Artificial Intelligence: 5 Ways It's Impacting Your Life

Contents:

• Dystopia in the Age of Technology
• Why Fines for Pedestrians and Drivers Are Important: An Analysis of Modern Technologies
• Reasons for Loan Denial: From an Unsavory Photo to Questionable Social Connections
• How Neural Networks Influence CV Decision-Making

Dystopia in the Age of Technology

Artificial intelligence (AI) has been actively used for several years to evaluate people in various fields, including employment and lending. Algorithms that analyze data are capable of making decisions that can significantly impact your life. Depending on the results of the assessment, you may not only get the job you want but also face rejection. It's also worth considering that AI can influence financial decisions, including your ability to obtain loans and the associated penalties. Therefore, it's important to be aware of how these technologies work and how they may impact your financial and career prospects.

Artificial intelligence is increasingly being used in various fields, including the judicial system. Some US states use the COMPAS system, which assesses the likelihood of recidivism based on an analysis of the defendant's background, psychological test results, and other data. These assessments can significantly influence judges' sentencing decisions, highlighting the high level of responsibility associated with the use of algorithms in justice. The use of AI in such critical areas raises serious questions about the fairness and accuracy of algorithmic decisions, requiring careful analysis and regulation.

In March 2020, a significant event occurred: a Kargu-2 unmanned aerial vehicle, operating autonomously, voluntarily decided to attack Libyan National Army soldiers. According to data published in the Bulletin of the Atomic Scientists, the drone used information gathered through machine learning. This incident raises serious questions about the oversight and ethics of using autonomous combat systems. Technologies capable of making decisions without human intervention require careful evaluation and regulation to prevent undesirable consequences.

The COVID-19 pandemic has dramatically accelerated the implementation of technologies that businesses and government agencies had put off until later. In 2020-2021, we witnessed rapid change in various areas of life. What consequences could this have for society in the future, and what risks are we already taking? Skillbox Media examines the implementation of artificial intelligence in Russia's largest banks and analyzes the reasons for job applicant denials. It is also worth considering how justified it is to challenge fines for minor violations, such as accidentally crossing road markings with a vehicle.

Why Fines for Pedestrians and Drivers Are Important: An Analysis of Modern Technologies

In today's realities of traffic enforcement, special attention is being paid to the implementation of artificial intelligence technologies. China is a leader in this field, actively using facial recognition systems to impose fines on both motorists and pedestrians. Unfortunately, such innovations have not yet been implemented in Russia, which could negatively impact traffic compliance and road safety. The introduction of such technologies could significantly improve the situation, increasing road users' accountability and reducing the number of violations.

The use of CCTV cameras in Russia began in 2008, and technology has evolved significantly since then. By 2012, neural networks began to be actively used to detect violations. As of 2023, approximately 17,000 stationary and 5,000 mobile CCTV cameras are operational in Russia. Given the rapid development of technology, the total number is expected to increase to 170,000 in the coming years. The introduction of modern CCTV systems improves safety and the effectiveness of public space monitoring, making them an indispensable tool in the fight against crime.

Modern cameras can identify up to 22 different types of traffic violations. Since 2020, these systems have learned to record instances of drivers talking on the phone while driving. This demonstrates that artificial intelligence has reached a significant level of development, allowing it to analyze driver behavior and identify potential violations. As Pyotr Shkumatov, coordinator of the Blue Buckets movement, noted, modern technology can distinguish between moments when a driver is distracted by a phone and situations when they are simply fixing their hair. This underscores the importance of using intelligent systems to improve road safety and reduce the number of accidents related to driver distraction.

The camera does not record the traffic violation itself, but rather the event and its characteristics. Grigory Shukhman, an expert in photo and video recording, clarifies: "The main function of cameras is to identify events that may indicate a violation and document them. The decision to impose a fine is made by a person, not an automated system." However, many drivers mistakenly believe that recording an event automatically means a violation has occurred. It is important to understand that the final decision is made based on an analysis of the collected data, and not solely on the video recording.

Uncertainty in legislation means that drivers often face undeserved fines. The process of disputing these fines remains complex and ineffective. Pyotr Shkumatov emphasizes that the appeals system still uses outdated technology, making the process lengthy and expensive. In most cases, the costs of litigation exceed the fine itself, causing dissatisfaction and increasing stress among drivers. It is important to update the appeals system to make it more transparent and accessible to citizens.

One of the most bizarre cases in traffic regulation was a fine for a shadow. In 2018, an unusual incident occurred: a camera captured a car's shadow crossing a solid line, resulting in a fine for the driver. Despite significant improvements in modern image processing algorithms, such errors still occur. This occurs when cameras detect borderline situations, such as a car's wheel barely touching the line. Such incidents highlight the importance of a careful approach to automated traffic enforcement systems.

Pyotr Shkumatov proposes implementing a risk-based approach to improve the situation on the roads. Camera systems will be able to accumulate data on driver behavior and classify it into risk categories. This will allow for the identification of three risk groups: green, yellow, and red. Drivers in the green group, who demonstrate responsible driving, will receive less attention from cameras, while violators in the red group will be subject to fines. This approach will not only improve road safety but also optimize the performance of traffic enforcement systems.

Reasons for loan denial: from an unsuccessful photo to questionable social connections

Modern technologies, such as artificial intelligence (AI), play An increasingly important role in the lending industry. Neural networks can not only analyze borrowers' financial habits but also study their social connections. Algorithms can assess a person's creditworthiness based on their spending patterns and money transfers to other people. This approach is based on the principle: "Tell me who your friends are, and I'll tell you who you are." Using such technologies allows credit institutions to more accurately assess risks, which contributes to a more efficient decision-making process for issuing loans.

FscoreLab has presented an innovative neural network capable of analyzing borrowers' risks based on their passport photos. According to the developers, studying 600,000 images from credit bureaus made it possible to identify specific characteristics characteristic of debtors. This technology opens up new horizons in creditworthiness assessment, allowing for more accurate financial risk forecasting.

Despite advances in technology, not all financial institutions provide complete information about their credit scoring algorithms. Nevertheless, many large banks in Russia are already implementing such methods. For example, Maxim Konovalikhin, Head of Data Analysis at VTB, emphasizes that machine learning models are actively used to forecast client income. The bank continues to develop the use of artificial intelligence as part of its technological transformation, which allows it to improve the accuracy of credit analysis and enhance customer service.

MCB Bank has been actively using artificial intelligence and machine learning to process loan applications for ten years. However, details about their algorithms remain confidential. Alexander Fedenko, Director of IT at MCB, noted that the bank can only provide general information about the categories of data used in the process. This includes documents confirming the client's solvency, internal history of interactions with the bank, data from the Credit History Bureau, as well as accumulated historical client data that contributes to the formation of a risk assessment model. Effective use of this data allows MKB to improve the quality of lending and reduce the risks associated with issuing loans.

The use of artificial intelligence in lending opens up new opportunities, However, borrowers should recognize the importance of their financial decisions and active social participation. For example, an unfavorable environment can lead to loan denial, even if financial indicators seem satisfactory. Understanding the influence of social factors on credit decisions will help borrowers better prepare for the loan application process and increase their chances of approval.

How neural networks influence resume decision-making

Modern companies are increasingly implementing artificial intelligence for the initial assessment of candidates. The world's largest brands, including McDonald's, JP Morgan, PWC, and Kraft Heinz, use the Pymetrics platform to automatically assess the cognitive skills and emotional reactions of applicants. The use of AI in the recruitment process significantly speeds up the process and improves the accuracy of candidate assessment. This makes the hiring process more efficient and minimizes the influence of the human factor. The integration of such technologies helps companies find the most suitable employees who fit their corporate culture and job requirements.

Modern technologies significantly accelerate the recruitment process, especially in the context of mass hiring. According to Undercover Recruiter, by 2033, automated systems could replace up to 16% of HR professionals. This underscores the importance of implementing technology in recruiting, which not only optimizes processes but also significantly improves the efficiency of candidate search and selection. The use of automation in HR helps companies respond more quickly to changes in the labor market and find the right specialists in the shortest possible time.

The use of neural networks in the recruitment process raises numerous concerns. Errors in algorithms can lead to discrimination against candidates, a topic that has become a hot topic in light of new regulations in New York aimed at limiting the use of artificial intelligence in HR processes. These measures highlight the importance of ensuring fairness and transparency in hiring, which is necessary to create a more inclusive work environment.

Neural networks do not make final hiring decisions, but they significantly influence the preliminary screening of resumes. On popular platforms like hh.ru, moderation algorithms process 74% of resumes without human intervention. This automated solution effectively identifies resumes that may not meet established criteria, such as those containing profanity or overly broad wording. Using neural networks in the recruitment process speeds up the process and improves the quality of selection, allowing employers to focus on the most suitable candidates.

Algorithms hh.ru Optimize the search for the most suitable candidates for employers by displaying resumes that best match job postings. This can be compared to recommendations offered by streaming platforms, where content is selected based on user preferences. This approach significantly speeds up the recruitment process and increases its efficiency, which is an important aspect of successful recruiting.

Artificial intelligence not only matches resume text with job posting requirements but also studies employer behavior on the platform. The system analyzes which candidates are most often invited to interviews and suggests resumes of specialists with similar characteristics. This helps improve the efficiency of recruitment and improve the match between employers and potential employees. Alexander Sidorov, HeadHunter's Head of Data Analysis, emphasizes the importance of such technologies for streamlining the hiring process and improving the quality of job postings.

Geographical factors play a significant role in the candidate selection process. The system takes into account the applicant's location, which is especially important for positions such as storekeeper or cashier. It's worth emphasizing that exaggerating achievements on a resume doesn't significantly impact the performance of neural networks, as people are equally prone to exaggeration. This underscores the need for an honest and transparent approach to resume writing, which can improve your chances of successful employment.

Currently, the HeadHunter platform uses artificial intelligence primarily to analyze text information and geocoordinates. The algorithms don't consider candidates' photographs or social media activity, although many companies still consider these aspects during the recruitment process. Using AI to analyze texts helps improve the quality of selection, but ignoring visual and social factors can limit the completeness of candidate assessment.