#FactCheck: Viral video claims Ahmedabad plane crash but actually a Hollywood Movie Clip

About Global Commission on Internet Governance 

The Global Commission on Internet Governance was established in January 2014 with the goal of formulating and advancing a strategic vision for Internet governance going forward. Independent research on Internet-related issues of international public policy is carried out and supported over the two-year initiative. An official commission report with particular policy recommendations for the future of Internet governance will be made available as a result of this initiative.

There are two goals for the Global Commission on Internet Governance. First, it will encourage a broad and inclusive public discussion on how Internet governance will develop globally. Second, through its comprehensive policy-oriented report and the subsequent marketing of this final report, the Global Commission on Internet Governance will present its findings to key stakeholders at major Internet governance events.‍

‍The Internet: exploring the world wide web and the deep web

The Internet can be thought of as a vast networking infrastructure, or network of networks. By linking millions of computers worldwide, it creates a network that allows any two computers, provided they are both online, to speak with one another.

The Hypertext Transfer Protocol is the only language spoken over the Internet and is used by the Web to transfer data. Email, which depends on File Transfer Protocol, Usenet newsgroups, Simple Mail Transfer Protocol, and instant messaging, is also used on the Internet—not the Web. Thus, even though it's a sizable chunk, the Web is only a part of the Internet [1]. In summary, the deep Web is the portion of the Internet that is not visible to the naked eye. It is stuff from the World Wide Web that isn't available on the main Web. Standard search engines cannot reach it. More than 500 times larger than the visible Web is this enormous subset of the Internet [1-2].

The Global Commission on Internet Governance will concentrate on four principal themes:

• Improving the legitimacy of government, including standards and methods for regulation;

• Promoting economic innovation and expansion, including the development of infrastructure, competition laws, and vital Internet resources; 

• Safeguarding online human rights, including establishing the idea of technological neutrality for rights to privacy, human rights, and freedom of expression;

• Preventing systemic risk includes setting standards for state behaviour, cooperating with law enforcement to combat cybercrime, preventing its spread, fostering confidence, and addressing disarmament-related issues.

Dark Web 

The part of the deep Web that has been purposefully concealed and is unreachable using conventional Web browsers is known as the "dark Web." Dark Web sites are a platform for Internet users who value their anonymity since they shield users from prying eyes and typically utilize encryption to thwart monitoring. The Tor network is a well-known source for content that may be discovered on the dark web. Only a unique Web browser known as the Tor browser is required to access the anonymous Tor network (Tor 2014). It was a technique for anonymous online communication that the US Naval Research Laboratory first introduced as The Onion Routing (Tor) project in 2002. Many of the functionality offered by Tor are also available on I2P, another network. On the other hand, I2P was intended to function as a network inside the Internet, with traffic contained within its boundaries. Better anonymous access to the open Internet is offered by Tor, while a more dependable and stable "network within the network" is provided by I2P [3].

Cybersecurity in the dark web

Cyber crime is not any different than crime in the real world — it is just executed in a new medium: “Virtual criminality’ is basically the same as the terrestrial crime with which we are familiar. To be sure, some of the manifestations are new. But a great deal of crime committed with or against computers differs only in terms of the medium. While the technology of implementation, and particularly its efficiency, may be without precedent, the crime is fundamentally familiar. It is less a question of something completely different than a recognizable crime committed in a completely different way [4].”

Dark web monitoring

The dark Web, in general, and the Tor network, in particular, offer a secure platform for cybercriminals to support a vast amount of illegal activities — from anonymous marketplaces to secure means of communication, to an untraceable and difficult to shut down infrastructure for deploying malware and botnets.

As such, it has become increasingly important for security agencies to track and monitor the activities in the dark Web, focusing today on Tor networks, but possibly extending to other technologies in the near future. Due to its intricate webbing and design, monitoring the dark Web will continue to pose significant challenges. Efforts to address it should be focused on the areas discussed below [5].

Hidden service directory of dark web

A domain database used by both Tor and I2P is based on a distributed system called a "distributed hash table," or DHT. In order for a DHT to function, its nodes must cooperate to store and manage a portion of the database, which takes the shape of a key-value store. Owing to the distributed character of the domain resolution process for hidden services, nodes inside the DHT can be positioned to track requests originating from a certain domain [6].

Conclusion

The deep Web, and especially dark Web networks like Tor (2004), offer bad actors a practical means of transacting in products anonymously and lawfully.

The absence of discernible activity in non-traditional dark web networks is not evidence of their nonexistence. As per the guiding philosophy of the dark web, the actions are actually harder to identify and monitor. Critical mass is one of the market's driving forces. It seems unlikely that operators on the black Web will require a great degree of stealth until the repercussions are severe enough, should they be caught. It is possible that certain websites might go down, have a short trading window, and then reappear, which would make it harder to look into them.

References

1. Ciancaglini, Vincenzo, Marco Balduzzi, Max Goncharov and Robert McArdle. 2013. “Deepweb and Cybercrime: It’s Not All About TOR.” Trend Micro Research Paper. October. 
2. Coughlin, Con. 2014. “How Social Media Is Helping Islamic State to Spread Its Poison.” The Telegraph, November 5. 
3. Dahl, Julia. 2014. “Identity Theft Ensnares Millions while the Law Plays Catch Up.” CBS News, July 14. 
4. Dean, Matt. 2014. “Digital Currencies Fueling Crime on the Dark Side of the Internet.” Fox Business, December 18.
5. Falconer, Joel. 2012. “A Journey into the Dark Corners of the Deep Web.” The Next Web, October 8. 
6. Gehl, Robert W. 2014. “Power/Freedom on the Dark Web: A Digital Ethnography of the Dark Web Social Network.” New Media & Society, October 15. http://nms.sagepub.com/content/early/2014/ 10/16/1461444814554900.full#ref-38.

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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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The Ghibli trend has been in the news for the past couple of weeks for multiple reasons, be it good or bad. The nostalgia that everyone has for the art form has made people turn a blind eye to what the trend means to the artists who painstakingly create the art. The open-source platforms may be trained on artistic material without the artist's ‘explicit permission’ making it so that the rights of the artists are downgraded. The artistic community has reached a level where they are questioning their ability to create, which can be recreated by this software in a couple of seconds and without any thought as to what it is doing. OpenAI’s update on ChatGPT makes it simple for users to create illustrations that are like the style created by Hayao Miyazaki and made into anything from personal pictures to movie scenes and making them into Ghibli-style art. The updates in AI to generate art, including Ghibli-style, may raise critical questions about artistic integrity, intellectual property, and data privacy risks.

AI and the Democratization of Creativity

AI-powered tools have lowered barriers and enable more people to engage with artistic expression. AI allows people to create appealing content in the form of art regardless of their artistic capabilities. The update of ChatGPT has made it so that art has been democratized, and the abilities of the user don't matter. It makes art accessible, efficient and a creative experiment to many. 

Unfortunately, these developments also pose challenges for the original artistry and the labour of human creators. The concern doesn't just stop at AI replacing artists, but also about the potential misuse it can lead to. This includes unauthorized replication of distinct styles or deepfake applications. When it is used ethically, AI can enhance artistic processes. It can assist with repetitive tasks, improving efficiency, and enabling creative experimentation. 

However, its ability to mimic existing styles raises concerns. The potential that AI-generated content has could lead to a devaluation of human artists' work, potential copyright issues, and even data privacy risks. Unauthorized training of AI models that create art can be exploited for misinformation and deepfakes, making human oversight essential. Few artists believe that AI artworks are disrupting the accepted norms of the art world.  Additionally, AI can misinterpret prompts, producing distorted or unethical imagery that contradicts artistic intent and cultural values, highlighting the critical need for human oversight.

The Ethical and Legal Dilemmas

The main dilemma that surrounds trends such as the Ghibli trend is whether it compromises human efforts by blurring the line between inspiration and infringement of artistic freedom. Further, an issue that is not considered by most users is whether the personal content (personal pictures in this case) uploaded on AI models is posing a risk to their privacy. This leads to the issue where the potential misuse of AI-generated content can be used to spread misinformation through misleading or inappropriate visuals. 

The negative effects can only be balanced if a policy framework is created that can ensure the fair use of AI in Art. Further, this should ensure that the training of AI models is done in a manner that is fair to the artists who are the original creators of a style. Human oversight is needed to moderate the AI-generated content.  This oversight can be created by creating ethical AI usage guidelines for platforms that host AI-generated art. 

Conclusion: What Can Potentially Be Done?

AI is not a replacement for human effort, it is to ease human effort. We need to promote a balanced AI approach that protects the integrity of artists and, at the same time, continues to foster innovation. And finally, strengthening copyright laws to address AI-generated content. Labelling AI content and ensuring that this content is disclosed as AI-generated is the first step. Furthermore, there should be fair compensation made to the human artists based on whose work the AI model is trained. There is an increasing need to create global AI ethics guidelines to ensure that there is transparency, ethical use and human oversight in AI-driven art. The need of the hour is that industries should work collaboratively with regulators to ensure that there is responsible use of AI. 

References

• https://medium.com/@haileyq/my-experience-with-studio-ghibli-style-ai-art-ethical-debates-in-the-gpt-4o-era-b84e5a24cb60 
• https://www.bbc.com/future/article/20241018-ai-art-the-end-of-creativity-or-a-new-movement 

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