SageMaker aims to provide a machine learning service which can be used to build, train, and deploy ML models for virtually any use case.

During this year’s re:Invent conference, AWS made several announcements to further improve SageMaker’s capabilities.

Swami Sivasubramanian, VP of Amazon Machine Learning at AWS, said:

“Hundreds of thousands of everyday developers and data scientists have used our industry-leading machine learning service, Amazon SageMaker, to remove barriers to building, training, and deploying custom machine learning models. One of the best parts about having such a widely-adopted service like SageMaker is that we get lots of customer suggestions which fuel our next set of deliverables.

Today, we are announcing a set of tools for Amazon SageMaker that makes it much easier for developers to build end-to-end machine learning pipelines to prepare, build, train, explain, inspect, monitor, debug, and run custom machine learning models with greater visibility, explainability, and automation at scale.”

The first announcement is Data Wrangler, a feature which aims to automate the preparation of data for machine learning.

Data Wrangler enables customers to choose the data they want from their various data stores and import it with a single click. Over 300 built-in data transformers are included to help customers normalise, transform, and combine features without having to write any code.

Frank Farrall, Principal of AI Ecosystems and Platforms Leader at Deloitte, comments:

“SageMaker Data Wrangler enables us to hit the ground running to address our data preparation needs with a rich collection of transformation tools that accelerate the process of machine learning data preparation needed to take new products to market.

In turn, our clients benefit from the rate at which we scale deployments, enabling us to deliver measurable, sustainable results that meet the needs of our clients in a matter of days rather than months.”

The second announcement is Feature Store. Amazon SageMaker Feature Store provides a new repository that makes it easy to store, update, retrieve, and share machine learning features for training and inference.

Feature Store aims to overcome the problem of storing features which are mapped to multiple models. A purpose-built feature store helps developers to access and share features that make it much easier to name, organise, find, and share sets of features among teams of developers and data scientists. Because it resides in SageMaker Studio – close to where ML models are run – AWS claims it provides single-digit millisecond inference latency.

Mammad Zadeh, VP of Engineering, Data Platform at Intuit, says:

“We have worked closely with AWS in the lead up to the release of Amazon SageMaker Feature Store, and we are excited by the prospect of a fully managed feature store so that we no longer have to maintain multiple feature repositories across our organization.

Our data scientists will be able to use existing features from a central store and drive both standardisation and reuse of features across teams and models.”

Next up, we have SageMaker Pipelines—which claims to be the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning.

Developers can define each step of an end-to-end machine learning workflow including the data-load steps, transformations from Amazon SageMaker Data Wrangler, features stored in Amazon SageMaker Feature Store, training configuration and algorithm set up, debugging steps, and optimisation steps.

SageMaker Clarify may be one of the most important features being debuted by AWS this week considering ongoing events.

Clarify aims to provide bias detection across the machine learning workflow, enabling developers to build greater fairness and transparency into their ML models. Rather than turn to often time-consuming open-source tools, developers can use the integrated solution to quickly try and counter any bias in models.

Andreas Heyden, Executive VP of Digital Innovations for the DFL Group, says:

“Amazon SageMaker Clarify seamlessly integrates with the rest of the Bundesliga Match Facts digital platform and is a key part of our long-term strategy of standardising our machine learning workflows on Amazon SageMaker.

By using AWS’s innovative technologies, such as machine learning, to deliver more in-depth insights and provide fans with a better understanding of the split-second decisions made on the pitch, Bundesliga Match Facts enables viewers to gain deeper insights into the key decisions in each match.”

Deep Profiling for Amazon SageMaker automatically monitors system resource utilisation and provides alerts where required for any detected training bottlenecks. The feature works across frameworks (PyTorch, Apache MXNet, and TensorFlow) and collects system and training metrics automatically without requiring any code changes in training scripts.

Next up, we have Distributed Training on SageMaker which AWS claims makes it possible to train large, complex deep learning models up to two times faster than current approaches.

Kristóf Szalay, CTO at Turbine, comments:

“We use machine learning to train our in silico human cell model, called Simulated Cell, based on a proprietary network architecture. By accurately predicting various interventions on the molecular level, Simulated Cell helps us to discover new cancer drugs and find combination partners for existing therapies.

Training of our simulation is something we continuously iterate on, but on a single machine each training takes days, hindering our ability to iterate on new ideas quickly.

We are very excited about Distributed Training on Amazon SageMaker, which we are expecting to decrease our training times by 90% and to help us focus on our main task: to write a best-of-the-breed codebase for the cell model training.

Amazon SageMaker ultimately allows us to become more effective in our primary mission: to identify and develop novel cancer drugs for patients.”

SageMaker’s Data Parallelism engine scales training jobs from a single GPU to hundreds or thousands by automatically splitting data across multiple GPUs, improving training time by up to 40 percent.

With edge computing advancements increasing rapidly, AWS is keeping pace with SageMaker Edge Manager.

Edge Manager helps developers to optimise, secure, monitor, and maintain ML models deployed on fleets of edge devices. In addition to helping optimise ML models and manage edge devices, Edge Manager also provides the ability to cryptographically sign models, upload prediction data from devices to SageMaker for monitoring and analysis, and view a dashboard which tracks and provided a visual report on the operation of the deployed models within the SageMaker console.

Igor Bergman, VP of Cloud and Software of PCs and Smart Devices at Lenovo, comments:

“SageMaker Edge Manager will help eliminate the manual effort required to optimise, monitor, and continuously improve the models after deployment. With it, we expect our models will run faster and consume less memory than with other comparable machine-learning platforms.

As we extend AI to new applications across the Lenovo services portfolio, we will continue to require a high-performance pipeline that is flexible and scalable both in the cloud and on millions of edge devices. That’s why we selected the Amazon SageMaker platform. With its rich edge-to-cloud and CI/CD workflow capabilities, we can effectively bring our machine learning models to any device workflow for much higher productivity.”

Finally, SageMaker JumpStart aims to make it easier for developers which have little experience with machine learning deployments to get started.

JumpStart provides developers with an easy-to-use, searchable interface to find best-in-class solutions, algorithms, and sample notebooks. Developers can select from several end-to-end machine learning templates(e.g. fraud detection, customer churn prediction, or forecasting) and deploy them directly into their SageMaker Studio environments.

AWS has been on a roll with SageMaker improvements—delivering more than 50 new capabilities over the past year. After this bumper feature drop, we probably shouldn’t expect any more until we’ve put 2020 behind us.

You can find coverage of AWS’ more cloud-focused announcements via our sister publication CloudTech here.

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Amazon Web Services (AWS) first launched a GPU instance 10 years ago with the NVIDIA M2050. It’s rather poetic that, a decade on, NVIDIA is now providing AWS with the hardware to power the next generation of groundbreaking innovations.

The A100 outperformed CPUs in this year’s MLPerf by up to 237x in data centre inference. A single NVIDIA DGX A100 system – with eight A100 GPUs – provides the same performance as nearly 1,000 dual-socket CPU servers on some AI applications.

“We’re at a tipping point as every industry seeks better ways to apply AI to offer new services and grow their business,” said Ian Buck, Vice President of Accelerated Computing at NVIDIA, following the benchmark results.

Businesses can access the A100 in AWS’ P4d instance. NVIDIA claims the instances reduce the time to train machine learning models by up to 3x with FP16 and up to 6x with TF32 compared to the default FP32 precision.

Each P4d instance features eight NVIDIA A100 GPUs. If even more performance is required, customers are able to access over 4,000 GPUs at a time using AWS’s Elastic Fabric Adaptor (EFA).

Dave Brown, Vice President of EC2 at AWS, said:

“The pace at which our customers have used AWS services to build, train, and deploy machine learning applications has been extraordinary. At the same time, we have heard from those customers that they want an even lower-cost way to train their massive machine learning models.

Now, with EC2 UltraClusters of P4d instances powered by NVIDIA’s latest A100 GPUs and petabit-scale networking, we’re making supercomputing-class performance available to virtually everyone, while reducing the time to train machine learning models by 3x, and lowering the cost to train by up to 60% compared to previous generation instances.”

P4d supports 400Gbps networking and makes use of NVIDIA’s technologies including NVLink, NVSwitch, NCCL, and GPUDirect RDMA to further accelerate deep learning training workloads.

Some of AWS’ customers across various industries have already begun exploring how the P4d instance can help their business.

Karley Yoder, VP & GM of Artificial Intelligence at GE Healthcare, commented:

“Our medical imaging devices generate massive amounts of data that need to be processed by our data scientists. With previous GPU clusters, it would take days to train complex AI models, such as Progressive GANs, for simulations and view the results.

Using the new P4d instances reduced processing time from days to hours. We saw two- to three-times greater speed on training models with various image sizes while achieving better performance with increased batch size and higher productivity with a faster model development cycle.”

For an example from a different industry, the research arm of Toyota is exploring how P4d can improve their existing work in developing self-driving vehicles and groundbreaking new robotics.

“The previous generation P3 instances helped us reduce our time to train machine learning models from days to hours,” explained Mike Garrison, Technical Lead of Infrastructure Engineering at Toyota Research Institute.

“We are looking forward to utilizing P4d instances, as the additional GPU memory and more efficient float formats will allow our machine learning team to train with more complex models at an even faster speed.”

P4d instances are currently available in the US East (N. Virginia) and US West (Oregon) regions. AWS says further availability is planned soon.

You can find out more about P4d instances and how to get started here.

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Companies around the world are having to look at new ways of safely continuing business as we adapt to the “new normal” of life with the coronavirus.

Amazon has used its AI expertise to create what it calls the Distance Assistant. Using a time-of-flight sensor, often found in modern smartphones, the AI measures the distance between employees.

The AI is used to differentiate people from their background and what it sees is displayed on a 50-inch screen for workers to quickly see whether they’re adhering to keeping a safe distance.

Augmented reality is used to overlay either a green or red circle underneath each employee. As you can probably guess – a green circle means that the employee is a safe distance from others, while a red circle indicates that person needs to give others some personal space.

The whole solution is run locally and does not require access to the cloud to function. Amazon says it’s only deployed Distance Assistant in a handful of facilities so far but plans to roll out “hundreds” more “over the next few weeks.”

While the solution appears rather draconian, it’s a clever – and arguably necessary – way of helping to keep people safe until a vaccine for the virus is hopefully found. However, it will strengthen concerns that the coronavirus will be used to normalise increased surveillance and erode privacy.

Amazon claims it will be making Distance Assistant open-source to help other companies adapt to the coronavirus pandemic and keep their employees safe.

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During a midnight keynote, Amazon unveiled Transcribe Medical, SageMaker Operators for Kubernetes, and DeepComposer.

Transcribe Medical

The first announcement we’ll be talking about is likely to have the biggest impact on people’s lives soonest.

Transcribe Medical is designed to transcribe medical speech for primary care. The feature is aware of medical speech in addition to standard conversational diction.

Amazon says Transcribe Medical can be deployed across “thousands” of healthcare facilities to provide clinicians with secure note-taking abilities.

Transcribe Medical offers an API and can work with most microphone-equipped smart devices. The service is fully managed and sends back a stream of text in real-time.

Furthermore, and most importantly, Transcribe Medical is covered under AWS’ HIPAA eligibility and business associate addendum (BAA). This means that any customer that enters into a BAA with AWS can use Transcribe Medical to process and store personal health information legally.

SoundLines and Amgen are two partners which Amazon says are already using Transcribe Medical.

Vadim Khazan, president of technology at SoundLines, said in a statement:

“For the 3,500 health care partners relying on our care team optimisation strategies for the past 15 years, we’ve significantly decreased the time and effort required to get to insightful data.”

SageMaker Operators for Kubernetes

The next announcement is Amazon SageMaker Operators for Kubernetes.

Amazon’s SageMaker is a machine learning development platform and this new feature lets data scientists using Kubernetes train, tune, and deploy AI models.

SageMaker Operators can be installed on Kubernetes clusters and jobs can be created using Amazon’s machine learning platform through the Kubernetes API and command line tools.

In a blog post, AWS deep learning senior product manager Aditya Bindal wrote:

“Customers are now spared all the heavy lifting of integrating their Amazon SageMaker and Kubernetes workflows. Starting today, customers using Kubernetes can make a simple call to Amazon SageMaker, a modular and fully-managed service that makes it easier to build, train, and deploy machine learning (ML) models at scale.”

Amazon says that compute resources are pre-configured and optimised, only provisioned when requested, scaled as needed, and shut down automatically when jobs complete.

SageMaker Operators for Kubernetes is generally available in AWS server regions including US East (Ohio), US East (N. Virginia), US West (Oregon), and EU (Ireland).

DeepComposer

Finally, we have DeepComposer. This one is a bit more fun for those who enjoy playing with hardware toys.

Amazon calls DeepComposer the “world’s first” machine learning-enabled musical keyboard. The keyboard features 32-keys and two octaves, and is designed for developers to experiment with pretrained or custom AI models.

In a blog post, AWS AI and machine learning evangelist Julien Simon explains how DeepComposer taps a Generative Adversarial Network (GAN) to fill in gaps in songs.

After recording a short tune, a model for the composer’s favourite genre is selected in addition to setting the model’s parameters. Hyperparameters are then set along with a validation sample.

Once this process is complete, DeepComposer then generates a composition which can be played in the AWS console or even shared to SoundCloud (then it’s really just a waiting game for a call from Jay-Z).

Developers itching to get started with DeepComposer can apply for a physical keyboard for when they become available, or get started now with a virtual keyboard in the AWS console.

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PAX, a Dutch NGO, ranked 50 firms based on three criteria:

1. If technology they’re developing could be used for killer AI.
2. Their involvement with military projects.
3. If they’ve committed to not being involved with military applications in the future.

Microsoft and Amazon are named among the world’s ‘highest risk’ tech companies putting the world at risk, while Google leads the way among large tech companies implementing proper safeguards.

Google’s ranking among the safest tech companies may be of surprise to some given the company’s reputation for mass data collection. Mountain View was also caught up in an outcry regarding its controversial ‘Project Maven’ contract with the Pentagon.

Project Maven was a contract Google had with the Pentagon to supply AI technology for military drones. Several high-profile employees resigned over the contract, while over 4,000 Google staff signed a petition demanding their management cease the project and never again “build warfare technology.”

Following the Project Maven backlash, Google CEO Sundar Pichai promised in a blog post the company will not develop technologies or weapons that cause harm, or anything which can be used for surveillance violating “internationally accepted norms” or “widely accepted principles of international law and human rights”.

Pichai’s promise not to be involved with such contracts in the future appears to have satisfied PAX in their rankings. Google has since attempted to improve its public image around its AI developments with things such as the creation of a dedicated ethics panel, but that backfired and collapsed quickly after featuring a member of a right-wing think tank and a defense drone mogul.

“Why are companies like Microsoft and Amazon not denying that they’re currently developing these highly controversial weapons, which could decide to kill people without direct human involvement?” said Frank Slijper, lead author of the report published this week.

Microsoft, which ranks among the highest risk tech companies in PAX’s list, warned investors back in February that its AI offerings could damage the company’s reputation. 

In a quarterly report, Microsoft wrote:

“Some AI scenarios present ethical issues. If we enable or offer AI solutions that are controversial because of their impact on human rights, privacy, employment, or other social issues, we may experience brand or reputational harm.”

Some of Microsoft’s forays into the technology have already proven troublesome, such as chatbot ‘Tay’ which became a racist, sexist, generally-rather-unsavoury character after internet users took advantage of its machine-learning capabilities.

Microsoft and Amazon are both currently bidding for a $10 billion Pentagon contract to provide cloud infrastructure for the US military.

“Tech companies need to be aware that unless they take measures, their technology could contribute to the development of lethal autonomous weapons,” comments Daan Kayser, PAX project leader on autonomous weapons. “Setting up clear, publicly-available policies is an essential strategy to prevent this from happening.”

You can find PAX’s full risk assessment of the companies here (PDF).

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Loup Ventures asked each of the three main virtual assistants – Google Assistant, Alexa, and Siri – a total of 800 questions. The assistants understood almost every question, even if not all of the responses were correct/sufficient.

In terms of understanding the questions, these are the results:

• Google Assistant – 100 percent
• Alexa – 99.9 percent
• Siri – 99.8 percent

Loup Ventures’ say their question set it designed to comprehensively test a virtual assistant’s ability and utility. Questions are broken down into five categories:

1. Local – Where is the nearest coffee shop?
2. Commerce – Order me more paper towels.
3. Navigation – How do I get to Uptown on the bus?
4. Information – Who do the Twins play tonight?
5. Command – Remind me to call Jerome at 2 pm today.

This is the percentage of questions each assistant answered correctly:

• Google Assistant – 92.9 percent
• Siri – 83.1 percent
• Alexa –  79.8 percent

The results are a huge improvement over Assistant, Alexa, and Siri’s results last year.

In 2018, Loup Ventures found Google Assistant answered the most questions with an 86 percent success rate. This was followed by Siri at 79 percent, while Alexa trailed behind at just 61 percent.

Alexa’s jump in answering the question correctly from 61 percent last year to almost 80 percent this year is the most commendable performance improvement, even if Amazon’s assistant is still in last place overall.

The researchers explained that they’ve stopped including Cortana in their tests due to a strategy change from Microsoft earlier this year.

Microsoft said in January that it’s no longer attempting to compete with Alexa or Google Assistant in areas like smart speakers, but instead is repositioning Cortana more like a skill that can be embedded in services where she can be of assistance.

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Rekognition was called out by the American Civil Liberties Union (ACLU) for erroneously labelling those with darker skin tones as criminals more often in a test using a database of mugshots.

Jacob Snow, Technology and Civil Liberties Attorney at the ACLU Foundation of Northern California, said:

“Face surveillance will be used to power discriminatory surveillance and policing that targets communities of colour, immigrants, and activists. Once unleashed, that damage can’t be undone.”

Amazon disputed the methodology used by the ACLU claiming the default ‘confidence’ setting of 80 percent was left on when it suggests at least 95 percent for law enforcement purposes.

Orlando Police was using Rekognition to automatically detect suspected criminals in live footage taken by surveillance cameras. Despite help from Amazon, the police spent 15 months failing to get it to work properly.

“We haven’t even established a stream today,” the city’s chief information officer Rosa Akhtarkhavari told the Orlando Weekly. “We’re talking about more than a year later. We have not, today, established a reliable stream.”

Employees of Amazon recently wrote a letter to CEO Jeff Bezos expressing their concerns over the sale of facial recognition software and other services to US government bodies such as ICE (Immigration and Customs Enforcement).

In their letter, the Amazonians wrote:

“We refuse to build the platform that powers ICE, and we refuse to contribute to tools that violate human rights. As ethically concerned Amazonians, we demand a choice in what we build and a say in how it is used.”

Orlando Police has now cancelled its contract with Amazon. The news will be of some relief to those concerned about the privacy implications of such big brother-like systems.

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Current digital assistants listen for a wakeword such as “Ok, Google” or “Alexa,” before recording speech for processing. Especially for companies such as Google and Amazon which thrive on knowing everything about users, this helps to quell privacy concerns.

There are some drawbacks from this approach, mainly context. Future AI assistants will be able to provide more help when armed with information leading up to the request.

For example, say you were discussing booking a seat at your favourite restaurant next Tuesday. After asking, “Alexa, do I have anything on my schedule next Tuesday?” it could respond: “No, would you like me to book a seat at the restaurant you were discussing and add it to your calendar?”

Today, such a task would require three separate requests.

Amazon’s patent isn’t quite as complex just yet. The example provided in the filing envisions allowing the user to say things such as “Play ‘And Your Bird Can Sing’ Alexa, by the Beatles,” (Note the wakeword after the play song command.)

David Emm, Principal Security Researcher at Kaspersky Lab, said:

“Many Amazon Alexa users will likely be alarmed by today’s news that the company’s latest patent would allow the devices – commonplace in homes across the UK – to record everything a person says before even being given a command. Whilst the patent doesn’t suggest it will be installed in future Alexa-enabled devices, this still signals an alarming development in the further surrender of our personal privacy.

Given the amount of sensitive information exchanged in the comfort of people’s homes, Amazon would be able to access a huge volume of personal information – information that would be of great value to cybercriminals and threat actors. If the data isn’t secured effectively, a successful breach of Amazon’s systems could have a severe knock-on effect on the data security and privacy of huge numbers of people.

If this patent comes into effect, consumers need to be made very aware of the ramifications of this – and to be fully briefed on what data is being collected, how it is being used, and how they can opt out of this collection. Amazon may argue that analysing stored data will make their devices smarter for Alexa owners – but in today’s digital era, such information could be used nefariously, even by trusted parties. For instance, as we saw with Cambridge Analytica, public sector bodies could target election campaigns at those discussing politics.

There’s a world of difference between temporary local storage of sentences, to determine if the command word has been used, and bulk retention of data for long periods, or permanently – even if the listening process is legitimate and consumers have opted in. There have already been criticisms of Amazon for not making it clear what is being recorded and stored – and we are concerned that this latest development shows the company moving in the wrong direction – away from data visibility, privacy, and consent.”

There’s a joke about Uber that society used to tell you not to get into cars with strangers, and now you’re encouraged to order one from your phone. Lyft has been able to ride in Uber’s wake relatively negative PR free.

Getting the balance right between innovation and safety can be a difficult task. Pioneers often do things first and face the backlash before it actually becomes somewhat normal. That’s not advocating Amazon’s possible approach, but we’ve got to be careful outrage doesn’t halt progress while remaining vigilant of actual dangers.

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Back in January, AI News reported on findings by Algorithmic Justice League founder Joy Buolamwini who researched some of the world’s most popular facial recognition algorithms.

Buolamwini found most of the algorithms were biased and misidentified subjects with darker skin colours and/or females more often.

Here were the results in descending order of accuracy:

Microsoft

• Lighter Males (100 percent)
• Lighter Females (98.3 percent)
• Darker Males (94 percent)
• Darker Females (79.2 percent)

Face++

• Darker Males (99.3 percent)
• Lighter Males (99.2 percent)
• Lighter Females (94 percent)
• Darker Females (65.5 percent)

IBM

• Lighter Males (99.7 percent)
• Lighter Females (92.9 percent)
• Darker Males (88 percent)
• Darker Females (65.3 percent)

Amazon executives rebuked the findings and claimed a lower level of accuracy was used than what they recommend for law enforcement use.

“The answer to anxieties over new technology is not to run ‘tests’ inconsistent with how the service is designed to be used, and to amplify the test’s false and misleading conclusions through the news media,” Matt Wood, GM of AI for Amazon’s cloud-computing division, wrote in a January blog post.

Signatories of the open letter came to Buolamwini’s defense, including AI pioneer Yoshua Bengio who is a recent winner of the Turing Award.

“In contrast to Dr. Wood’s claims, bias found in one system is cause for concern in the other, particularly in use cases that could severely impact people’s lives, such as law enforcement applications,” they wrote.

Despite having the most accurate facial recognition, Microsoft has rightly not been content at that and has further improved its accuracy since Buolamwini’s work. The firm supports a policy requiring signs to be visible wherever facial recognition is used.

IBM has also made huge strides in levelling the accuracy of their algorithms to represent all parts of society. Earlier this year, the company unveiled a new one million image dataset more representative of the diversity in society.

When Buolamwini reassessed IBM’s algorithm, the accuracy when assessing darker males jumped from 88 percent to 99.4 percent, for darker females from 65.3 percent to 83.5 percent, for lighter females from 92.9 percent to 97.6 percent, and for lighter males it stayed the same at 97 percent.

Buolamwini commented: “So for everybody who watched my TED Talk and said: ‘Isn’t the reason you weren’t detected because of, you know, physics? Your skin reflectance, contrast, et cetera,’ — the laws of physics did not change between December 2017, when I did the study, and 2018, when they launched the new results.”

“What did change is they made it a priority.”

Aside from potentially automating societal problems like racial profiling, inaccurate facial recognition could be the difference between life and death. For example, a recent study (PDF) found that driverless cars observing the road for pedestrians had a more difficult time detecting individuals with darker skin colours.

Everyone, not just AI experts, should be pressuring companies to ensure biases are kept well away from algorithms.

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Michael Punke, VP of Global Public Policy at Amazon Web Services, said.

“Over the past several months, we’ve talked to customers, researchers, academics, policymakers, and others to understand how to best balance the benefits of facial recognition with the potential risks.

It’s critical that any legislation protect civil rights while also allowing for continued innovation and practical application of the technology.”

In a blog post today, Amazon highlighted five guidelines to ensure facial recognition is developed and used ethically.

The first of the five calls for facial recognition to follow existing laws which protect civil liberties. To ensure accountability, the second guideline wants all facial recognition to be reviewed by humans before any decision is taken.

Other guidelines include a call for transparancy in how agencies are using facial recognition technology, and visual notices placed where it’s being used in public or commercial settings.

Facial Recognition Concerns

The company has faced criticism of its ‘Rekognition’ system which is used by police forces and has been pitched to agencies such as US Immigration and Customs Enforcement (ICE).

In a letter addressed to Amazon CEO Jeff Bezos, employees wrote:

“We refuse to build the platform that powers ICE, and we refuse to contribute to tools that violate human rights.

As ethically concerned Amazonians, we demand a choice in what we build and a say in how it is used.”

The letter was sent following ICE’s separation of immigrant children from their families at the US border and subsequent detainment. There’s no evidence ICE ultimately purchased or used Amazon’s technology.

In July last year, the American Civil Liberties Union tested Amazon’s facial recognition technology on members of Congress to see if they match with a database of criminal mugshots.

Rekognition compared pictures of all members of the House and Senate against 25,000 arrest photos. The false matches disproportionately affected members of the Congressional Black Caucus.

Dr Matt Wood, General Manager of AI at Amazon Web Services, commented on the ACLU’s findings later that month. He said the ACLU left Rekognition’s default confidence setting of 80 percent on when it suggests 95 percent or higher for law enforcement.

Wood, however, went on to say it showed how standards are needed to ensure facial recognition systems are used properly. He called for “the government to weigh in and specify what temperature (or confidence levels) it wants law enforcement agencies to meet to assist in their public safety work.”

The call for facial recognition standards extends beyond the US. In China, the CEO of SenseTime – the world’s most funded AI startup – also said he wants to see facial recognition standards established for a ‘healthier’ industry.

In the UK, Information Commissioner Elizabeth Denham announced her office has identified facial recognition technology as a priority to establish what protections are needed for the public.

SenseTime is so well-funded not just because of its powerful facial recognition technology, but also from adoption by the Chinese government. The firm aims to process and analyse over 100,000 simultaneous real-time streams from traffic cameras, ATMs, and more as part of its ‘Viper’ system.

If such a system was deployed with biased algorithms, it will exacerbate current societal problems. Algorithmic Justice League founder Joy Buolamwini gave a fantastic presentation during the World Economic Forum last month on the need to fight AI bias.

As Spider-Man’s Uncle Ben would say: “With great power, comes great responsibility”.

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