#FactCheck - Viral Video of Burning Aircraft Falsely Linked to UAE, Found to Be AI-Generated

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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18th November 2022 CyberPeace Foundation in association with Universal Acceptance has successfully conducted the workshop on Universal Acceptance and Multilingual Internet for the students and faculties of Royal Global University under CyberPeace Center of Excellence (CCoE). CyberPeace Foundation has always been engaged towards the aim of spreading awareness regarding the various developments, avenues, opportunities and threats regarding cyberspace. The same has  been the keen principle of the CyberPeace Centre of Excellence setup in collaboration with various esteemed educational institutes. We at CyberPeace Foundation would like to take the collaborations and our efforts to a new height of knowledge and awareness by proposing a workshop on UNIVERSAL ACCEPTANCE AND MULTILINGUAL INTERNET. This workshop was instrumental in providing the academia and research community a wholesome outlook towards the multilingual spectrum of internet including Internationalized domain names and email address Internationalization.

‍Date –18th November 2022

‍Time – 10:00 AM to 12:00 PM

‍Duration – 2 hours

‍Mode - Online

‍Audience – Academia and Research Community

‍Participants Joined- 130

‍Crowd Classification - Engineering students (1st and 4th year, all streams) and Faculties members

‍Organizer : Mr. Harish Chowdhary : UA Ambassador Moderator: Ms. Pooja Tomar, Project coordinator cum trainer

‍GuestSpeakers:Mr. Abdalmonem Galila, Abdalmonem: Vice Chair , Universal Acceptance Steering Group (UASG) ,Mr. Mahesh D Kulkarni: Director, Evaris Systems and Former Senior Director, CDAC, Government of India, Mr. Akshat Joshi, Founder Think Trans First session was delivered by Mr. Abdalmonem Galila, Abdalmonem: Vice Chair , Universal Acceptance Steering Group (UASG) “Universal Acceptance( UA) and why UA matters?”

• What is universal acceptance?
• UA is cornerstone to a digitally inclusive internet by ensuring all domain names and email addresses in all languages, script and character length.
• Achieving UA ensures that every person has the ability to navigate the internet.
• Different UA issues were also discussed and explained.
• Tagated systems by the UA and implication were discussed in detail.

Second Session was delivered by Mr. Akshat Joshi, Founder Think Trans on “Universal Acceptance to the IDNsand the economic Landscape”

• What is Universal Acceptance?
• The internet has had standards that allow people to use domain names and email addresses in their native scripts. Software developers need to bring their applications up-to-date so that consumers can use their chosen identity.
• A typical problem is that an IDN email address is not recognised by a website form as a valid email address.
• The importance of adopting IDNs z Enable citizens to use their own identity online (correct spelling, native language) z Relates to language, culture and content z Promotes local and regional content z Allows businesses and politicians to better target their messages.

Third session was delivered by Mr. Mahesh D Kulkarni, ES Director Evaris on the topic of “IDNs in Indian languages perspective- challenges and solutions”.

• The multilingual diversity of India was focused on and its impact.
• Most students were not aware of what Unicode, IDNS is and their usage.
• Students were briefed by giving real time examples on IDN, Domain name implementation using local language.
• In depth knowledge of and practical exposure of Universal Acceptance and Multilingual Internet has been served to the students.
• Tools and Resources for Domain Name and Domain Languages were explained.
• Languages nuances of Multilingual diversity of India explained with real time facts and figures.
• Given the idea of IDN Email,Homograph attack,Homographic variant with proper real time examples.
• Explained about the security threats and IDNA protocols.
• Given the explanation on ABNF.
• Explained the stages of Universal Acceptance.

Introduction

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Law grows by confronting its absences, it heals through its own gaps. States often find themselves navigating a shared frontier without a mutual guide or lines of law in an era of expanding digital boundaries and growing cyber damages. The United Nations General Assembly ratified the United Nations Convention against Cybercrime on December 24, 2024, and more than sixty governments were in attendance in the signing ceremony on 24th & 25th October this year, marking a moment of institutional regeneration and global commitment. 

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A new Lexicon for Global Order

The old liberal order is being strained by growing nationalism, economic fracturing, populism, and great-power competition as often emphasised in the works of scholars like G. John Iken berry and John Mearsheimer. Multilateral arrangements become more brittle in such circumstances. Therefore, the new cybercrimes convention represents not only a legal tool but also a resurgence of international promise, a significant win for collective governance in an uncertain time. It serves as a reminder that institutions can be rebuilt even after they have been damaged. 

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In Discussion: The Fabric of the Digital Polis

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The digital sphere has become a contentious area. On the one hand, the US and its allies support stakeholder governance, robust individual rights, and open data flows. On the other hand, nations like China and Russia describe a “post-liberal cyber order” based on state mediation, heavily regulated flows, and sovereignty. Instead of focusing on ideological dichotomies, India, which is positioned as both a rising power and a voice of the Global South, has offered a viewpoint based on supply-chain security, data localisation, and capacity creation. Thus, rather than being merely a regulation, the treaty arises from a framework of strategic recalibration. 

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What Changed & Why it Matters

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There have been regional cybercrime accords up to this point, such as the Budapest Convention. The goal of this new international convention, which is accessible to all UN members, is to standardise definitions, evidence sharing and investigation instruments. 72 states signed the Hanoi signature event in October, 2025, demonstrating an unparalleled level of scope and determination. In addition to establishing structures for cooperative investigations, extradition, and the sharing of electronic evidence, it requires signatories to criminalise acts such as fraud, unlawful access to systems, data interference, and online child exploitation. 

For the first time, a legally obligatory global architecture aims to harmonise cross-border evidence flows, mutual legal assistance, and national procedural laws. Cybercrime offers genuine promise for community defence at a time when it is no longer incidental but existential, attacks on hospitals, schools and infrastructure are now common, according to the Global Observatory.

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Holding the Line: India’s Deliberate Path in the Age of Cyber Multilateralism 

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India takes a contemplative rather than a reluctant stance towards the UN Cybercrime Treaty. Though it played an active role during the drafting sessions and lent its voice to the shaping of global cyber norms, New Delhi is yet to sign the convention. Subtle but intentional, the reluctance suggests a more comprehensive reflection, an evaluation of how international obligations correspond with domestic constitutional protections, especially the right to privacy upheld by the Supreme Court in Puttaswamy v. UOI (2017). 

Prudence is the reason for this halt. Policy circles speculate that the government is still assessing the treaty’s consequences for national data protection, surveillance regimes, and territorial sovereignty. Officials have not provided explicit justifications for India’s refusal to join. India’s position has frequently been characterised by striking a careful balance between digital sovereignty and taking part in cooperative international regimes. In earlier negotiations, India had even proposed including clauses to penalise “offensive messages” on social media, echoing the erstwhile Section 66A of the IT Act, 2000, but the suggestion found little international traction. 

Advocates for digital rights such as Raman Jit Singh Chima of Access Now have warned that ensuring that the treaty’s implementation upholds constitutional privacy principles may be necessary for India to eventually endorse it. He contends that the treaty’s wording might not entirely meet India’s legal requirements in the absence of such voluntary pledges. 

UN Secretary-General Antonio Guterres praised the agreement as “a powerful, legally binding instrument to strengthen our collective defences against “cybercrime” during its signing in Hanoi. The issue for India is to make sure that multilateral collaboration develops in accordance with constitutional values rather than to reject that vision. Therefore, the path forward is one of assertion rather than absence, careful march towards a cyber future that protects freedom and sovereignty. 

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Sources: 

• https://www.icwa.in/show_content.php?lang=1&level=3&ls_id=12244&lid=7470 
• https://www.medianama.com/2025/10/223-72-nations-un-first-cybercrime-treaty-india-abstains-privacy/ ‍
• https://therecord.media/us-declines-signing-cybercrime-treaty