#FactCheck - Misleading Video Allegedly Depicting Trampling of Indian Tri-colour in Kerala or Tamil Nadu Circulates on Social Media

Introduction

Search engines have become indispensable in our daily lives, allowing us to find information instantly by entering keywords or phrases. Using the prompt "search Google or type a URL" reflects just how seamless this journey to knowledge has become. With millions of searches conducted every second, and Google handling over 6.3 million searches per minute as of 2023 (Statista), one critical question arises: do search engines prioritise results based on user preferences and past behaviours, or are they truly unbiased?

Understanding AI Bias in Search Algorithms

AI bias is also known as machine learning bias or algorithm bias. It refers to the occurrence of biased results due to human biases that deviate from the original training data or AI algorithm which leads to distortion of outputs and creation of potentially harmful outcomes. The sources of this bias are algorithmic bias, data bias and interpretation bias which emerge from user history, geographical data, and even broader societal biases in training data. 

Common biases include excluding certain groups of people from opportunities because of AI bias. In healthcare, underrepresenting data of women or minority groups can skew predictive AI algorithms. While AI helps streamline the automation of resume scanning during a search to help identify ideal candidates, the information requested and answers screened out can result in biased outcomes due to a biased dataset or any other bias in the input data.

Case in Point: Google’s "Helpful" Results and Its Impact 

Google optimises results by analysing user interactions to determine satisfaction with specific types of content. This data-driven approach forms ‘filter bubbles’ by repeatedly displaying content that aligns with a user’s preferences, regardless of factual accuracy. While this can create a more personalised experience, it risks confining users to a limited view, excluding diverse perspectives or alternative viewpoints.

The personal and societal impacts of such biases are significant. At an individual level, filter bubbles can influence decision-making, perceptions, and even mental health. On a societal level, these biases can reinforce stereotypes, polarise opinions, and shape collective narratives. There is also a growing concern that these biases may promote misinformation or limit users’ exposure to diverse perspectives, all stemming from the inherent bias in search algorithms.

Policy Challenges and Regulatory Measures

Regulating emerging technologies like AI, especially in search engine algorithms, presents significant challenges due to their intricate, proprietary nature. Traditional regulatory frameworks struggle to keep up with them as existing laws were not designed to address the nuances of algorithm-driven platforms. Regulatory bodies are pushing for transparency and accountability in AI-powered search algorithms to counter biases and ensure fairness globally. For example, the EU’s Artificial Intelligence Act aims to establish a regulatory framework that will categorise AI systems based on risk and enforces strict standards for transparency, accountability, and fairness, especially for high-risk AI applications, which may include search engines. India has proposed the Digital India Act in 2023 which will define and regulate High-risk AI. 

Efforts include ethical guidelines emphasising fairness, accountability, and transparency in information prioritisation. However, a complex regulatory landscape could hinder market entrants, highlighting the need for adaptable, balanced frameworks that protect user interests without stifling innovation.

CyberPeace Insights

In a world where search engines are gateways to knowledge, ensuring unbiased, accurate, and diverse information access is crucial. True objectivity remains elusive as AI-driven algorithms tend to personalise results based on user preferences and past behaviour, often creating a biased view of the web. Filter bubbles, which reinforce individual perspectives, can obscure factual accuracy and limit exposure to diverse viewpoints. Addressing this bias requires efforts from both users and companies. Users should diversify sources and verify information, while companies should enhance transparency and regularly audit algorithms for biases. Together, these actions can promote a more equitable, accurate, and unbiased search experience for all users. 

References 

• https://www.bbc.com/future/article/20241101-how-online-photos-and-videos-alter-the-way-you-think 
• https://www.bbc.com/future/article/20241031-how-google-tells-you-what-you-want-to-hear
• https://www.ibm.com/topics/ai-bias#:~:text=In%20healthcare%2C%20underrepresenting%20data%20of,can%20skew%20predictive%20AI%20algorithms

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Executive Summary:

A video of Pakistani Olympic gold medalist and Javelin player Arshad Nadeem wishing Independence Day to the People of Pakistan, with claims of snoring audio in the background is getting viral. CyberPeace Research Team found that the viral video is digitally edited by adding the snoring sound in the background. The original video published on Arshad's Instagram account has no snoring sound where we are certain that the viral claim is false and misleading.

Claims:

A video of Pakistani Olympic gold medalist Arshad Nadeem wishing Independence Day with snoring audio in the background.

Fact Check:

Upon receiving the posts, we thoroughly checked the video, we then analyzed the video in TrueMedia, an AI Video detection tool, and found little evidence of manipulation in the voice and also in face.

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We then checked the social media accounts of Arshad Nadeem, we found the video uploaded on his Instagram Account on 14th August 2024. In that video, we couldn’t hear any snoring sound. 

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Hence, we are certain that the claims in the viral video are fake and misleading.

Conclusion:

The viral video of Arshad Nadeem with a snoring sound in the background is false. CyberPeace Research Team confirms the sound was digitally added, as the original video on his Instagram account has no snoring sound, making the viral claim misleading.

• Claim: A snoring sound can be heard in the background of Arshad Nadeem's video wishing Independence Day to the people of Pakistan.
• Claimed on: X, 
• Fact Check: Fake & Misleading

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Introduction

Discussions took place focused on cybersecurity measures, specifically addressing cybercrime in the context of emerging technologies such as Non-Fungible Tokens (NFTs), Artificial Intelligence (AI), and the Metaverse. Session 5 of the conference focused on the interconnectedness between the darknet and cryptocurrency and the challenges it poses for law enforcement agencies and regulators. They discussed that Understanding AI is necessary for enterprises. AI models have difficulties, but we are looking forward to trustworthy AIs. and AI technology must be transparent.

Darknet and Cryptocurrency

The darknet refers to the hidden part of the internet where illicit activities have proliferated in recent years. It was initially developed to provide anonymity, privacy, and protection to specific individuals such as journalists, activists, and whistleblowers. However, it has now become a playground for criminal activities. Cryptocurrency, particularly Bitcoin, has been widely adopted on the darknet due to its anonymous nature, enabling anti-money laundering and unlawful transactions.

Three major points emerge from this relationship: the integrated nature of the darknet and cryptocurrency, the need for regulations to prevent darknet-based crimes, and the importance of striking a balance between privacy and security.

Key Challenges:

1. Integrated Relations: The darknet and cryptocurrency have evolved independently, with different motives and purposes. It is crucial to understand the integrated relationship between them and how criminals exploit this connection.
2. Regulatory Frameworks: There is a need for effective regulations to prevent crimes facilitated through the darknet and cryptocurrency while striking a balance between privacy and security.
3. Privacy and Security: Privacy is a fundamental right, and any measures taken to enhance security should not infringe upon individual privacy. A multistakeholder approach involving tech companies and regulators is necessary to find this delicate balance.

Challenges Associated with Cryptocurrency Use:

The use of cryptocurrency on the darknet poses several challenges. The risks associated with darknet-based cryptocurrency crimes are a significant concern. Additionally, regulatory challenges arise due to the decentralised and borderless nature of cryptocurrencies. Mitigating these challenges requires innovative approaches utilising emerging technologies.

Preventing Misuse of Technologies:

The discussion emphasised that we can step ahead of the people who wish to use these beautiful technologies meant and developed for a different purpose, to prevent from using them for crime.

Monitoring the Darknet:

The darknet, as explained, is an elusive part of the internet that necessitates the use of a special browser for access. Initially designed for secure communication by the US government, its purpose has drastically changed over time. The darknet’s evolution has given rise to significant challenges for law enforcement agencies striving to monitor its activities.

Around 95% of the activities carried out on the dark net are associated with criminal acts. Estimates suggest that over 50% of the global cybercrime revenue originates from the dark net. This implies that approximately half of all cybercrimes are facilitated through the darknet.

The exploitation of the darknet has raised concerns regarding the need for effective regulation. Monitoring the darknet is crucial for law enforcement, national agencies, and cybersecurity companies. The challenges associated with the darknet’s exploitation and the criminal activities facilitated by cryptocurrency emphasise the pressing need for regulations to ensure a secure digital landscape.

Use of Cryptocurrency on the Darknet

Cryptocurrency plays a central role in the activities taking place on the darknet. The discussion highlighted its involvement in various illicit practices, including ransomware attacks, terrorist financing, extortion, theft, and the operation of darknet marketplaces. These applications leverage cryptocurrency’s anonymous features to enable illegal transactions and maintain anonymity.

AI's Role in De-Anonymizing the Darknet and Monitoring Challenges:

1.AI’s Potential in De-Anonymizing the Darknet

During the discussion, it was highlighted how AI could be utilised to help in de-anonymizing the darknet. AI’s pattern recognition capabilities can aid in identifying and analysing patterns of behaviour within the darknet, enabling law enforcement agencies and cybersecurity experts to gain insights into its operations. However, there are limitations to what AI can accomplish in this context. AI cannot break encryption or directly associate patterns with specific users, but it can assist in identifying illegal marketplaces and facilitating their takedown. The dynamic nature of the darknet, with new marketplaces quickly emerging, adds further complexity to monitoring efforts.

2.Challenges in Darknet Monitoring

Monitoring the darknet poses various challenges due to its vast amount of data, anonymous and encrypted nature, dynamically evolving landscape, and the need for specialised access. These challenges make it difficult for law enforcement agencies and cybersecurity professionals to effectively track and prevent illicit activities.

3.Possible Ways Forward

To address the challenges, several potential avenues were discussed. Ethical considerations, striking a balance between privacy and security, must be taken into account. Cross-border collaboration, involving the development of relevant laws and policies, can enhance efforts to combat darknet-related crimes. Additionally, education and awareness initiatives, driven by collaboration among law enforcement, government entities, and academia, can play a crucial role in combating darknet activities.

The panel also addressed the questions from the audience

• How law enforcement agencies and regulators can use AI to detect and prevent crimes on the darknet and cryptocurrency? The panel answered that- Law enforcement officers should also be AI and technology ready, and that kind of upskilling program should be there in place.
• How should lawyers and the judiciary understand the problem and regulate it? The panel answered that AI should only be applied by looking at the outcomes. And Law has to be clear as to what is acceptable and what is not.
• Aligning AI with human intention? Whether it’s possible? Whether can we create an ethical AI instead of talking about using AI ethically? The panel answered that we have to understand how to behave ethically. AI can beat any human. We have to learn AI. Step one is to focus on our ethical behaviour. And step two is bringing the ethical aspect to the software and technologies. Aligning AI with human intention and creating ethical AI is a challenge. The focus should be on ethical behaviour both in humans and in the development of AI technologies.

Conclusion

The G20 Conference on Crime and Security shed light on the intertwined relationship between the darknet and cryptocurrency and the challenges it presents to cybersecurity. The discussions emphasised the need for effective regulations, privacy-security balance, AI integration, and cross-border collaboration to tackle the rising cybercrime activities associated with the darknet and cryptocurrency. Addressing these challenges will require the combined efforts of governments, law enforcement agencies, technology companies, and individuals committed to building a safer digital landscape.