Over two decades old, blockchain has become the actual foundation for mining, security, and the creation of cryptocurrency. It is dependable, trusted, and widely used for multiple forms of digital currency around the world.
DDoS Can Cripple a Blockchain
Merely due to its digital nature, blockchain is susceptible to attack and exploitation. One of the most dangerous threats to blockchain is distributed denial of service attacks.
However, even when vulnerabilities exist, networks and users can find ways to prevent harm to blockchain transactions and information.
As we discuss here — you will want to protect yourself and your organization proactively.
What is Blockchain and How Does This Technology Work?
While complicated, blockchain generally boils down to a specific type of database. This is a way to store information in blocks chained together. These blocks are chronological in order and increase as fresh data comes into the blockchain.
The blockchain type of data transaction has no central authority and provides group access through decentralization.
Decentralized blockchains are irreversible transactions, so once the data is within the database, it cannot be changed.
Blockchain is a trustworthy transaction, secure from outside sources, and moves quickly throughout various networks worldwide.
Unlike other forms of currency, there is no physical representation to a blockchain, as it is only data. However, it can also store the history of cryptocurrency transactions, legally binding contracts, and inventories of various products.
How Blockchain is Built-In Bitcoin Mining
Cryptocurrency mining occurs through a process with computers solving intricate mathematic problems.
The mining of bitcoin and other cryptocurrencies occurs through these processes, and transactions combine with similar ones and are then transmitted to all nodes. This enacts an update to the associated ledgers.
New currency is possible through rewards given once the computers solve the mathematical computations. The mining involved with bitcoin creates blocks of data with these transactions, which eventually create blockchains. These are large and long sequences of mined transactional data.
The nodes will confirm trusted data and verify the information within the blockchain. Through checks and balances with these processes, the blockchain can consistently maintain integrity. The inherent integrity in the system ensures trust in the bitcoin mined through the blockchain.
Why is Blockchain Considered the Base of Cryptocurrency “Unhackable?”
Blockchain utilizes multiple sources of defense against hackers to prevent attacks and to assist in reducing the costs of damage once a cyber threat like a DDoS attack occurs.
Blockchain was once considered unshakable because the data is immutable or irreversible once entered. In addition, it is decentralized with no individual authority such as a bank or government. Additional protocols also identify and report potential threats to the blockchain in use.
The decentralization specifically reduces various risks to the data and limits fees with transactions and processing of the data. Blockchain accomplishes this by spreading processing power over multiple computers in a network.
How a DDoS attack can Cripple Blockchain
Related to the DDoS attacks on EXMO, blockchain has some vulnerabilities regarding DDoS attacks. These include the following:
- Blockchain nodes: Blockchains exist on nodes that update with the latest data and are all connected.
- Smart contracts: Blockchain programs run through predetermined conditions met within the blockchain.
- Consensus mechanisms: There are three consensus mechanisms. The Proof of Work or PoW, the Proof of Stake or PoS. and the Delegated Proof of Stake or DPoS. These all generally confirm the deletion of equivalent data within the blockchain.
- Wallets: A digital wallet provides access to, storage, and management of cryptocurrency such as bitcoin.
The 51 percent attack happens when a hacker or other malicious user injects data with over 50 percent of the network processing power or hash rate. When the attacker does this, it is possible to overtake chains of data that do not go to the intended party.
Another side effect is the hacker can copy the data and add it to the chan. This then will delete previous information, so the block never saw it.
The perpetrator of the attack will use a DDoS to infiltrate some of these vulnerabilities and steal access to blockchain data and transactions such as bitcoin. For example, 51 percent of attacks (seba dot swiss) led to losses of over $1.1 million with Ethereum Classic in 2019, nearly $2 million with Verge in 2018, and another 2018 Verge attack with just over $1 million.
What is a DDoS Attack, and How Does it Target Blockchain and Make it Prone to Hacking?
Someone with malicious intent such as a hacker can flood a server, single network, or multiple networks with various requests or additional traffic leading to a Distributed Denial of Service or DDoS. Usually, the individual or group responsible wants to either slow the system or cause an entire collapse.
Once a DDoS starts on one computer, it will spread to others in the same network, leading to catastrophic failure.
The vulnerabilities of blockchain through nodes, contracts, or wallets can lead to overutilization of processing power within the server or network. The overutilization then causes a loss of connectivity with cryptocurrency exchanges or other applications connected at the time.
The perpetrator of these attacks can start by tracking IP addresses with specific locations around the world.
The DDoS attacks on EXMO led to the British servers going offline. This caused the website to go down and the servers’ inability to run during the aftermath of the attack. Additionally, hot wallets were compromised during this event, and the perpetrators withdrew five percent of all assets involved.
EXMO explained that they would cover all losses after suspending every withdrawal at the time. This led to new infrastructure development with a separate server for hot wallets.
What Does This Mean to the Crypto-Market?
The Crypto-Market often fluctuates. Based on word of mouth, the value changes, information that can help or harm the influence of various cryptocurrencies, and damage through DDoS attacks that can lead to financial losses. Because these malicious users can cause websites, servers, or networks to go offline for indeterminate amounts of time, the Crypto-Market can see dips in investment and reliability of financial transactions.
Often, after a DDoS attack, the blockchain development may change focus or utilize new techniques that decrease the possibility of vulnerabilities.
After a DDoS Attack, What is its Implication for the Crypto Market and Bitcoin Specifically?
Cryptocurrency markets grew from $19 billion to $602 billion from the beginning of 2017 to the end of 2017. Volume trade with these markets and even the negative effects of DDoS attacks are normally mitigated within the same day the damage occurs. However, malicious users can affect the market through Twitter feeds, news through Google Searches, and the status pages of the network.
How Does a DDoS Attack Affect the Bitcoin Ecosystem?
The trade of Bitcoin will fluctuate based on the downtime of the servers or websites associated with the cryptocurrency. In addition, offline websites affect the access to trades, the ability to purchase or sell, and access to Bitcoin.
Additionally, if someone influential says something through social media, the market can suffer a downtrend or an uptrend based on positive or negative reviews of Bitcoin. This generally leads to either more buying of the cryptocurrency or fewer purchases.
Once the market is affected by these trends, the prices will change. For example, mass-selling may occur after a DDoS attack if there are numerous users affected by a loss of financial transactions. This is even possible if the company behind the Bitcoin data reimburses users for these losses.
Can a Cyber-Attack Change the Market from Bull to Bear?
The general statistical trend of the crypto-market appears little affected by the negative effects of DDoS attacks.
Websites are normally back up and online within the same day. Trades, purchasing, and selling cryptocurrency are not usually severely impacted by most cyber-attacks. Bitcoin, in particular, has few patterns that explain the bull and bear rise and fall of prices.
However, multiple cyberattacks targeting one website, server, or network can lead to sustained losses for the company. The loss of faith in the downtime from the attack can lead to a bear market where losses are constant for a period of time.
What Cybersecurity Measures Should be Taken to Prevent a DDoS Attack?
To prevent similar DDoS attacks such as those that occurred on EXMO and other companies, you can put numerous cybersecurity measures in place.
Prevention is key. There are several ways to prevent DDoS and other cybersecurity attacks.
- Develop a Denial of Service plan by assessing security risks and what to do in case an attack ever occurs.
- Enhance network infrastructure security for multi-level protection protocols.
- Minimize user errors and security vulnerabilities.
- Develop a strong network architecture by focusing on redundant resources within the network and servers.
- Utilize the cloud to spread out the attack and use multiple environments to prevent damage within the system.
- Recognize common warning signs of DDoS attacks, such as increased traffic, intermittent connectivity, and a lack of standard performance.
- Consider investing in DDoS-as-a-Service, which can provide flexibility, third-party resources, and cloud or dedicated hosting on multiple types of servers at the same time.
It is imperative to identify and then respond to attacks in real-time.
By using checkboxes, captcha and other methods on the website, programs and users can discover if the activity is real or a potential threat. In addition, changing response times through automation, recognizing patterns of attack, and implementing defense systems can all provide measures of protection.
Automation of attack detection can reduce DDoS response time against the attack.
The automation method provides near-instant detection for incoming DDoS attacks.
When traffic spikes to untenable levels, automation can redirect traffic through an automated defense system. This system is usually adaptive and can employ various methods if the DDoS event is different from the previous attacks.
Automation can identify patterns in traffic by sifting through a large amount of data quickly. This can provide real-time solutions during the attack. The defense system of automation can also access IP blocklists and weapons to protect certain zones of information.
Automated defense systems provide adaptable solutions for the ever-evolving hacker intent on stealing data. With real-time updates and access to lists the company or user may not have, network administrators can implement strategies to prevent or mitigate the damage caused at the attack time.
New Trends in Protecting Against DDoS Attacks
A new cybersecurity trend involves awarding cryptocurrency to users that spot irregular activity and report the issue. Previous and continuing trends involve tracking the deviation in traffic. Some companies will use software to analyze answers to queries, determine if transactions are legitimate, and evaluate if processes are in line with true activity. This can single out bots or malicious traffic.
Pattern recognition is important when determining whether a DDoS attack is underway.
Companies can use machine learning technology to detect irregular patterns. For example, a query can help to determine which IP addresses, timeframes, or accounts are affecting the network during a DDoS attack.
Do this early — and well to guard against attacks.
Another trend to guard against DDoS attacks identifying IP addresses commonly associated with DDoS attacks and blocking them.
Some companies use forensic tools after a data breach or DDoS attack to determine how the attack occurred and how to respond to a similar one in the future. This may involve using programs and encrypted recorded logs to review later.
It is vital to stay alert to potential threats. By always being prepared for potential disasters, you should be able to prevent catastrophe.
Having a plan in place when the attack happens can limit response time to prevent the website or network from going offline.
To accomplish these goals, you can implement stronger cybersecurity measures and invest in resources that recognize various DDoS patterns and alert users immediately to take direct action. These proactive steps can help protect blockchain data and keep cryptocurrency from falling into malicious hands.
9 Expert Tips for Keeping Children Safe Online
The internet can be a valuable source of knowledge, education, and amusement for kids. At the same time, it can come with its own problems, such as inappropriate material, cyberbullying, and online harassment.
Some websites can be hazardous for children because they may be created to mislead them into giving away personal details or initiating malware downloads. Additionally, children may be exposed to cyberbullying or some other digital abuse.
Mobile apps may also be dangerous for children. For example, some apps offer anonymous messaging, allowing predators to contact kids easily. Other apps may have lax privacy controls, which could put children at risk of having their personal information shared without their consent. Still, other apps may be designed for adults but are attractive to children and may expose them to inappropriate content or adult themes.
As a parent, it is crucial to protect your kids from these potential dangers and help them stay safe online. In this article, I will provide tips and advice for keeping your children safe while using the internet.
1. Set online behavior rules and enforce them consistently
The first and most important step is to set clear rules. This might include rules about how much time children can spend online, the types of websites they can visit, and the types of personal information they are allowed to share. It is vital to make sure your kids understand the consequences of breaking these rules. You should always follow through with those consequences if necessary.
To enforce these rules, it can be helpful to establish a routine for your child’s internet use. This might involve setting specific times of day when they are allowed to go online and monitoring their activity to ensure they follow the rules. Always reward children for doing things right.
2. Encourage open and honest communication
- Ask your children about their online activities and interests and listen to what they have to say without judging or lecturing.
- Be available and approachable. Children should know that they can come to you at any time and speak openly about their online experiences. If your children come to you with a question or concern, take it seriously and help them find the information or support they need.
- Be a good role model. Show your children that you value open and honest communication by being open and honest with them yourself.
It is also essential to continue having regular conversations with your children about internet safety as they grow and their online habits and interests change.
3. Educate your children about internet safety
- Talk to your kids regularly about the potential risks of using the internet, such as cyberbullying, exposure to inappropriate content, and meeting strangers online.
- Explain the importance of protecting personal information, including name, phone number, address, and other identifying information.
- Explain to children that once something is posted online, it can be difficult or impossible to remove it completely.
- Remind them that online behavior can have real-life consequences and that they should always be respectful and considerate of others when posting online.
- Encourage your children to use privacy settings on their social media accounts and other online services to control who can see their posts and personal information.
When buying a new laptop or phone, explain how to use it securely. By educating your children, you help them make responsible decisions when it comes to their online activity.
4. Teach your kids to be skeptical of messages and content they receive online
Explain to your children that not everything they see online is true. People can easily create fake accounts, websites, and content. Encourage them to think critically about the information they get online. Ask them questions to help them evaluate the information they are seeing, such as:
- Who is the source of this information?
- Is there any evidence to support it?
- Are there any reasons why the information might be false or misleading?
Encourage them to speak up if kids receive a message or see content that makes them uncomfortable.
5. Help your children understand the potential consequences of cyberbullying
Explain to your children what cyberbullying is and why it is wrong. Children should always speak up if they or someone they know are being bullied. Help them understand the importance of being an upstander, not a bystander.
Cyberbullying can have severe consequences for both the victim and the person doing the bullying. For example, the victim may experience anxiety, depression, or other mental health issues, and the person doing the bullying may face legal consequences, such as criminal charges or civil lawsuits.
6. Monitor your kids’ online activity
Parents should watch their children when they use the computer or phone. It is important to occasionally check in with them and see what they are watching or typing, and consider their mood and reaction.
For example, when making dinner for my youngest son, I let him play games for 45 minutes, but I observe his internet activity from the kitchen. Even though most games are harmless, they can still cause anger and frustration. This is not shoulder surfing but taking proper precautions.
It is good to occasionally check the browsing history on children’s devices to see which websites they have visited. You can also monitor your kids’ social media accounts to see what they are posting and who they are interacting with. It is also good to review all installed apps.
Many parents do not know what their children are doing online. By monitoring your kids’ online activity, you can stay informed about what they are doing and take steps to keep them safe.
7. Use privacy settings
Use the “Privacy” settings in social media accounts to control who can see your kids’ posts, who can see their profile information, and who can send them friend requests. You can also block specific people. As mentioned above, you should educate your children about the importance of privacy settings and encourage kids to use them themselves.
8. Consider using parental control software
Parental control apps allow parents to set internet usage limits, block access to inappropriate websites and apps, and monitor kids’ online activity in real-time. By configuring the settings properly (celltrackingapps dotcom; parental controls), you can protect your child from plenty of potential risks.
Research different parental control software to find one that meets your needs. Look for an app that offers the features you need. Most solutions require you to install the app on your child’s device in order to monitor their activity.
As your children grow, you may need to adjust the settings on the parental control app to continue protecting them effectively. Be sure to review the app settings regularly.
9. Stay up to date on the latest trends and platforms to understand the digital landscape your child is navigating
- Read articles and blogs about internet safety and parenting.
- Consider attending workshops and seminars on internet safety. Many organizations and schools offer such events.
- Reach out to other parents and experts in the field of internet safety and parenting to learn more about the latest trends and platforms in the digital landscape. They can provide valuable insights and advice based on their experiences and expertise.
Keeping children safe while they are using the internet is an important responsibility. By setting online behavior rules, monitoring your kids’ online activity, educating them about internet safety, and taking advantage of parental control apps, you protect your children from many risks and ensure they have a positive and safe experience.
Stay involved in your children’s online activities and continue to have open and honest conversations to help them make responsible decisions while using the internet.
Featured Image Credit: Jessica Lewis; Pexels; Thank you!
How Machine Learning is Solving Fraud Detection in Finance
You will find financial apps on a majority of smartphones. We often reach our phones to make online banking transactions or check our bank balances. Mobile apps are transforming how we used to perform financial activities, bringing convenience and flexibility to people’s lives. Banks offer nearly every possible service a user wants directly to their mobile apps. Henceforth, the need to visit the bank has reduced significantly, and there are so many customers who have not visited a bank in months or even years.
Digital Platforms in Finance
However, the injection of digital platforms in the finance industry also packs numerous fraud activities that the finance industry has to solve for users. A key solution to financial fraud detection and prevention is Machine learning.
Machine learning in Finance is actively acting as a defender against criminal activities that target acquiring money through deception. As a business, financial institutions are building robust solutions to deliver optimum security to their customers, and machine learning plays a key role in the process. As a result, mobile app developers actively integrate numerous algorithms and explicit programming to make their apps fraud free for financial institutions.
By 2026, the global Fraud detection and prevention market size is projected to reach $65.8 Billion. Additionally, the CAGR of the same will be 21.5%, where Machine learning will play a key role in making fraud-free finance apps. The question of how machine learning prevents fraud stays a curiosity for entrepreneurs. So, this article will explore the following:
- What are the major types of activities Machine learning is preventing
- How important is machine learning for financial fraud prevention?
- What are machine learning models used in fraud prevention?
- Real live cases of fraud prevention in Finance mobile apps
With the above concept, you will have the necessary knowledge required to understand fraud detection in finance apps through machine learning.
What are the major types of frauds machine learning can prevent in finance apps?
Machine learning is rapidly evolving, and there are no limitations to its growth. The scope of fraud detection and prevention covers a range of activities fraudsters do; money laundering, insurance frauds, phishing scams, and more. Here are some of the frequent financial types that machine learning is rapidly trying to solve:
Email Phishing Fraud
Some people rely on their email for professional and personal usage. With this wide usage and reliability of people on emails, fraudsters execute email phishing frauds through multiple methods. When a cybercriminal sends spam or malware in an email, it is known as Email Phishing Fraud. 96% of phishing attacks come through Email.
Such emails may contain the following:
- Unsafe links
- Request banking otps (one-time passcodes)
- Net banking passwords
Automated methods with machine learning allow the system to detect phishing attempts in advance to alert the users. A great example of the same is Gmail. The algorithms detect emails and send them to spam.
Credit Card Fraud
The cashless economy is boosting the use of debit and credit cards, where a credit card has become a primary source of shopping for users. And stealing information on credit cards is becoming an immensely popular fraud among users. The victim can notice unrecognized purchases on behalf of their credit card. Thousands of cases are filed yearly mentioning credit card fraud. Machine learning can act as a layer of security to identify if an authorized party performs the transaction or not.
Mobile Phone Fraud
Mobile phones are prone to cyber-attacks. With the NFC technology in smartphones, hackers can infiltrate your private data and steal information. Machine learning can prevent wifi based transactions by predetermining unauthorized access. Further, smart technologies like biometric-based security can prevent Mobile phone-based fraud.
Websites have sensitive credentials of users. Besides, if a hacker steals sensitive information, they can pretend to be you and can initiate transactions on behalf of your identity. Additionally, users also use social platforms to sign in to multiple platforms with one click. As a result, machine learning can deliver a secure process to prevent misidentification in financial institutions.
How important is machine learning for financial fraud prevention?
Implementing machine learning brings multiple benefits to fraud detection in the finance industry. Here are the notable benefits that Machine learning offers:
Fast Data Collection
Today data is an asset for businesses. When financial institutions gather data, especially from the victims of fraud activities, they understand patterns to identify the cause of fraud. As a result, ML can collect relevant data, prepare categories and deliver insightful actions to alert or prevent fraud activities.
High Level of Efficiency
ML can execute endless programs/functions without any errors. It can easily manage massive data sets and find the source of fraudulent activities without any compromise in efficiency or hampering organizations’ time and efforts. Additionally, it also saves costs for the company.
Financial institutions can process as much data as they want with big data technology capabilities. Eventually, ML can differentiate even the slightest difference in the data sets to deliver optimum actions to the users. As a result, when financial institutions can make use of massive data sets, it increases their scalability.
Innovative and Rapid Improvement
A great benefit of building machine learning solutions is that you can modify them according to your needs at any time. It is never a one-time investment. Further, you can keep innovating your machine learning algorithms to inject the latest trends used by fraudsters to improve your fraud detection approach rapidly.
What are machine learning models used in fraud prevention?
Machine learning technology is continuously evolving, yet some highly robust ML models are useful for the finance industry in preventing fraud.
Supervised learning is one of the finance industry’s most popular machine learning approaches. The supervised learning model is based on predictive data analysis and database management, working on data arriving from multiple sources. Additionally, the data is labeled and classified into multiple categories. For fraud detection, supervised learning can differentiate between two similar data sets to train the machine learning algorithm.
Unsupervised learning plays a key role in fraud detection when there is very little information available to analyze. It continuously monitors and learns from the pieces of information in multiple time stamps and variations. Finally, when the algorithm has collected sufficient data, it initiates action.
Semi-supervised learning combines both, Supervised and unsupervised learning patterns to produce optimum results. The learning model automatically switches to semi-supervised or supervised learning according to the requirements.
How PI (fraud detection solution) by PayTm works using Machine Learning?
Pi is a finance fraud detection model built by PayTm to prevent fraud for businesses and users. (Pi is the PI.Exchange), and its smart model highly utilizes a machine learning approach to deliver optimum risk prevention to users.
This is how Pi works:
- Detect: Intelligent risk scoring with a personalized approach. Every user has their own risk scores for multiple finance-related activities.
- Diagnose: Using an unsupervised machine learning approach, the fraud detection model actively monitors activities 24/7, recommending user rule sets and alerts.
PI uses high-level intelligent algorithms to give a strong fraud detection tool:
- Data ingestion
- Anomaly Detection
- Dynamic risk scoring
- Machine learning studio
- Smart Simulations
- Case management
PI is a perfect real-life example of a fraud detection tool for the finance sector showcasing efficient use of the machine learning approach.
Want to integrate fraud detection & prevention ML algorithms in your finance app?
If you want to build a finance app or want to increase the security of your finance app, an IT Solution provider can deliver an optimum fraud detection and prevention solution for your app. Machine learning and artificial intelligence (trickyenough dotcom — integrate ai in mobile) experts are passionate about integrating intelligent solutions aligning with your finance apps. These experts are extremely prominent in supervised, unsupervised, and semi-supervised machine learning algorithms according to your app development demands.
Image Credit: Provided by the Author; Thank you!
The Power of Human Touch in Software Development and AI Environments
Necessity is the mother of invention — a principle that has defined technological advancement and design for generations. We innovate and create new products, services, and processes based on human needs that arise. Just as these needs evolve with each generation, so too must the way we approach the innovation required to address them, and that requires a human touch in software development.
Digitalization Driving Productivity and Collaboration
In today’s era of digitalization, companies are turning to new technologies, specifically software applications, to drive efficiency, productivity, lowered costs, and smarter collaboration. As the adoption of digital transformation has increased, so has the number of software solutions enabling and supporting it.
Companies are looking to software developers for new ways to integrate technology. Operations demand process automation, improved communications, and the delivery of more value to their customers.
Artificial Intelligence (AI) has been an influential part of this progression and an enabler for many capabilities today; however, we must exercise restraint in relying too much on technology alone to reach business goals.
Restraint With AI Makes Better Sense Today
There’s power in the human touch that can set the stage for meaningful transformation when embraced during the software development process. As we continue to integrate digital technology into nearly every facet of work and life, we need intelligent software design to incorporate more of the human experience to thrive.
The key is finding a measured balance between purpose-driven software design and human touch and understanding how to execute this collaboration successfully and ethically.
AI’s Role in the Consumerization of Software
Historically, enterprise software was initially designed with a particular persona and a very specific set of requirements in mind. As a result, it was built to serve simply as a tool with no focus on the user’s experience, behavior, or even the potential ability of the user to change behaviors.
For example, if a person were using a software program to search for fruit, that software would need to be coded to show each specific fruit. Then, if a user searched for a peach, the software would display a peach. If they wanted to search for an orange instead, the software would need to be hard-coded to show an orange. This is a very cut-and-dry process leaves little room for interpretation.
Software Designed to Consider User’s Behavior
Today, AI-powered software is designed with realistic human values in mind, defining a set of user stories and use cases that proactively adjust to potential user behavior, with a strict focus on providing and maximizing end-user value and experience. Explained differently, rather than designing software around who a user is or what they do, the software is designed to consider the user’s behavior and the value the product can provide for an optimum user experience.
Revisiting the example above, if a user in Florida is seeking “fruit,” the initial result the software is likely to show them would be an orange. Alternatively, if the user lives in Georgia, it would likely show them a peach — since these fruits are indigenous to their respective regions.
Software Built With Human Touch Provides an Experience
In this example, the software was built to look for an outcome and design an experience that considers several factors to present the most likely desired result. This is how AI algorithms are used to design an environment meant to improve the customer experience by anticipating needs based on trends. This is also how software developers automate an experience.
Several different inputs are analyzed to the point where an assumption can be made based on a person’s behavior over time. Content is then populated, or an action taken that the program deems would be most useful for that person, all in the name of improving the user experience. But what if the person in Florida doesn’t like oranges and the person in Georgia doesn’t like peaches? This is where the human touch comes into play
Finding the Ethical Balance Between Smart Personalization and Human Privacy
AI algorithms require and collect personal user data to customize the experience in a unique and valuable way for a specific user. However, they can sometimes be too rigid when analyzing only the data without considering personal preference, free will, or choice.
AI alone, without any oversight, can also go too far, as there’s a fine line between personalization and privacy, and AI can’t make the distinction on its own. A human developer addresses this issue by providing the guardrails necessary to ensure ethical design and desired behavior while also incorporating the flexibility needed to give users a greater sense of choice.
How Do You Protect Your Customers?
Software must be built so the user can determine what data they want to provide to improve their life or minimize evasiveness. On the front end, as software is being designed, developers can build flexible architecture with certain constraints in mind to provide an ethical and secure framework around how the AI algorithm works. This allows users to define where the line is or how much personal data they allow into the architecture or the cloud.
Ultimately, users want AI to optimize the tasks they need to perform but don’t want the AI to make decisions for them without approval. This idea of choice is another critical element of why the human touch is so imperative in the development process, even as the industry has sought faster and more efficient ways of writing software programs.
The Artistry of Software Engineering From the Human Touch
As a means of automating code writing, code generators were introduced several decades ago to quickly write new code and get applications to market faster. Requiring minimal human intervention, the developer would simply set what it needed the app to do, and then code was generated for that specific purpose. It was easy to write code that would solve a particular problem and take the user from point A to point B in the most efficient way possible.
However, the lack of human touch is the issue with this model. There’s no consideration for the user experience or innovation that excites and delights; the software is simply working to solve the problem without room for interpretation. Alternatively, the human coder can push boundaries and determine how to interpret the user requirements to decide the best path between the two points.
Flexible Code Environments Make Efficient Programming
For example, if a program were coded to simply draw a tree, a code generator would produce the same tree over and over with no differentiation in color, leaf type, size, etc. There would be no artistry behind it, and users wouldn’t have the ability to choose from various tree designs. Instead, they’d have to accept the tree the generator produced. But when an engineer has the ability to integrate their personal touch, the artistry of software design is unveiled, and the experience becomes something very different.
Today, our industry has evolved even further, and low-code/no-code environments have emerged as the next generation of modern and efficient programming. Developers can now quickly build the foundation of a program from existing services and functions that have already been written by other coders, easily defining the path the user takes to get from point A to B.
This type of coding is a much more flexible way of designing software that allows engineers to adopt the preferences of the end-user and change functions as the end user changes. However, the various functions are still being coded by an individual and, thus, are constrained to the preferences of that person.
Reusable Templates Boost Productivity
So, to revisit the previous “tree” example, software developer A designs a single tree which developer B can now take and, in about the same amount of time as it took to create just one original tree, reuses as a copy over and over but in different ways.
Several copies can be placed side by side to depict a forest, or a single copy can be placed in front of an abandoned building or on a deserted island to create different applications entirely. However, because they’re all using the same original tree design, all of the trees in each environment still look the same — the artistry is limited to that of the person who originally designed the tree.
Decide and Improve the End Product to Customize
Where the human element comes into play is when the software engineer can have the ability to decide how they want their tree to look and can add features along the entire design process to augment it.
Then, the next engineer can come in and change or improve it to meet the needs of their users, and so on. Now, instead of one tree that everyone has to use repeatedly in different ways, there are hundreds of different trees that all came from the same initial version but have been customized and improved upon.
How Does AI Build a Tree?
If we relied on AI to build the perfect tree, it would calculate only one way to do so, but what if you don’t like it? It’s no longer perfect for you. So, rather than relying on AI completely, developers can choose how to embed AI capabilities into their programs to drive the best customer experience.
These AI integrations have to be customized to fit the environment, however, and this requires an actual engineer to be part of the process, which ensures the human element is preserved.
Ultimately, software engineers have to find the right balance of enabling smarter technology that can proactively anticipate and solve our problems while also ensuring end users have the ability to exercise their right to choose and decide what’s right for them based on the facts presented.
Human Touch at the Heart of Modern Software Design
No longer are software solutions built for a single defined purpose. Instead, they are built to learn and evolve based on the creativity, artistry, and ethics of the human touch. As emerging technologies have helped enable modern innovation with unprecedented speed, and invention can now be born out of demand and pursuit of progress rather than out of necessity.
As AI continues to be integrated into the software development process, the human touch will play an integral role in teaching the software how to adapt to each user’s needs, values, behavior, and privacy priorities.
Featured Image Credit: Photo by Andrea Piacquadio; Pexels; Thank you!