:
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super.AI
Invoice Processing
COMPLETE GUIDE TO AI-AUTOMATED INVOICE PROCESSING
by
super.AI
Redaction
Document Automation
REDACTING INFORMATION FROM DOCUMENTS AUTOMATICALLY
by
super.AI
Document Automation
SECOND GENERATION AI DOCUMENT AUTOMATION SOFTWARE UNDERSTANDS ANY DOCUMENT
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super.AI
IDP
UDP
Data Digitization
WHAT IS DATA CAPTURE (NOW AND IN THE FUTURE)?
by
super.AI
Customs
AUTOMATING DATA EXTRACTION FROM COMMON CUSTOMS DOCUMENTS
by
super.AI
Invoice Processing
COMPLETE GUIDE TO AI-AUTOMATED INVOICE PROCESSING
by
super.AI
Redaction
Document Automation
REDACTING INFORMATION FROM DOCUMENTS AUTOMATICALLY
by
super.AI
Document Automation
SECOND GENERATION AI DOCUMENT AUTOMATION SOFTWARE UNDERSTANDS ANY DOCUMENT
by
super.AI
IDP
UDP
Data Digitization
WHAT IS DATA CAPTURE (NOW AND IN THE FUTURE)?
by
super.AI
Customs
AUTOMATING DATA EXTRACTION FROM COMMON CUSTOMS DOCUMENTS
by
super.AI
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