#FactCheck-Video Falsely Shared as Nepal Expelling Bangladeshis; Actually Shows Anti-Encroachment Drive in Dhaka

Artificial intelligence is revolutionizing industries such as healthcare to finance to influence the decisions that touch the lives of millions daily. However, there is a hidden danger associated with this power: unfair results of AI systems, reinforcement of social inequalities, and distrust of technology. One of the main causes of this issue is training data bias, which appears when the examples on which an AI model is trained are not representative or skewed. To deal with it successfully, this needs a combination of statistical methods, algorithmic design that is mindful of fairness, and robust governance over the AI lifecycle. This article discusses the origin of bias, the ways to reduce it, and the unique position of fairness-conscious algorithms.

‍

Why Bias in Training Data Matters

‍

The bias in AI occurs when the models mirror and reproduce the trends of inequality in the training data. When a dataset has a biased representation of a demographic group or includes historical biases, the model will be trained to make decisions in ways that will harm the group. This is a fact that has a practical implication: prejudiced AI may cause discrimination during the recruitment of employees, lending, and evaluation of criminal risks, as well as various other spheres of social life, thus compromising justice and equity. These problems are not only technical in nature but also require moral principles and a system of governance (E&ICTA).

Bias is not uniform. It may be based on the data itself, the algorithm design, or even the lack of diversity among developers. The bias in data occurs when data does not represent the real world. Algorithm bias may arise when design decisions inadvertently put one group at an unfair advantage over another. Both the interpretation of the model and data collection may be affected by human bias. (MDPI)

‍

Statistical Principles for Reducing Training Data Bias 

‍

Statistical principles are at the core of bias mitigation and they redefine the data-model interaction. These approaches are focused on data preparation, training process adjustment, and model output corrections in such a way that the notion of fairness becomes a quantifiable goal.

‍

Balancing Data Through Re-Sampling and Re-Weighting

‍

Among the aforementioned methods, a fair representation of all the relevant groups in the dataset is one way. This can be achieved by oversampling underrepresented groups and undersampling overrepresented groups. Oversampling gives greater weight to minority examples, whereas re-weighting gives greater weight to under-represented data points in training. The methods minimize the tendency of models to fit to salient patterns and improve coverage among vulnerable groups. (GeeksforGeeks)

‍

Feature Engineering and Data Transformation

‍

The other statistical technique is to convert data characteristics in such a way that sensitive characteristics have a lesser impact on the results. In one example, fair representation learning adjusts the data representation to discourage bias during the untraining of the model. The disparate impact remover adjust technique performs the adjustment of features of the model in such a way that the impact of sensitive features is reduced during learning. (GeeksforGeeks)

‍

Measuring Fairness With Metrics

‍

Statistical fairness measures are used to measure the effectiveness of a model in groups. 

‍

Fairness-Aware Algorithms Explained

‍

Fair algorithms do not simply detect bias. They incorporate fairness goals in model construction and run in three phases including pre-processing, in-processing, and post-processing.

‍

Pre-Processing Techniques

‍

Fairness-aware pre-processing deals with bias prior to the model consuming the information. This involves the following ways:

‍

• Rebalancing training data through sampling and re-weighting training data to address sample imbalances.
• Data augmentation to generate examples of underrepresented groups.
• Feature transformation removes or downplays the impact of sensitive attributes prior to the commencement of training. (IJMRSET)

‍

These methods can be used to guarantee that the model is trained on more balanced data and to reduce the chances of bias transfer between historical data.

‍

In-Processing Techniques

‍

The in-processing techniques alter the learning algorithm. These include:

‍

• Fairness constraints that penalize the model for making biased predictions during training.
• Adversarial debiasing, where a second model is used to ensure that sensitive attributes are not predicted by the learned representations.
• Fair representation learning that modifies internal model representations in favor of 

‍

Post-Processing Techniques

‍

Fairness may be enhanced after training by changing the model outputs. These strategies comprise:

‍

• Threshold adjustments to various groups to meet conditions of fairness, like equalized odds.
• Calibration techniques such that the estimated probabilities are fair indicators of the actual probabilities in groups. (GeeksforGeeks)

‍

Challenges 

‍

Mitigating bias is complex. The statistical bias minimization may at times come at the cost of the model accuracy, and there is a conflict between predictive performance and fairness. The definition of fairness itself is potentially a difficult task because various applications of fairness require various criteria, and various criteria can be conflicting. (MDPI)

Gaining varied and representative data is also a challenge that is experienced because of privacy issues, incomplete records, and a lack of resources. The auditing and reporting done on a continuous basis are needed so that mitigation processes are up to date, as models are continually updated. (E&ICTA)

‍

Why Fairness-Aware Development Matters

‍

The outcomes of the unfair treatment of some groups by AI systems are far-reaching. Discriminatory software in recruitment may support inequality in the workplace. Subjective credit rating may deprive deserving people of opportunities. Unbiased medical forecasts might result in the flawed allocation of medical resources. In both cases, prejudice contravenes the credibility and clouds the greater prospect of AI. (E&ICTA)

Algorithms that are fair and statistical mitigation plans provide a way to create not only powerful AI but also fair and trustworthy AI. They admit that the results of AI systems are social tools whose effects extend across society. Responsible development will necessitate sustained fairness quantification, model adjustment, and upholding human control.

‍

Conclusion 

‍

AI bias is not a technical malfunction. It is a mirror of real-world disparities in data and exaggerated by models. Statistical rigor, wise algorithm design, and readiness to address the trade-offs between fairness and performance are required to reduce training data bias. Fairness-conscious algorithms (which can be implemented in pre-processing, in-processing, or post-processing) are useful in delivering more fair results. As AI is taking part in the most crucial decisions, it is necessary to consider fairness at the beginning to have a system that serves the population in a responsible and fair manner.

‍

References

‍

• Understanding Bias in Artificial Intelligence: Challenges, Impacts, and Mitigation Strategies: E&ICTA, IITK
• Bias and Fairness in Artificial Intelligence: Methods and Mitigation Strategies: JRPS Shodh Sagar 
• Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies: MDPI 
• Ensuring Fairness in Machine Learning Algorithms: GeeksforGeeks
  Bias and Fairness in Machine Learning Models: A Critical Examination of Ethical Implications: IJMRSET 
• Bias in AI Models: Origins, Impact, and Mitigation Strategies: Preprints
• Bias in Artificial Intelligence and Mitigation Strategies: TCS
• Survey on Machine Learning Biases and Mitigation Techniques: MDPI

‍

Executive Summary:

A manipulated image showing someone making an offensive gesture towards Prime Minister Narendra Modi is circulating on social media. However, the original photo does not display any such behavior towards the Prime Minister. The CyberPeace Research Team conducted an analysis and found that the genuine image was published in a Hindustan Times article in May 2019, where no rude gesture was visible. A comparison of the viral and authentic images clearly shows the manipulation. Moreover, The Hitavada also published the same image in 2019. Further investigation revealed that ABPLive also had the image.

Claims:

A picture showing an individual making a derogatory gesture towards Prime Minister Narendra Modi is being widely shared across social media platforms.

‍

Fact Check:

Upon receiving the news, we immediately ran a reverse search of the image and found an article by Hindustan Times, where a similar photo was posted but there was no sign of such obscene gestures shown towards PM Modi.

ABP Live and The Hitavada also have the same image published on their website in May 2019. 

‍

Comparing both the viral photo and the photo found on official news websites, we found that almost everything resembles each other except the derogatory sign claimed in the viral image.

With this, we have found that someone took the original image, published in May 2019, and edited it with a disrespectful hand gesture, and which has recently gone viral across social media and has no connection with reality.

Conclusion:

In conclusion, a manipulated picture circulating online showing someone making a rude gesture towards Prime Minister Narendra Modi has been debunked by the Cyberpeace Research team. The viral image is just an edited version of the original image published in 2019. This demonstrates the need for all social media users to check/ verify the information and facts before sharing, to prevent the spread of fake content. Hence the viral image is fake and Misleading.

• Claim: A picture shows someone making a rude gesture towards Prime Minister Narendra Modi
• Claimed on: X, Instagram
• Fact Check: Fake & Misleading

‍

Introduction

All citizens are using tech to their advantage, and so we see a lot of upskilling among the population leading to innovation in India. As we go deeper into cyberspace, we must maintain our cyber security efficiently and effectively. When bad actors use technology to their advantage, we often see data loss or financial loss of the victim, In this blog, we will shine light upon two new forms of cyber attacks, causing havoc upon the innocent. The “Daam” Malware and a new malicious app are the two new issues.

Daam Botnet

Since 2021, the DAAM Android botnet has been used to acquire unauthorised access to targeted devices. Cybercriminals use it to carry out different destructive actions. Using the DAAM Android botnet’s APK binding service, threat actors can combine malicious code with a legitimate application. Keylogging, ransomware, VOIP call records, runtime code execution, browser history collecting, incoming call recording, PII data theft, phishing URL opening, photo capture, clipboard data theft, WiFi and data status switching, and browser history gathering are just a few of the functions offered by the DAAM Android botnet. The DAAM botnet tracks user activity using the Accessibility Service and stores keystrokes it has recorded together with the name of the programme package in a database. It also contains a ransomware module that encrypts and decrypts data on the infected device using the AES method.

Additionally, the botnet uses the Accessibility service to monitor the VOIP call-making features of social media apps like WhatsApp, Skype, Telegram, and others. When a user engages with these elements, the virus begins audio recording.

The Malware

CERT-IN, the central nodal institution that reacts to computer security-related issues, claims that Daam connects with various Android APK files to access a phone. The files on the phone are encrypted using the AES encryption technique, and it is distributed through third-party websites.

It is claimed that the malware can damage call recordings and contacts, gain access to the camera, change passwords, take screenshots, steal SMS, download/upload files, and perform a variety of other things.

Safeguards and Guidelines by Cert-In

Cert-In has released the guideline for combating malware. These were issued in the public interest. The recommendations by Cert-In are as follows-

Only download from official app stores to limit the risk of potentially harmful apps.

Before downloading an app, always read the details and user reviews; likewise, always give permissions that are related to the program’s purpose.

Install Android updates solely from Android device vendors as they become available.

Avoid visiting untrustworthy websites or clicking on untrustworthy

Install and keep anti-virus and anti-spyware software up to date.

Be cautious if you see mobile numbers that appear to be something other than genuine/regular mobile numbers.

Conduct sufficient investigation Before clicking on a link supplied in a communication.

Only click on URLs that clearly display the website domain; avoid abbreviated URLs, particularly those employing bit.ly and tinyurl.

Use secure browsing technologies and filtering tools in antivirus, firewall, and filtering services.

Before providing sensitive information, look for authentic encryption certificates by looking for the green lock in your browser’s URL information, look for authentic encryption certificates by looking for the green lock in your browser’s URL bar.

Any ‘strange’ activity in a user’s bank account must be reported immediately to the appropriate bank.

‍

New Malicious App

From the remote parts of Jharkhand, a new form of malicious application has been circulated among people on the pretext of a bank account closure. The bad actors have always used messaging platforms like Whatsapp and Telegram to circulate malicious links among unaware and uneducated people to dupe them of their hard-earned money.

They send an ordinary-looking message on Whatsapp or Telegram where they mention that the user has a bank account at ICICI bank and, due to irregularity with the credentials, their account is being deactivated. Further, they ask users to update their PAN card to reactivate their account by uploading the PAN card on an application. This app, in turn, is a malicious app that downloads all the user’s personal credentials and shares them with the bad actors via text message, allowing them to bypass banks’ two-factor authentication and drain the money from their accounts. The Jharkhand Police Cyber Cells have registered numerous FIRs pertaining to this type of cybercrime and are conducting full-scale investigations to apprehend the criminals.

Conclusion

Malware and phishing attacks have gained momentum in the previous years and have become a major contributor to the tally of cybercrimes in the country. DaaM malware is one of the examples brought into light due to the timely action by Cert-In, but still, a lot of such malware are deployed by bad actors, and we as netizens need to use our best practices to keep such criminals at bay. Phishing crimes are often substantiated by exploiting vulnerabilities and social engineering. Thus working towards a rise in awareness is the need of the hour to safeguard the population by and large.