Taming Bias in AI: Statistical Principles, Fairness-Aware Algorithms and Why It Matters
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.
Why Bias in Training Data Matters
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)
Statistical Principles for Reducing Training Data Bias
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.
Balancing Data Through Re-Sampling and Re-Weighting
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)
Feature Engineering and Data Transformation
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)
Measuring Fairness With Metrics
Statistical fairness measures are used to measure the effectiveness of a model in groups.
Fairness-Aware Algorithms Explained
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.
Pre-Processing Techniques
Fairness-aware pre-processing deals with bias prior to the model consuming the information. This involves the following ways:
- 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)
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.
In-Processing Techniques
The in-processing techniques alter the learning algorithm. These include:
- 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
Post-Processing Techniques
Fairness may be enhanced after training by changing the model outputs. These strategies comprise:
- 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)
Challenges
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)
Why Fairness-Aware Development Matters
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.
Conclusion
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.
References
- 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
The Telecom Regulatory Authority of India (TRAI), on March 13 2023, published a new rule to regulate telemarketing firms. Trai has demonstrated strictness when it comes to bombarding users with intrusive marketing pitches. In a report, TRAI stated that 10-digit mobile numbers could not be utilised for advertising. In reality, different phone numbers are given out for regular calls and telemarketing calls. Hence, it is an appropriate and much-required move in order to suppress and eradicate phishing scammers and secure the Indian Cyber-ecosystem at large.
What are the new rules?
The rules state that now 10-digit unregistered mobile numbers for promotional purposes would be shut down over the following five days. The rule claim that calling from unregistered mobile numbers had been banned was published on February 16. In this case, using 10-digit promotional messages for promotional calling will end within the following five days. This step by TRAI has been seen after nearly 6-8 months of releasing the Telecommunication Bill, 2022, which has focused towards creating a stable Indian Telecom market and reducing the phoney calls/messages by bad actors to reduce cyber crimes like phishing. This is done to distinguish between legitimate and promotional calls. According to certain reports, some telecom firms allegedly break the law by using 10-digit mobile numbers to make unwanted calls and send promotional messages. All telecom service providers must execute the requirements under the recent TRAI directive within five days.
How will the new rules help?
The promotional use of a cellphone number with 10 digits was allowed since the start, however, with the latest NCRB report on cyber crimes and the rising instances and reporting of cyber crimes primarily focused towards frauds related to monetary gains by the bad actors points to the issue of unregulated promotional messages. This move will act as a critical step towards eradicating scammers from the cyber-ecosystem, TRAI has been very critical in understanding the dynamics and shortcomings in the regulation of the telecom spectrum and network in India and has shown keen interest towards suppressing the modes of technology used by the scammers. It is a fact that the invention of the technology does not define its use, the policy of the technology does, hence it is important to draft ad enact policies which better regulate the existing and emerging technologies.
What to avoid?
In pursuance of the rules enacted by TRAI, the business owners involved in promotional services through 10-digit numbers will have to follow these steps-
- It is against the law to utilise a 10-digit cellphone number for promotional calls.
- You should stop doing so right now.
- Your mobile number will be blocked in the following five days if not.
- Users employed by telemarketing firms are encouraged to refrain from using the system in such circumstances.
- Those working for telemarketing firms are encouraged not to call from their mobile numbers.
- Users should phone the company’s registered mobile number for promotional purposes.
Conclusion
The Indian netizen has been exposed to the technology a little later than the western world. However, this changed drastically during the Covid-19 pandemic as the internet and technology penetration rates increased exponentially in just a couple of months. Although this has been used as an advantage by the bad actors, it was pertinent for the government and its institutions to take an effective and efficient step to safeguard the people from financial fraud. Although these frauds occur in high numbers due to a lack of knowledge and awareness, we need to work on preventive solutions rather than precautionary steps and the new rules by TRAI point towards a safe, secured and sustainable future of cyberspace in India.
Introduction
In the vast, cosmic-like expanse of international relations, a sphere marked by the gravitational pull of geopolitical interests, a singular issue has emerged, casting a long shadow over the fabric of Indo-Canadian diplomacy. It is a narrative spun from an intricate loom, interlacing the yarns of espionage and political machinations, shadowboxing with the transient, yet potent, specter of state-sanctioned violence. The recent controversy undulating across this geopolitical landscape owes its origins to the circulation of claims which the Indian Ministry of External Affairs (MEA) vehemently dismisses as a distorted tapestry of misinformation—a phantasmagoric fable divorced from reality.
This maelstrom of contention orbits around the alleged existence of a 'secret memo', a document reportedly dispatched with stealth from the helm of the Indian government to its consulates peppered across the vast North American continent. This mysterious communique, assuming its spectral presence within the report, was described as a directive catalyzing a 'sophisticated crackdown scheme' against specific Sikh diaspora organizations. A proclamation that MEA has repudiated with adamantine certainty, branding the report as a meticulously fabricated fiction.
THE MEA Stance
The official statement from the Indian Ministry of External Affairs (MEA) emerged as a paragon of clarity cutting through the dense fog of accusations, 'We strongly assert that such reports are fake and emphatically concocted. The referenced memo is non-existent. This narrative is a chapter in the protracted saga of a disinformation campaign aimed against India.' The outlet responsible for airing this contentious story, as per the Indian authorities, has a historical penchant for circulating narratives aligned with the interests of rival intelligence agencies, particularly those associated with Pakistani strategic circles—a claim infusing yet another complex layer to the situation at hand.
The report that catapulted itself onto the stage with the force of an untamed tempest insists the 'secret memo' was decked with several names—all belonging to individuals under the hawk-like gaze of Indian intelligence.
The Plague of Disinformation
The profoundly intricate confluence of diplomacy is one that commands grace, poise, and an acute sense of balance—nations effortlessly tip-toeing around sensitivities, proffering reciprocity and an equitable stance within the grand ballroom of international affairs. Hence, when S. Jaishankar, India's Minister of External Affairs, found himself fielding inquiries on the perceived inconsistent treatment afforded to Canada compared to the US—despite similar claims emanating from both—his response was the embodiment of diplomatic discretion: 'As far as Canada is concerned, there was a glaring absence of specific evidence or inputs provided to us. The robust question of equitable treatment between two nations, where only one has furnished substantive input and the other has not, is naturally unmerited.'
The articulation from the Ministry's spokesperson, Arindam Bagchi, further solidified India's stance. He calls into question the credibility of The Intercept—the publication that initially disseminated the report—accusing it of acting as a vessel for 'invented narratives' propagated under the auspices of Pakistani intelligence interests.
Conclusion
In the grand theater of international politics, the distinction between reality and deception is frequently obscured by the heavy drapes of secrecy and diplomatic guile. The persistent denial by the Indian government of any 'secret memo' serves as a critical reminder of the blurred lines between narrative and counter-narrative in the global concert of power and persuasion. As observant spectators within the arena of world politics, we are endowed with the unenviable task of untangling the convoluted web of claims and counterclaims, hoping to uncover the enduring truths that linger therein. In this domain of authentic and imaginary tales, the only unwavering certainty is the persistent rhythm of diplomatic interplay and the subtle shadows it casts upon the international stage. The Ministry of External Affairs fact-checked a claim on the secret memo, rubbishing it as fake and fabricated. The government has said there is a deliberate disinformation campaign that has been on against India.
References
- https://timesofindia.indiatimes.com/india/mea-denies-report-it-issued-secret-memo-on-nijjar-to-missions/articleshow/105884217.cms?from=mdr
- https://www.hindustantimes.com/india-news/india-denies-secret-memo-against-nijjar-report-peddled-by-pak-intelligence-101702229753576.html
Executive Summary:
A viral social media video falsely claims that Meta AI reads all WhatsApp group and individual chats by default, and that enabling “Advanced Chat Privacy” can stop this. On performing reverse image search we found a blog post of WhatsApp which was posted in the month of April 2025 which claims that all personal and group chats remain protected with end to end (E2E) encryption, accessible only to the sender and recipient. Meta AI can interact only with messages explicitly sent to it or tagged with @MetaAI. The “Advanced Chat Privacy” feature is designed to prevent external sharing of chats, not to restrict Meta AI access. Therefore, the viral claim is misleading and factually incorrect, aimed at creating unnecessary fear among users.
Claim:
A viral social media video [archived link] alleges that Meta AI is actively accessing private conversations on WhatsApp, including both group and individual chats, due to the current default settings. The video further claims that users can safeguard their privacy by enabling the “Advanced Chat Privacy” feature, which purportedly prevents such access.
Fact Check:
Upon doing reverse image search from the keyframe of the viral video, we found a WhatsApp blog post from April 2025 that explains new privacy features to help users control their chats and data. It states that Meta AI can only see messages directly sent to it or tagged with @Meta AI. All personal and group chats are secured with end-to-end encryption, so only the sender and receiver can read them. The "Advanced Chat Privacy" setting helps stop chats from being shared outside WhatsApp, like blocking exports or auto-downloads, but it doesn’t affect Meta AI since it’s already blocked from reading chats. This shows the viral claim is false and meant to confuse people.
Conclusion:
The claim that Meta AI is reading WhatsApp Group Chats and that enabling the "Advance Chat Privacy" setting can prevent this is false and misleading. WhatsApp has officially confirmed that Meta AI only accesses messages explicitly shared with it, and all chats remain protected by end-to-end encryption, ensuring privacy. The "Advanced Chat Privacy" setting does not relate to Meta AI access, as it is already restricted by default.
- Claim: Viral social media video claims that WhatsApp Group Chats are being read by Meta AI due to current settings, and enabling the "Advance Chat Privacy" setting can prevent this.
- Claimed On: Social Media
- Fact Check: False and Misleading
