Law in 30 Seconds? The Rise of Influencer Hype and Legal Misinformation

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.

The Expanding Governance Challenge of Artificial Intelligence
‍

Artificial intelligence (AI) systems are increasingly embedded in economic and social infrastructure. They are being adopted in financial services, healthcare diagnostics, hiring systems, and public administration. But while these systems improve efficiency and decision-making, they also introduce new forms of technological risk. 

Unlike conventional software, AI systems learn patterns from data and continue to evolve as they run. This poses governance issues since risks can arise throughout the AI life cycle, whether at the coding level or in their implementation.

The latest regulatory frameworks, such as the European Union’s AI Act (EU AI Act) and the UNESCO Recommendation on the Ethics of Artificial Intelligence, note that responsible AI governance depends on the realisation of where risks emerge across the development process. 

This article maps the AI system lifecycle, identifies the risks that emerge at each stage and evaluates the policy tools used to mitigate them using the lifecycle framework developed by the Organisation of Economic Co-operation and Development (OECD).
‍

The Lifecycle of an AI System
‍

AI systems are developed through a structured process that includes problem definition, dataset collection and preparation, model development, testing and validation, deployment, and monitoring.

The OECD conceptualises this development process as the AI system lifecycle. Each stage entails various technical and administrative procedures, since choices made during these stages will dictate the goals and limits of an AI system. Further, the quality and representativeness of training sets will have a strong effect on the behaviour of models after implementation.

Since this is an iterative and not a linear procedure, risks can be introduced at each stage of the AI lifecycle. New data can be retrained into different models, and systems are regularly updated once they have been deployed, to address performance degradation, model errors, or unintended outputs. This iterative process means governance must address risks across the entire lifecycle, not just at deployment.

‍

Where AI Risks Emerge
‍

AI risks usually emerge earlier in the development process, especially in the phases when system objectives are formulated and training data are chosen. The EU AI Act and the UNESCO Recommendation on the Ethics of AI outline the following risks:  bias and discrimination, privacy and data security violations, the absence of transparency in automated decision-making, and risks to fundamental rights.

‍

AI Governance Risk Landscape: Core Risk Categories Under International Frameworks
‍

 Risk categories jointly identified by the EU AI Act and UNESCO Recommendation on the Ethics of Artificial Intelligence

Outlining the risks throughout the AI lifecycle helps understand the areas where governance interventions are most necessary. For example, discriminatory outcomes often result from biased or unrepresentative training data, while safety failures are typically linked to inadequate testing before deployment. Risks such as misinformation arise post the development process, when generative AI systems are deployed at scale on digital platforms. 
‍

AI System Lifecycle: Key Risks at Each Stage
‍

Risks identified per the EU AI Act and UNESCO Recommendation on the Ethics of AI

Understanding where risks emerge across the lifecycle explains why governance frameworks classify AI systems by risk and apply oversight at multiple stages.
‍

Policy Tools for Mitigating AI Risks
‍

Governments and international organisations have developed regulatory tools to help mitigate AI risks in the lifecycle. These tools are meant to make sure that AI technologies are identified as up to standard in safety, accountability and fairness prior to and after deployment. 

For example, the OECD AI Policy Observatory recommends that governments adopt policy instruments such as risk evaluations, algorithmic auditing necessities, regulatory sandboxes, and transparency necessities of AI systems. The European Union’s Artificial Intelligence Act (AI Act) is one of the most comprehensive systems of governance that introduces a risk-oriented regulation strategy. It mandates adherence to requirements concerning data governance, documentation, human oversight, and robustness, and cybersecurity. Such requirements bring regulatory checkpoints to the lifecycle of AI systems.

Mapping these policy tools across the lifecycle illustrates how governance mechanisms can intervene at different stages of AI development. 
‍

‍

Governance Overlay: Policy Interventions Across the AI Lifecycle
‍

Regulatory tools mapped at each stage of AI development per the EU AI Act and UNESCO Recommendation on the Ethics of AI

Several policy tools are directed at the risks that occur in the pre-developmental stages. In one example, algorithmic impact assessment has been applied in various jurisdictions to measure the possible consequences of automated decision systems on society before implementation. On the same note, the requirements of dataset documentation, including dataset transparency requirements and model cards, are aimed at enhancing accountability during the training and development stages of the AI systems. Therefore, lifecycle-based policy design allows regulators to intervene before harmful outcomes occur, rather than responding only after AI systems have caused damage in real-world environments.
‍

The Policy Gap in AI Governance
‍

The misalignment between risks and governance tools across the AI lifecycle indicates a critical structural gap in existing regulations. Numerous governance processes become activated after AI systems are classified as “high risk” or after they are implemented in the real world. But the most serious sources of damage have their roots in earlier stages of the development procedure.

An example is that prejudiced or unbalanced training data is almost inevitably a source of discriminative results in automated decision systems. When these types of models are applied in areas like staffing, credit rating, or in providing services to the public, such biases can quickly spread to large populations and undermine democratic rights. In the same way, the lack of transparency in model design might result in the fact that the regulator or individuals are affected by the decision-making process. This reflects a broader timing gap in AI governance, where risks originate during design and development, but regulatory intervention typically occurs only after deployment. 

Analysis
‍

1. Key risks originate before deployment: As depicted in the lifecycle mapping, the data collection and model development phase presents several significant governance risks as opposed to the deployment phase. Structural issues can be entrenched within AI systems even before they are deployed in practice due to bias in data sets, incomplete reporting of training sets, and obscured network designs.

2. Data governance is a primary point of vulnerability: Most of the instances of algorithmic discrimination listed above are associated with training material that is not representative of some population groups or is historical. Since machine learning models are optimisations of patterns that exist in datasets, these biases can be carried through the whole lifecycle and reproduced after deployment. 

3. Regulatory approaches remain mismatched across jurisdictions: Different countries adopt varying approaches to AI governance, ranging from risk-based frameworks such as the EU AI Act to more sector-specific or voluntary guidelines in other regions. This divergence creates inconsistencies in safety, accountability, and enforcement standards, allowing risks to persist across borders and potentially undermining the protection of users in globally deployed AI systems. 

4. Governance interventions remain uneven across the lifecycle: Whereas the various regulatory instruments aim at deployment and monitoring, fewer instruments systematically tackle the risks that are posed by the previous design and development phases.
‍

Recommendations
‍

1. Introduce mandatory lifecycle risk assessments: The regulatory systems need to demand systemic risk evaluation at the beginning of AI development, especially at the problem design and dataset selection phases. This would assist in detecting possible harmful applications in advance, before systems are constructed and installed.

2. Strengthen dataset governance standards: Training datasets must be supplemented with documentation as to their provenance, composition and limitations. Standardised documentation frameworks of data sets can assist in the discovery by regulators and auditors of the potential sources of bias or privacy threats.

3. Expand independent algorithmic auditing: AI systems can be assessed by regular third-party audits based on fairness, strength, and security weaknesses. The auditing mechanisms especially apply to high-risk systems employed in employment, finance or the public services.

4. Integrate continuous monitoring requirements: AI systems may be monitored regularly after implementation to identify model drift, unforeseen consequences, or abuse. Reporting systems can facilitate the process where the regulators can see the emerging risks and modify the governance systems.
‍

Conclusion - The Need for Global AI Governance
‍

Despite growing regulatory attention, global air governance remains fragmented. Different jurisdictions adopt varying approaches to risk classification, oversight, and enforcement, leading to inconsistencies in safety and accountability standards. Given that AI systems are often developed, deployed, and used across borders, this lack of coordination allows risks to persist beyond national regulatory frameworks.

Addressing these challenges requires a shift towards greater international cooperation and lifecycle-based governance. Developing shared standards, improving cross-border regulatory alignment, and embedding oversight across all stages of AI development will be essential to ensuring that AI systems are safe, transparent, and accountable in a globally interconnected environment.
‍

References
‍

• OECD AI lifecycle
• OECD AI system lifecycle description
• OECD AI governance lifecycle framework
• EU AI Act overview
• EU AI Act risk categories
• UNESCO Recommendation on the Ethics of AI
• AI governance lifecycle analysis
• OECD AI policy tools database

‍

Executive Summary:

A photo that has gone viral on social media alleges that the Indian company Patanjali founded by Yoga Guru Baba Ramdev is selling a product called “Recipe Mix for Beef Biryani”. The image incorporates Ramdev’s name in its promotional package. However, upon looking into the matter, CyberPeace Research Team revealed that the viral image is not genuine. The original image was altered and it has been wrongly claimed which does not even exist. Patanjali is an Indian brand designed for vegetarians and an intervention of Ayurveda. For that reason, the image in context is fake and misleading.

‍

Claims:

An image circulating on social media shows Patanjali selling "Recipe Mix for Beef Biryani”.

‍

Fact Check:

Upon receiving the viral image, the CyberPeace Research Team immediately conducted an in-depth investigation. A reverse image search revealed that the viral image was taken from an unrelated context and digitally altered to be associated with the fabricated packaging of "National Recipe Mix for Biryani". 

The analysis of the image confirmed signs of manipulation. Patanjali, a well-established Indian brand known for its vegetarian products, has no record of producing or promoting a product called “Recipe mix for Beef Biryani”. We also found a similar image with the product specified as “National Biryani” in another online store.

Comparing both photos, we found that there are several differences.

Further examination of Patanjali's product catalog and public information verified that this viral image is part of a deliberate attempt to spread misinformation, likely to damage the reputation of the brand and its founder. The entire claim is based on a falsified image aimed at provoking controversy, and therefore, is categorically false.

Conclusions:

The viral image associating Patanjali and Baba Ramdev with "Recipe mix for Beef Biryani" is entirely fake. This image was deliberately manipulated to spread false information and damage the brand’s reputation. Social media users are encouraged to fact-check before sharing any such claims, as the spread of misinformation can have significant consequences. The CyberPeace Research Team emphasizes the importance of verifying information before circulating it to avoid spreading false narratives.

• Claim: Patanjali and Baba Ramdev endorse "Recipe mix for Beef Biryani"
• Claimed on: X
• Fact Check: Fake & Misleading

‍