#FactCheck - Viral Videos of Mutated Animals Debunked as 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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The Rise of Tech Use Amongst Children

Technology today has become an invaluable resource for children, as a means to research issues, be informed about events, gather data, and share views and experiences with others. Technology is no longer limited to certain age groups or professions: children today are using it for learning & entertainment, engaging with their friends, online games and much more. With increased digital access, children are also exposed to online mis/disinformation and other forms of cyber crimes, far more than their parents, caregivers, and educators were in their childhood or are, even in the present. Children are particularly vulnerable to mis/disinformation due to their still-evolving maturity and cognitive capacities. The innocence of the youth is a major cause for concern when it comes to digital access because children simply do not possess the discernment and caution required to be able to navigate the Internet safely. They are active users of online resources and their presence on social media is an important factor of social, political and civic engagement but young people and children often lack the cognitive and emotional capacity needed to distinguish between reliable and unreliable information. As a result, they can be targets of mis/disinformation. ‘A UNICEF survey in 10 countries’[1] reveals that up to three-quarters of children reported feeling unable to judge the veracity of the information they encounter online.

Social media has become a crucial part of children's lives, with them spending a significant time on digital platforms such as Youtube, Facebook, Instagram and more. All these platforms act as source of news, educational content, entertainment, peer communication and more. These platforms host a variety of different kinds of content across a diverse range of subject matters, and each platform’s content and privacy policies are different. Despite age restrictions under the Children's Online Privacy Protection Act (COPPA), and other applicable laws, it is easy for children to falsify their birth date or use their parent's accounts to access content which might not be age-appropriate. 

The Impact of Misinformation on Children

In virtual settings, inaccurate information can come in the form of text, images, or videos shared through traditional and social media channels. In this age, online misinformation is a significant cause for concern, especially with children, because it  can cause anxiety, damage self-esteem, shape beliefs, and skewing their worldview/viewpoints. It can distort children's understanding of reality, hinder their critical thinking skills, and cause confusion and cognitive dissonance. The growing infodemic can even cause an overdose of information. Misinformation can also influence children's social interactions, leading to misunderstandings, conflicts, and mistrust among peers. Children from low literacy backgrounds are more susceptible to fabricated content. Mis/disinformation can exacerbate social divisions amongst peers and lead to unwanted behavioural patterns. Sometimes even children themselves can unwittingly spread/share misinformation. Therefore, it is important to educate & empower children to build cognitive defenses against online misinformation risks, promote media literacy skills, and equip them with the necessary tools to critically evaluate online information.

CyberPeace Policy Wing Recommendations

1. Role of Parents & Educators to Build Cognitive Defenses

One way parents shape their children's values, beliefs and actions is through modelling. Children observe how their parents use technology, handle challenging situations, and make decisions. For example, parents who demonstrate honesty, encourage healthy use of social media and show kindness and empathy are more likely to raise children who hold these qualities in high regard. Hence parents/educators play an important role in shaping the minds of their young charges and their behaviours, whether in offline or online settings. It is important for parents/educators to realise that they must pay close attention to how online content consumption is impacting the cognitive skills of their child. Parents/educators should educate children about authentic sources of information. This involves instructing children on the importance of using reliable, credible sources to utilise while researching on any topic of study or otherwise, and using verification mechanisms to test suspected information., This may sound like a challenging ideal to meet, but the earlier we teach children about Prebunking and Debunking strategies and the ability to differentiate between fact and misleading information, the sooner we can help them build cognitive defenses so that they may use the Internet safely. Hence it becomes paramount important for parents/educators to require children to question the validity of information, verify sources, and critically analyze content. Developing these skills is essential for navigating the digital world effectively and making informed decisions.

2. The Role of Tech & Social Media Companies to Fortify their Steps in Countering Misinformation

Is worth noting that all major tech/social media companies have privacy policies in place to discourage any spread of harmful content or misinformation. Social media platforms have already initiated efforts to counter misinformation by introducing new features such as adding context to content, labelling content, AI watermarks and collaboration with civil society organisations to counter the widespread online misinformation. In light of this, social media platforms must prioritise both the designing and the practical implementation aspects of policy development and deployment to counter misinformation strictly. These strategies can be further improved upon  through government support and regulatory controls. It is recommended that social media platforms must further increase their efforts to counter increasing spread of online mis/disinformation and apply advanced techniques to counter misinformation including filtering, automated removal, detection and prevention, watermarking, increasing reporting mechanisms, providing context to suspected content, and promoting authenticated/reliable sources of information.

Social media platforms should consider developing children-specific help centres that host educational content in attractive, easy-to-understand formats so that children can learn about misinformation risks and tactics, how to spot red flags and how to increase their information literacy and protect themselves and their peers. Age-appropriate, attractive and simple content can go a long way towards fortifying young minds and making them aware and alert without creating fear. 

3. Laws and Regulations

It is important that the government and the social media platforms work in sync to counteract misinformation. The government must consult with the concerned platforms and enact rules and regulations which strengthen the platform’s age verification mechanisms at the sign up/ account creation stage whilst also respecting user privacy. Content moderation, removal of harmful content, and strengthening reporting mechanisms all are important factors which must be prioritised at both the regulatory level and the platform operational level. Additionally, in order to promote healthy and responsible use of technology by children, the government should collaborate with other institutions to design information literacy programs at the school level. The government must make it a key priority to work with civil society organisations and expert groups that run programs to fight misinformation and co-create  a safe cyberspace for everyone, including children. 

4. Expert Organisations and Civil Societies

Cybersecurity experts and civil society organisations possess the unique blend of large scale impact potential and technical expertise. We have the ability to educate and empower huge numbers, along with the skills and policy acumen needed to be able to not just make people aware of the problem but also teach them how to solve it for themselves. True, sustainable solutions to any social concern only come about when capacity-building and empowerment are at the heart of the initiative. Programs that prioritise resilience, teach Prebunking and Debunking and are able to understand the unique concerns, needs and abilities of children and design solutions accordingly are the best suited to implement the administration’s mission to create a safe digital society. 

Final Words 

Online misinformation significantly impacts child development and can hinder their cognitive abilities, color their viewpoints, and cause confusion and mistrust. It is important that children are taught not just how to use technology but how to use it responsibly and positively. This education can begin at a very young age and parents, guardians and educators can connect with CyberPeace and other similar initiatives on how to define age-appropriate learning milestones. Together, we can not only empower children to be safe today, but also help them develop into netizens who make the world even safer for others tomorrow. 

References:

• [1] Digital misinformation / disinformation and children
• [2] Children's Privacy | Federal Trade Commission

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Executive Summary:

Recently, a viral social media post alleged that the Delhi Metro Rail Corporation Ltd. (DMRC) had increased ticket prices following the BJP’s victory in the Delhi Legislative Assembly elections. After thorough research and verification, we have found this claim to be misleading and entirely baseless. Authorities have asserted that no fare hike has been declared.

Claim:

Viral social media posts have claimed that the Delhi Metro Rail Corporation Ltd. (DMRC) increased metro fares following the BJP's victory in the Delhi Legislative Assembly elections.

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Fact Check:

After thorough research, we conclude that the claims regarding a fare hike by the Delhi Metro Rail Corporation Ltd. (DMRC) following the BJP’s victory in the Delhi Legislative Assembly elections are misleading. Our review of DMRC’s official website and social media handles found no mention of any fare increase.Furthermore, the official X (formerly Twitter) handle of DMRC has also clarified that no such price hike has been announced. We urge the public to rely on verified sources for accurate information and refrain from spreading misinformation.

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Conclusion:

Upon examining the alleged fare hike, it is evident that the increase pertains to Bengaluru, not Delhi. To verify this, we reviewed the official website of Bangalore Metro Rail Corporation Limited (BMRCL) and cross-checked the information with appropriate evidence, including relevant images. Our findings confirm that no fare hike has been announced by the Delhi Metro Rail Corporation Ltd. (DMRC).

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• Claim: Delhi Metro price Hike after BJP’s victory in election 
• Claimed On: X (Formerly Known As Twitter)
• Fact Check: False and Misleading