TECHNOLOGY

Ethics and Errors of Facial Recognition Technology

Published

2 years ago

May 24, 2022

The sheer potential of facial recognition technology in various fields is almost unimaginable.

However, certain errors that commonly creep into its functionality and a few ethical considerations need to be addressed before its most elaborate applications can be realized.

An accurate facial recognition system uses biometrics to map facial features from a photograph or video. It compares the information with a database of known faces to find a match. Facial recognition can help verify a person’s identity, but it also raises privacy issues.

A few decades back, we could not have predicted that facial recognition would go on to become a near-indispensable part of our lives in the future. From unlocking your smartphone to making a digital transaction for an online (or offline) purchase, the technology is well and truly ingrained in our daily life today. An incredible application of AI’s computer vision and machine learning components, facial recognition systems work in the following way: trained algorithms determine the various distinctive details in a person’s face, such as the number of pixels that can fit between their eyes or the curvature of their lips, amongst other details interpreted logically to recreate the face within the system. This recreation is then compared with a wide array of faces stored in the system database. If the algorithms detect that the recreation mathematically matches a face present in the database, then the system ‘recognizes’ it and carries out the user’s task.

Apart from executing this entire exercise in a matter of nanoseconds, today’s facial recognition systems can do their job competently even in poor lighting, image resolution, and angle of view.

Like other AI-powered technologies, facial recognition systems need to follow a few ethical principles while being used for various purposes. These regulations include:

1. Impartiality in Facial Recognition

Firstly, a facial recognition device must be developed in a way that the system completely prevents, or at least minimizes, bias against any person or group based on their race, gender, facial features, deformities or other aspects. Now, it is well documented that facial recognition systems cannot be 100% fair in their operations. Therefore, companies that build the systems supporting this technology generally spend hundreds of hours eliminating all traces of bias found in them.

Reputed organizations such as Microsoft generally employ qualified experts from as many ethnic communities as possible. During the research, development, testing, and design phase of their facial recognition systems, the diversity allows them to create massive datasets to train the AI, data models. While the huge datasets reduce the bias quotient, the diversity is symbolic too. The selection of individuals from all over the world is useful to reflect the diversity found in the real world.

Organizations must travel the extra mile to remove bias from facial recognition systems. To achieve this, the datasets used for machine learning and labeling must be diversified. More than anything, a fair facial recognition system will be incredibly high on output quality as it will work seamlessly in any part of the world without an element of bias.

To ensure fairness in a facial recognition system, developers can also involve end customers during the beta testing phase. Testing the competence of such a system in a real-world scenario will only enhance the quality of its functionality.

2. Openness Regarding AI’s Internal Workings

Organizations that incorporate facial recognition systems in their workplaces and cybersecurity systems need to have all the details about where the machine learning information is stored. Such organizations need to understand the limitations and capabilities of the technology before implementing it in their daily operations. The company which provides AI-based technology must be completely transparent with their clients regarding these details. Additionally, the service provider must also ensure that their facial recognition system can be used by customers from any location based on their convenience. Any updates in the system must be made only after receiving valid approval from the client.

3. Accountability Towards Stakeholders

As specified earlier, facial recognition systems are deployed in several sectors. Organizations that manufacture such systems must provide accountability for them, especially in cases where the technology could directly impact any person or group (law enforcement, surveillance). Accountability in such systems means the inclusion of use cases to prevent physical or health-based injuries, financial embezzlement or other issues that may be caused by the system. To bring an element of control into the process, a qualified individual is put in charge of the system in organizations to make measured and logical decisions. Apart from this, organizations that incorporate facial recognition systems in their daily operations must resolve customer grievances related to the technology on an immediate basis.

4. Consent and Notice Prior to Monitoring

Under normal circumstances, a facial recognition system must not be used to snoop on individuals, groups or otherwise without their consent. Certain bodies, such as the European Union (EU), have a standardized set of laws (GDPR) to prevent unauthorized organizations from monitoring individuals within the governing body’s jurisdiction. Organizations possessing such systems must comply with all the data protection and privacy laws of the land.

5. Lawful Surveillance to Avoid Human Rights Violation

Unless authorized for the same by a national government or decisive governing body for purposes related to national security or other high-profile situations, an organization cannot use a facial recognition system to monitor any person or group. Basically, the technology is strictly prohibited from being used to violate the victim’s human rights and freedom.

Despite being programmed to follow these regulations without any exception, facial recognition systems can cause problems due to errors in their operations. Some of the main problems related to the technology are:

6. Verification Errors while Making Purchases

As specified earlier, facial recognition systems are incorporated in digital payment applications so that users can verify transactions with the technology. Criminal activities such as facial identity theft and debit card fraud are quite possible with the presence of this technology for the purpose of payments. Customers opt for facial recognition systems for the purpose because of the sheer convenience it offers for users. However, an error that can take place in such systems is when identical twins use them to make unauthorized payments from each other’s bank accounts. Worryingly, duplication of faces allows financial embezzlement despite the security protocols present in facial recognition systems.

7. Inaccuracies in Law Enforcement Applications

Facial recognition systems are used to identify criminals out in the open before capturing them. While the technology is undeniably useful as a concept in law enforcement, there are some glaring issues in its working. There are a few ways in which criminals can abuse this technology. For example, the biased AI concept provides inaccurate results to law enforcement officers as, on occasions, the systems cannot distinguish between men of color. Generally, such systems are trained with datasets containing images of white men. As a result, the system’s workings are error-ridden when it comes to identifying people from other ethnicities.

There have been several instances wherein organizations or public bodies have been accused of unlawful surveillance of civilians with advanced facial recognition systems. The video data collected by continuously monitoring individuals can be used for several devious purposes. One of the biggest complaints with facial recognition systems is the generalized output it provides. For instance, if an individual is suspected to have committed a felony, their picture is taken and run alongside the pictures of several criminals to check whether the individual had any criminal record or not. However, the stacking of data together means that the facial recognition database maintains the picture of the man alongside seasoned felons. So, despite the individual’s relative innocence, his or her privacy is invaded. Secondly, the person may be seen in a bad light despite being, by all accounts, innocent.

As we can see, the main issues and errors related to facial recognition technology stem from a lack of advancement in technology, a lack of diversity in datasets, and inefficient handling of the system by organizations. In my opinion, AI and its applications have infinite scope for application in real-world requirements. The risks around facial recognition technology typically take place when the technology works in the same way it is supposed to work despite differences in real-world requirements.

It can be expected that, with further technological advancements in the future, the problems related to the technology will be ironed out. The problems related to bias in AI’s algorithms will eventually be eliminated. However, for the technology to work perfectly without any ethical breaches, organizations will have to maintain a strict level of governance over such systems. With a greater degree of governance, the facial recognition system’s errors can be resolved in the future. As a result, improvements in the research, development, and design of such systems must be carried out to achieve positive solutions.

Up Next

WinX Video Converter – The Ultimate Solution to Video Playback Errors

Don't Miss

Multi-access edge computing spend to reach $23 billion globally by 2027

Click to comment

You must be logged in to post a comment Login

TECHNOLOGY

Next-gen chips, Amazon Q, and speedy S3

Published

5 months ago

December 1, 2023

Max

AWS re:Invent, which has been taking place from November 27 and runs to December 1, has had its usual plethora of announcements: a total of 21 at time of print.

Perhaps not surprisingly, given the huge potential impact of generative AI – ChatGPT officially turns one year old today – a lot of focus has been on the AI side for AWS’ announcements, including a major partnership inked with NVIDIA across infrastructure, software, and services.

Yet there has been plenty more announced at the Las Vegas jamboree besides. Here, CloudTech rounds up the best of the rest:

Next-generation chips

This was the other major AI-focused announcement at re:Invent: the launch of two new chips, AWS Graviton4 and AWS Trainium2, for training and running AI and machine learning (ML) models, among other customer workloads. Graviton4 shapes up against its predecessor with 30% better compute performance, 50% more cores and 75% more memory bandwidth, while Trainium2 delivers up to four times faster training than before and will be able to be deployed in EC2 UltraClusters of up to 100,000 chips.

The EC2 UltraClusters are designed to ‘deliver the highest performance, most energy efficient AI model training infrastructure in the cloud’, as AWS puts it. With it, customers will be able to train large language models in ‘a fraction of the time’, as well as double energy efficiency.

As ever, AWS offers customers who are already utilising these tools. Databricks, Epic and SAP are among the companies cited as using the new AWS-designed chips.

Zero-ETL integrations

AWS announced new Amazon Aurora PostgreSQL, Amazon DynamoDB, and Amazon Relational Database Services (Amazon RDS) for MySQL integrations with Amazon Redshift, AWS’ cloud data warehouse. The zero-ETL integrations – eliminating the need to build ETL (extract, transform, load) data pipelines – make it easier to connect and analyse transactional data across various relational and non-relational databases in Amazon Redshift.

A simple example of how zero-ETL functions can be seen is in a hypothetical company which stores transactional data – time of transaction, items bought, where the transaction occurred – in a relational database, but use another analytics tool to analyse data in a non-relational database. To connect it all up, companies would previously have to construct ETL data pipelines which are a time and money sink.

The latest integrations “build on AWS’s zero-ETL foundation… so customers can quickly and easily connect all of their data, no matter where it lives,” the company said.

Amazon S3 Express One Zone

AWS announced the general availability of Amazon S3 Express One Zone, a new storage class purpose-built for customers’ most frequently-accessed data. Data access speed is up to 10 times faster and request costs up to 50% lower than standard S3. Companies can also opt to collocate their Amazon S3 Express One Zone data in the same availability zone as their compute resources.

Companies and partners who are using Amazon S3 Express One Zone include ChaosSearch, Cloudera, and Pinterest.

Amazon Q

A new product, and an interesting pivot, again with generative AI at its core. Amazon Q was announced as a ‘new type of generative AI-powered assistant’ which can be tailored to a customer’s business. “Customers can get fast, relevant answers to pressing questions, generate content, and take actions – all informed by a customer’s information repositories, code, and enterprise systems,” AWS added. The service also can assist companies building on AWS, as well as companies using AWS applications for business intelligence, contact centres, and supply chain management.

Customers cited as early adopters include Accenture, BMW and Wunderkind.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

TECHNOLOGY

HCLTech and Cisco create collaborative hybrid workplaces

Published

5 months ago

November 29, 2023

Max

Digital comms specialist Cisco and global tech firm HCLTech have teamed up to launch Meeting-Rooms-as-a-Service (MRaaS).

Available on a subscription model, this solution modernises legacy meeting rooms and enables users to join meetings from any meeting solution provider using Webex devices.

The MRaaS solution helps enterprises simplify the design, implementation and maintenance of integrated meeting rooms, enabling seamless collaboration for their globally distributed hybrid workforces.

Rakshit Ghura, senior VP and Global head of digital workplace services, HCLTech, said: “MRaaS combines our consulting and managed services expertise with Cisco’s proficiency in Webex devices to change the way employees conceptualise, organise and interact in a collaborative environment for a modern hybrid work model.

“The common vision of our partnership is to elevate the collaboration experience at work and drive productivity through modern meeting rooms.”

Alexandra Zagury, VP of partner managed and as-a-Service Sales at Cisco, said: “Our partnership with HCLTech helps our clients transform their offices through cost-effective managed services that support the ongoing evolution of workspaces.

“As we reimagine the modern office, we are making it easier to support collaboration and productivity among workers, whether they are in the office or elsewhere.”

Cisco’s Webex collaboration devices harness the power of artificial intelligence to offer intuitive, seamless collaboration experiences, enabling meeting rooms with smart features such as meeting zones, intelligent people framing, optimised attendee audio and background noise removal, among others.

Tags: Cisco, collaboration, HCLTech, Hybrid, meetings

TECHNOLOGY

Canonical releases low-touch private cloud MicroCloud

Published

5 months ago

November 28, 2023

Max

Canonical has announced the general availability of MicroCloud, a low-touch, open source cloud solution. MicroCloud is part of Canonical’s growing cloud infrastructure portfolio.

It is purpose-built for scalable clusters and edge deployments for all types of enterprises. It is designed with simplicity, security and automation in mind, minimising the time and effort to both deploy and maintain it. Conveniently, enterprise support for MicroCloud is offered as part of Canonical’s Ubuntu Pro subscription, with several support tiers available, and priced per node.

MicroClouds are optimised for repeatable and reliable remote deployments. A single command initiates the orchestration and clustering of various components with minimal involvement by the user, resulting in a fully functional cloud within minutes. This simplified deployment process significantly reduces the barrier to entry, putting a production-grade cloud at everyone’s fingertips.

Juan Manuel Ventura, head of architectures & technologies at Spindox, said: “Cloud computing is not only about technology, it’s the beating heart of any modern industrial transformation, driving agility and innovation. Our mission is to provide our customers with the most effective ways to innovate and bring value; having a complexity-free cloud infrastructure is one important piece of that puzzle. With MicroCloud, the focus shifts away from struggling with cloud operations to solving real business challenges” says

In addition to seamless deployment, MicroCloud prioritises security and ease of maintenance. All MicroCloud components are built with strict confinement for increased security, with over-the-air transactional updates that preserve data and roll back on errors automatically. Upgrades to newer versions are handled automatically and without downtime, with the mechanisms to hold or schedule them as needed.

With this approach, MicroCloud caters to both on-premise clouds but also edge deployments at remote locations, allowing organisations to use the same infrastructure primitives and services wherever they are needed. It is suitable for business-in-branch office locations or industrial use inside a factory, as well as distributed locations where the focus is on replicability and unattended operations.

Cedric Gegout, VP of product at Canonical, said: “As data becomes more distributed, the infrastructure has to follow. Cloud computing is now distributed, spanning across data centres, far and near edge computing appliances. MicroCloud is our answer to that.

“By packaging known infrastructure primitives in a portable and unattended way, we are delivering a simpler, more prescriptive cloud experience that makes zero-ops a reality for many Industries.“

MicroCloud’s lightweight architecture makes it usable on both commodity and high-end hardware, with several ways to further reduce its footprint depending on your workload needs. In addition to the standard Ubuntu Server or Desktop, MicroClouds can be run on Ubuntu Core – a lightweight OS optimised for the edge. With Ubuntu Core, MicroClouds are a perfect solution for far-edge locations with limited computing capabilities. Users can choose to run their workloads using Kubernetes or via system containers. System containers based on LXD behave similarly to traditional VMs but consume fewer resources while providing bare-metal performance.

Coupled with Canonical’s Ubuntu Pro + Support subscription, MicroCloud users can benefit from an enterprise-grade open source cloud solution that is fully supported and with better economics. An Ubuntu Pro subscription offers security maintenance for the broadest collection of open-source software available from a single vendor today. It covers over 30k packages with a consistent security maintenance commitment, and additional features such as kernel livepatch, systems management at scale, certified compliance and hardening profiles enabling easy adoption for enterprises. With per-node pricing and no hidden fees, customers can rest assured that their environment is secure and supported without the expensive price tag typically associated with cloud solutions.

Tags: automation, Canonical, MicroCloud, private cloud