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Ethics and Errors of Facial Recognition Technology



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.

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Daasity builds ELT+ for Commerce on the Snowflake Data Cloud



Cloud Computing News

Modular data platform Daasity has launched ELT+ for Commerce, Powered by Snowflake.

It is thought ELT+ for Commerce will benefit customers by enabling consumer brands selling via eCommerce, Amazon, retail, and/or wholesale to implement a full or partial data and analytics stack. 

Dan LeBlanc, Daasity co-founder and CEO, said: “Brands using Daasity and Snowflake can rapidly implement a customisable data stack that benefits from Snowflake’s dynamic workload scaling and Secure Data Sharing features.

“Additionally, customers can leverage Daasity features such as the Test Warehouse, which enables merchants to create a duplicate warehouse in one click and test code in a non-production environment. Our goal is to make brands, particularly those at the enterprise level, truly data-driven organisations.”

Building its solution on Snowflake has allowed Daasity to leverage Snowflake’s single, integrated platform to help joint customers extract, load, transform, analyse, and operationalise their data. With Daasity, brands only need one platform that includes Snowflake to manage their entire data environment.

Scott Schilling, senior director of global partner development at Snowflake, said: “Daasity’s ELT+ for Commerce, Powered by Snowflake, will offer our joint customers a way to build a single source of truth around their data, which is transformative for businesses pursuing innovation.

“As Snowflake continues to make strides in mobilising the world’s data, partners like Daasity give our customers flexibility around how they build data solutions and leverage data across the organisation.” 

Daasity enables omnichannel consumer brands to be data-driven. Built by analysts and engineers, the Daasity platform supports the varied data architecture, analytics, and reporting needs of consumer brands selling via eCommerce, Amazon, retail, and wholesale. Using Daasity, teams across the organisation get a centralised and normalised view of all their data, regardless of the tools in their tech stack and how their future data needs may change. 

ELT stands for Extract, Load, Transform, meaning customers can extract data from various sources, load the data into Snowflake, and transform the data into actions that marketers can pursue. For more information about Daasity, our 60+ integrations, and how the platform drives more profitable growth for 1600+ brands, visit us at

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4 Activities that Automakers Can Digitize Now



4 Activities that Automakers Can Digitize Now

Digital automaking is supported by technology-driven trends, consumer needs and new developments in artificial intelligence.

Manufacturing, procurement of raw materials, marketing and sales are factors involved in this change.

Digital automaking is a process that combines simulation, three-dimensional visualizations, analytics and several tool partnerships to make automotive manufacturing easier. Since the automotive industry has been undergoing a digital transformation primarily driven by intelligent mobility, it has encouraged the market to adopt new technology and software for modern vehicles. There has also been a growing need to increase industrial processes’ sustainability, environmental friendliness and adaptability. All of this has made automotive digitalization extremely important.

Automotive digitization helps to keep precise control over business operations, which is made possible using modern technologies like ML (machine learning) and AI (artificial intelligence) to improve short- and long-term performance. 

Automotive digitization has also increased the capacity to monitor each component of the supply chain while lowering costs and risks. Digital automaking can offer automotive solutions in terms of better design, time efficiency, and many other industry solutions.

4 Activities that Can Be Digitized by Automakers Now


1. Manufacturing

Customers desire tailored goods, but they don’t want to pay more than they would for items that are mass-produced. As a result, manufacturing must be more adaptable than ever, leading to mass customization. Thus, the design, fabrication, use and maintenance of products are changing as a result of the digitalization of manufacturing. It is also changing the operations, procedures and energy footprint of supply chains and more. Digital manufacturing enables firms to provide additional options that are tailored to individual customers. Businesses can better understand supply-chain challenges, including inventory levels, delivery status and demand cycles, thanks to digital manufacturing. 

The factories of the future will move from automation to autonomy, strengthening real-time communication between equipment, physical systems, and people. These factories are referred to as smart factories. The most notable advantages of a smart factory are its shop floor connectivity, advanced robotics, flexible automation, augmented and virtual reality systems, and efficient energy management. The general manufacturing sector’s global standards are established by the automotive industry.

Over the past two decades, the automotive sector has expanded tremendously. However, the main elements that will affect whether digitalization is successfully implemented are the significance of realizing a return on investment (RoI) and the willingness of employees at both the top-most and lowest levels of an organization.

2. Supply Chain

By removing the functional barriers that divide different areas, the digitization of the supply chain is a cross-functional process that spans the entire lifecycle of a vehicle or product and involves all company divisions. It allows for an ecosystem that connects all stakeholders, from raw material and component suppliers to logistics companies, dealers and customers.

Utilizing digital technology throughout the entire supply chain allows for real-time monitoring of all supply-chain stages, be it either procurement of raw materials or finished products ready to be delivered or purchased. The evaluation and management of each event’s impacts on the supply chain can help the automation of procedures and the avoidance of potential interruption.

3. Design

Design plays a significant role in the automotive industry. By digitizing design activity, design professionals can test multiple hypotheses before proceeding with the design phase. Digitalization in the designing of products has been enabled by a digital model known as Digital Twins, which represents tangible assets in 3D. Digital twins mirror the complete car or one of its components’ appearance and behavior. With great assistance from sophisticated software, businesses can collect information about configuration, sensors, inspection data, and other details to improve the product’s design.

Automobile manufacturers are among the many industrial firms that recognize digital twins’ possibilities and the potential it has to bring in the best in the business of automobiles. The design and production processes are simplified by 3D representations, improving vehicle performance and cutting costs for the manufacturers. The twin technology is quickly rising to the top of the list of software solutions used in contemporary auto manufacturing, with applications ranging from car design to predictive maintenance to boosting sales using digitally generated models.

4. Marketing

Any marketing strategy aims to tailor the right message to the right set of audiences at the right time. A marketing campaign that appeals to a 45-year-old countryside man might not affect a 23-year-old lady residing in an urban area. Therefore, the impact of marketing combined with the effectiveness of Artificial Intelligence (AI) can be the biggest boon to any business. The automotive industry can enormously benefit in how they market their brand/product by adding the power of artificial intelligence to their current data. It can lead to a strong possibility of purchasing your products early in the sales process, possibly before customers even begin looking for their new car, which is indicated by specific online activities. 

As a result of recent advancements in third-party cookies and mobile advertising identifiers, AI can now assist brands in finding new prospects much more quickly by utilizing data to identify customers with similar characteristics and behaviors. This strategy can potentially increase your prospective customer base and give you an advantage over your competitors. You can identify high-priority targets by identifying the demographic categories that overlap. These solutions don’t require cookies and are more likely to comply with escalating privacy requirements because they rely on behaviors rather than personal data.

The automotive sector has modified its strategy and is now embracing digitization. Digital transformation in the automotive industry still has a lot of gaps to be addressed, but the trend toward digitization is a sign that the stakeholders in the automotive sector will be properly supplied with digital solutions in the coming days. With intelligent technology, and operations across the entire company and all departments, including manufacturing, supply chain, marketing, and sales, digital automaking will help the automotive industry to flourish in this digital era. An increasingly digital supply chain will also dismantle established barriers and greatly enhance communication. Undoubtedly, businesses must adopt a more significant digital transformation to be ready in this competitive automotive industry.

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The 10 Worst Cybersecurity Strategies You Need To Know



The 10 Worst Cybersecurity Strategies You Need To Know

Employees should be trained on basic cybersecurity practices and the dangers of phishing scams.

Granting too many privileges to user accounts can lead to security breaches. Failing to update software on time can leave vulnerabilities open to attacks. 

Organizations should have a disaster recovery plan in place to ensure quick recovery in the event of a cyberattack.

Counting down to the absolutely worst cybersecurity strategies. 

Sadly, these are all prevalent in the industry. Many organizations have failed spectacularly simply because they chose to follow a long-term path that leads to disaster. You know who you are…

Let’s count them down.  

10. Cyber-Insurance

No need for security, just get insurance. Transferring risk is better than mitigating it!

Famous Last Words: Sure, it should be covered

9. Audit Confidence

Conducting a comprehensive security audit. …and ignoring the results

Famous Last Words: We will close those gaps later…

8. Best Tools, Left Unmanaged

Deploying several good tools, set to autopilot. No need to manage or maintain anything 

Famous Last Words: Security is not that difficult…

7. Regulatory Compliance

Meeting the minimum requirements (defined 2 years ago)

Famous Last Words: Relax, we are compliant!

6. One Good Tool

We just need one good tool (ex. AV) and we are set. 

Famous Last Words: That should do it.

5. IT Dependence  

Cybersecurity is a tech problem, it’s IT’s responsibility. 

Famous Last Words: The IT dept has it covered.

4. Security by Marketing  

Believing the snake-oil (deceptive marketing) salesperson that will ‘solve‘ your security problems

Famous Last Words: We are totally protected now! (or similar derivative from the sales brochure)

3. Default Security Settings  

Products and services come with security built in! 

Famous Last Words: It’s new, shiny, and looks secure. Don’t worry, we should be fine!

2. Security by Obscurity

Nobody knows or cares about us. We are too small to be targeted.

Famous Last Words: We haven’t been attacked yet…

1. Hope, as a Strategy

I hope we don’t get attacked. Let’s move on with more important things.

Famous Last Words: <meek inner voice>> Just don’t think about security because it is too scary, expensive, and complex!


This is the menu that evokes anger, frustration, and pity among cybersecurity professionals around the globe. Eventually it always ends in despair, blame, and a side of tears.

A solid long-term strategic plan is a necessity for an efficient and capable cybersecurity capability. Cybersecurity fails without a proper strategy. 

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