#FactCheck-Fake Graphic Falsely Claims Rajinikanth Appointed Kerala Governor

Introduction

Cyber slavery is a form of modern exploitation that begins with online deception and evolves into physical human trafficking. In recent times, cyber slavery has emerged as a serious threat that involves exploiting individuals through digital means under coercive or deceptive conditions. Offenders target innocent individuals and lure them by giving fake promises to offer them employment or alike. Cyber slavery can occur on a global scale, targeting vulnerable individuals worldwide through the internet and is a disturbing continuum of online manipulation that leads to real-world abuse and exploitation, where individuals are entrapped by false promises and subjected to severe human rights violations. It can take many different forms, such as coercive involvement in cybercrime, forced employment in online frauds, exploitation in the gig economy, or involuntary slavery. This issue has escalated to the highest level where Indians are being trafficked for jobs in countries like Laos and Cambodia. Recently over 5,000 Indians were reported to be trapped in Southeast Asia, where they are allegedly being coerced into carrying out cyber fraud. It was reported that particularly Indian techies were lured to Cambodia for high-paying jobs and later they found themselves trapped in cyber fraud schemes, forced to work 16 hours a day under severe conditions. This is the harsh reality for thousands of Indian tech professionals who are lured under false pretences to employment in Southeast Asia, where they are forced into committing cyber crimes.

Over 5,000 Indians Held in Cyber Slavery and Human Trafficking Rings

India has rescued 250 citizens in Cambodia who were forced to run online scams, with more than 5,000 Indians stuck in Southeast Asia. The victims, mostly young and tech-savvy, are lured into illegal online work ranging from money laundering and crypto fraud to love scams, where they pose as lovers online. It was reported that Indians are being trafficked for jobs in countries like Laos and Cambodia, where they are forced to conduct cybercrime activities. Victims are often deceived about where they would be working, thinking it will be in Thailand or the Philippines. Instead, they are sent to Cambodia, where their travel documents are confiscated and they are forced to carry out a variety of cybercrimes, from stealing life savings to attacking international governmental or non-governmental organizations. The Indian embassy in Phnom Penh has also released an advisory warning Indian nationals of advertisements for fake jobs in the country through which victims are coerced to undertake online financial scams and other illegal activities.

Regulatory Landscape

Trafficking in Human Beings (THB) is prohibited under the Constitution of India under Article

23 (1). The Immoral Traffic (Prevention) Act, of 1956 (ITPA) is the premier legislation for the prevention of trafficking for commercial sexual exploitation. Section 111 of the Bharatiya Nyaya Sanhita (BNS), 2023, is a comprehensive legal provision aimed at combating organized crime and will be useful in persecuting people involved in such large-scale scams. India has also ratified certain bilateral agreements with several countries to facilitate intelligence sharing and coordinated efforts to combat transnational organized crime and human trafficking.

CyberPeace Policy Recommendations

● Misuse of Technology has exploited the new genre of cybercrimes whereby cybercriminals utilise social media platforms as a tool for targeting innocent individuals. It requires collective efforts from social media companies and regulatory authorities to time to time address the new emerging cybercrimes and develop robust preventive measures to counter them.

● Despite the regulatory mechanism in place, there are certain challenges such as jurisdictional challenges, challenges in detection due to anonymity, and investigations challenges which significantly make the issue of cyber human trafficking a serious evolving threat. Hence International collaboration between the countries is encouraged to address the issue considering the present situation in a technologically driven world. Robust legislation that addresses both national and international cases of human trafficking and contains strict penalties for offenders must be enforced.

● Cybercriminals target innocent people by offering fake high-pay job opportunities, building trust and luring them. It is high time that all netizens should be aware of such tactics deployed by bad actors and recognise the early signs of them. By staying vigilant and cross-verifying the details from authentic sources, netizens can safeguard themselves from such serious threats which even endanger their life by putting them under restrictions once they are being trafficked. It is a notable fact that the Indian government and its agencies are continuously making efforts to rescue the victims of cyber human trafficking or cyber slavery, they must further develop robust mechanisms in place to conduct specialised operations by specialised government agencies to rescue the victims in a timely manner.

● Capacity building and support mechanisms must be encouraged by government entities, cyber security experts and Non-Governmental Organisations (NGOs) to empower the netizens to follow best practices while navigating the online landscape, providing them with helpline or help centres to report any suspicious activity or behaviour they encounter, and making them empowered to feel safe on the Internet while simultaneously building defenses to stay protected from cyber threats.

References:

1. https://www.news18.com/india/shock-treatment-physical-abuse-indians-getting-trafficked-for-cyber-slavery-to-dupe-other-indians-8954038.html

2. https://www.bbc.com/news/world-asia-india-68705913

3. https://therecord.media/india-rescued-cambodia-scam-centers-citizens

4. https://www.the420.in/rescue-indian-tech-workers-cambodia-cyber-fraud-awareness/

5. https://www.hindustantimes.com/india-news/360-indian-citizens-working-as-cyber-slaves-in-cambodia-return-home-101716431762282.html

6. https://www.ndtv.com/india-news/14-indians-rescued-from-cyber-slavery-in-cambodia-wait-to-return-home-6147228

7. https://www.dyami.services/post/intel-brief-250-indian-citizens-rescued-from-cyber-slavery

8. https://www.mea.gov.in/human-trafficking.htm

9. https://www.drishtiias.com/blog/the-vicious-cycle-of-human-trafficking-and-cybercrime

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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.

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Why Bias in Training Data Matters

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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)

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Statistical Principles for Reducing Training Data Bias 

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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.

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Balancing Data Through Re-Sampling and Re-Weighting

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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)

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Feature Engineering and Data Transformation

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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)

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Measuring Fairness With Metrics

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

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Fairness-Aware Algorithms Explained

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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.

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Pre-Processing Techniques

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

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• 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)

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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.

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In-Processing Techniques

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

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• 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 

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Post-Processing Techniques

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

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• 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)

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Challenges 

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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)

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Why Fairness-Aware Development Matters

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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.

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Conclusion 

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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.

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References

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• 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

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Introduction

In today's era of digitalised community and connections, social media has become an integral part of our lives. A large number of teenagers are also active and have their accounts on social media. They use social media to connect with their friends and family. Social media offers ease to connect and communicate with larger communities and even showcase your creativity. On the other hand, it also poses some challenges or issues such as inappropriate content, online harassment, online stalking, misuse of personal information, abusive and dishearted content etc. There could be unindented consequences on teenagers' mental health by such threats or overuse of social media. The data shows some teens spend hours a day on social media hence it has a larger impact on them whether we notice it or not.  Social media addiction and its negative repercussions such as overuse of social media by teens and online threats and vulnerabilities is a growing concern that needs to be taken seriously by social media platforms, regulatory policies and even user's responsibilities. Recently Colorado and California led a joint lawsuit filed by 33 states in the U.S. District Court for the Northern District of California against meta on the concern of child safety.

Meta and concern of child users safety

Recently Meta, the company that owns Facebook, Instagram, WhatsApp, and Messenger, has been sued by more than three dozen states for allegedly using features to hook children to its platforms. The lawsuit claims that Meta violated consumer protection laws and deceived users about the safety of its platforms. The states accuse Meta of designing manipulative features to induce young users' compulsive and extended use, pushing them into harmful content. However, Meta has responded by stating that it is working to provide a safer environment for teenagers and expressing disappointment in the lawsuit.

According to the complaint filed by the states, Meta “designed psychologically manipulative product features to induce young users’ compulsive and extended use" of platforms like Instagram. The states allege that Meta's algorithms were designed to push children and teenagers into rabbit holes of toxic and harmful content, with features like "infinite scroll" and persistent alerts used to hook young users. However, meta responded with disappointment with a lawsuit stating that meta working productively with companies across the industry to create clear, age-appropriate standards for the many apps.

Unplug for sometime

Overuse of social media is associated with increased mental health repercussions along with online threats and risks. Social media’s effect on teenagers is driven by factors such as inadequate sleep, exposure to cyberbullying and online threats and lack of physical activity. Its admitted that social media can help teens feel more connected to their friends and their support system and showcase their creativity to the online world.  However, social media overuse by teens is often linked with underlying issues that require attention. To help teenagers, encourage them for responsible use and unplug from social media for some time, encourage them to get outside in nature, do physical activities, and express themselves creatively.  

Understanding the threats & risks

• Psychological effects 

• Addiction: Excessive use of social media will lead to procrastination and excessively using social media can lead to physical and psychological addiction because it triggers the brain's reward system. 
• Mental Conditions Associated: Excessively using social media can be harmful for mental well-being which can also lead to depression and anxiety, self-consciousness and may also lead to social anxiety disorder.
• Eyes, Carpal tunnel syndrome: Excessive spending time on screen may lead to put a real strain on your eyes. Eye problems caused by computer/phone screen use fall under computer vision syndrome (CVS). Carpal tunnel syndrome is caused by pressure on the median nerve.

• Cyberbullying: Cyberbullying is one of the major concerns faced in online interactions on social media. Cyberbullying takes place using the internet or other digital communication technology to bully, harass, or intimidate others and it has become a major concern of online harassment on popular social media platforms. Cyberbullying may include spreading rumours or posting hurtful comments. Cyberbullying has emerged as a phenomenon that has a socio-psychological impact on the victims. 
• Online grooming: Online grooming is defined as the tactics abusers deploy through the internet to sexually exploit children. The average time for a bad actor to lure children into his trap is 3 minutes, which is a very alarming number. 
• Ransomware/Malware/Spyware: Cybercrooks impose threats such as ransomware, malware and spyware by deploying malicious links on social media. This poses serious cyber threats, and it causes consequences such as financial losses, data loss, and reputation damage. Ransomware is a type of malware which is designed to deny a user or organisation access to their files on the computer. On social media, cyber crooks post malicious links which contain malware, and spyware threats. Hence it is important to be cautious before clicking on any such suspicious link.
• Sextortion: Sextortion is a crime where the perpetrator threatens the victim and demands ransom or asks for sexual favours by threatening the victim to expose or reveal the victim’s sexual activity. It is a kind of sexual blackmail, it may take place on social media and youngsters are mostly targeted. The cyber crooks also misuse the advanced AI Deepfake technology which is capable of creating realistic images or videos which in actuality are created by machine algorithms. Deepfakes technology since easily accessible, is misused by fraudsters to commit various crimes including sextortion or deceiving and scamming people through fake images or videos which look realistic. 

• Child sexual abuse material(CSAM): CSAM is inappropriate or illicit content which is prohibited by the laws and regulatory guidelines. Child while using the internet if encounters age-restricted or inappropriate content which may be harmful to them child. Through regulatory guidelines, internet service providers are refrained from hosting the CSAM content on the websites and blocking such inappropriate or CSAM content. 

• In App purchases: The teen user also engages in-app purchases on social media or online gaming where they might fall into financial fraud or easy money scams. Where fraudster targets through offering exciting job offers such as part-time job, work-from-home job, small investments, liking content on social media, and earning money out of this. This has been prevalent on social media and fraudsters target innocent people ask for their personal and financial information, and commit financial fraud by scamming people on the pretext of offering exciting offers.

Safety tips:

To stay safe while using social media teens or users are encouraged to follow the best practices and stay aware of the online threats. Users must keep in regard to the best practices. Such as;

• Safe web browsing.
• Utilising privacy settings of your social media accounts.
• Using strong passwords and enabling two-factor authentication.
• Be careful about what you post or share.
• Becoming familiar with the privacy policy of the social media platforms.
• Being selective of adding unknown users to your social media network.
• Reporting any suspicious activity to the platform or relevant forum.

Conclusion:

Child safety is a major concern on social media platforms. Social media-related offences such as cyberstalking, hacking, online harassment and threats, sextortion, and financial fraud are seen as the most occurring cyber crimes on social media. The tech giants must ensure the safety of teen users on social media by implementing and adopting the best mechanisms on the platform. CyberPeace Foundation is working towards advocating for a Child-friendly SIM to protect from the illicit influence of the internet and Social Media. 

References:

• https://www.scientificamerican.com/article/heres-why-states-are-suing-meta-for-hurting-teens-with-facebook-and-instagram/
• https://www.nytimes.com/2023/10/24/technology/states-lawsuit-children-instagram-facebook.html

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