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The Expanding Governance Challenge of Artificial Intelligence
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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).
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The Lifecycle of an AI System
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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.

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Where AI Risks Emerge
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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.

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AI Governance Risk Landscape: Core Risk Categories Under International Frameworks
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 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. 
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AI System Lifecycle: Key Risks at Each Stage
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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.
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Policy Tools for Mitigating AI Risks
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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. 
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Governance Overlay: Policy Interventions Across the AI Lifecycle
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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.
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The Policy Gap in AI Governance
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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
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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.
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Recommendations
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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.
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Conclusion - The Need for Global AI Governance
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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.
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References
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• 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

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Introduction

Social media has emerged as a leading source of communication and information; its relevance cannot be ignored during natural disasters since it is relied upon by governments and disaster relief organisations as a tool for disseminating aid and relief-related resources and communications instantly. During disaster times, social media has emerged as a primary source for affected populations to access information on relief resources; community forums offering aid resources and official government channels for government aid have enabled efficient and timely administration of relief initiatives.

However, given the nature of social media, misinformation risks during natural disasters has also emerged as a primary concern that severely hampers aid administration during natural disasters. The disaster-disinformation network offers some sensationalised influential campaigns against communities at their most vulnerable. Victims who seek reliable resources during natural calamities often reach out to inhospitable campaigns and may experience delayed or lack of access to necessary healthcare, significantly impacting their recovery and survival. This delay can lead to worsening medical conditions and an increased death toll among those affected by the disaster. Victims may lack clear information on the appropriate agencies to seek assistance from, causing confusion and delays in receiving help. 

Misinformation Threat Landscape during Natural Disaster

During the 2018 floods in Kerala, it was noted that a fake video on water leakage from the Mullaperyar Dam created panic among the citizens and negatively impacted the rescue operations. Similarly, in 2017, reports emerged claiming that Hurricane Irma had caused sharks to be displaced onto a Florida highway. Similar stories, accompanied by the same image, resurfaced following Hurricanes Harvey and Florence. The disaster-affected nation may face international criticism and fail to receive necessary support due to its perceived inability to manage the crisis effectively. This lack of confidence from the global community can further exacerbate the challenges faced by the nation, leaving it more vulnerable and isolated in its time of need.

The spread of misinformation through social media severely hinders the administration of aid and relief operations during natural disasters since it hinders first responders' efforts to counteract and reduce the spread of misinformation, rumours, and false information and declines public trust in government, media, and non-governmental organisations (NGOs), who are often the first point of contact for both victims and officials due to their familiarity with the region and the community. In Moldova, it was noted that foreign influence has exploited the ongoing drought to create divisions between the semi-autonomous regions of Transnistria and Gagauzia and the central government in Chisinau. News coverage critical of the government leverages economic and energy insecurities to incite civil unrest in this already unstable region. Additionally, First responders may struggle to locate victims and assist them to safety, complicating rescue operations. The inability to efficiently find and evacuate those in need can result in prolonged exposure to dangerous conditions and a higher risk of injury or death.

Further, international aid from other countries could be impeded, affecting the overall relief effort. Without timely and coordinated support from the global community, the disaster response may be insufficient, leaving many needs unmet. Further, misinformation also impedes military, reducing the effectiveness of rescue and relief operations. Military assistance often plays a crucial role in disaster response, and any delays can hinder efforts to provide immediate and large-scale aid.

Misinformation also creates problems of allocation of relief resources to unaffected areas which resultantly impacts aid processes for regions in actual need. Following the April 2015 earthquake in Nepal, a Facebook post claimed that 300 houses in Dhading needed aid. Shared over 1,000 times, it reached around 350,000 people within 48 hours. The originator aimed to seek help for Ward #4’s villagers via social media. Given the average Facebook user has 350 contacts, the message was widely viewed. However, the need had already been reported on quakemap.org, a crisis-mapping database managed by Kathmandu Living Labs, a week earlier. Helping Hands, a humanitarian group was notified on May 7, and by May 11, Ward #4 received essential food and shelter. The re-sharing and sensationalisation of outdated information could have wasted relief efforts since critical resources would have been redirected to a region that had already been secured.

Policy Recommendations

Perhaps the most important step in combating misinformation during natural disasters is the increasing public education and the rapid, widespread dissemination of early warnings. This was best witnessed in the November 1970 tropical cyclone in southeastern Bangladesh, combined with a high tide, struck southeastern Bangladesh, leaving more than 300,000 people dead and 1.3 million homeless. In May 1985, when a comparable cyclone and storm surge hit the same area, local dissemination of disaster warnings was much improved and the people were better prepared to respond to them. The loss of life, while still high (at about 10,000), the numbers were about 3% of that in 1970. On a similar note, when a devastating cyclone struck the same area of Bangladesh in May 1994, fewer than 1,000 people died. In India, the 1977 cyclone in Andra Pradesh killed 10,000 people, but a similar storm in the same area 13 years later killed only 910. The dramatic difference in mortalities was owed to a new early-warning system connected with radio stations to alert people in low-lying areas. 

Additionally, location-based filtering for monitoring social media during disasters is considered as another best practice to curb misinformation. However, agencies should be aware that this method may miss local information from devices without geolocation enabled. A 2012 Georgia Tech study found that less than 1.4 percent of Twitter content is geolocated. Additionally, a study by Humanity Road and Arizona State University on Hurricane Sandy data indicated a significant decline in geolocation data during weather events.

Alternatively, Publish frequent updates to promote transparency and control the message. In emergency management and disaster recovery, digital volunteers—trusted agents who provide online support—can assist overwhelmed on-site personnel by managing the vast volume of social media data. Trained digital volunteers help direct affected individuals to critical resources and disseminate reliable information. 

Enhancing the quality of communication requires double-verifying information to eliminate ambiguity and reduce the impact of misinformation, rumors, and false information must also be emphasised. This approach helps prevent alert fatigue and "cry wolf" scenarios by ensuring that only accurate, relevant information is disseminated. Prioritizing ground truth over assumptions and swiftly releasing verified information or acknowledging the situation can bolster an agency's credibility. This credibility allows the agency to collaborate effectively with truth amplifiers. Prebunking and Debunking methods are also effective way to counter misinformation and build cognitive defenses to recognise red flags. Additionally, evaluating the relevance of various social media information is crucial for maintaining clear and effective communication.

References

1. https://www.nature.com/articles/s41598-023-40399-9#:~:text=Moreover%2C%20misinformation%20can%20create%20unnecessary,impacting%20the%20rescue%20operations29. 
2. https://www.redcross.ca/blog/2023/5/why-misinformation-is-dangerous-especially-during-disasters
3. https://www.soas.ac.uk/about/blog/disinformation-during-natural-disasters-emerging-vulnerability‍
4. https://www.dhs.gov/sites/default/files/publications/SMWG_Countering-False-Info-Social-M dia-Disasters-Emergencies_Mar2018-508.pdf

Introduction

Union Minister of State for Electronics and IT, Rajeev Chandrasekhar, announced that rules for the Digital Personal Data Protection (DPDP) Act are expected to be released by the end of January. The rules will be subject to a month-long consultation process, but their notification may be delayed until after the general elections in April-May 2024. Chandrasekhar mentioned changes to the current IT regulations would be made in the next few days to address the problem of deepfakes on social networking sites.

The government has observed a varied response from platforms regarding advisory measures on deepfakes, leading to the decision to enforce more specific rules. During the Digital India Dialogue, platforms were made aware of existing provisions and the consequences of non-compliance. An advisory was issued, and new amended IT rules will be released if satisfaction with compliance is not achieved.

When Sachin Tendulkar reported a deepfake on a site where he was seen endorsing a gaming application, it raised concerns about the exploitation of deepfakes. Tendulkar urged the reporting of such incidents and underlined the need for social media companies to be watchful, receptive to grievances, and quick to address disinformation and deepfakes.

The DPDP Act, 2023

The Digital Personal Data Protection Act (DPDP) 2023 is a brand-new framework for digital personal data protection that aims to protect individuals' digital personal data. The act ensures compliance by the platforms collecting personal data. The act aims to provide consent-based data collection techniques. DPDP Act 2023 is an important step toward protecting individual privacy. The Act, which requires express consent for the acquisition, administration, and processing of personal data, seeks to guarantee that organisations follow the stated objective for which user consent was granted. This proactive strategy coincides with global data protection trends and demonstrates India's commitment to safeguarding user information in the digital era.

Amendments to IT rules

Minister Chandrasekhar declared that existing IT regulations would be amended in order to combat the rising problem of deepfakes and disinformation on social media platforms. These adjustments, which will be published over the next few days, are primarily aimed at countering widespread of false information and deepfake. The decision follows a range of responses from platforms to deepfake recommendations made during Digital India Dialogues.

The government's stance: blocking non-compliant platforms

Minister Chandrasekhar reaffirmed the government's commitment to enforcing the updated guidelines. If platforms fail to follow compliance, the government may consider banning them. This severe position demonstrates the government's commitment to safeguarding Indian residents from the possible harm caused by false information.

Empowering Users with Education and Awareness

In addition to the upcoming DPDP Act Rules/recommendations and IT regulation changes, the government recognises the critical role that user education plays in establishing a robust digital environment. Minister Rajeev Chandrasekhar emphasised the necessity for comprehensive awareness programs to educate individuals about their digital rights and the need to protect personal information.

These instructional programs seek to equip users to make informed decisions about giving consent to their data. By developing a culture of digital literacy, the government hopes to guarantee that citizens have the information to safeguard themselves in an increasingly linked digital environment.

Balancing Innovation with User Protection

As India continues to explore its digital frontier, the junction of technology innovation and user safety remains a difficult balance. The upcoming Rules on the DPDP Act and modifications to existing IT rules represent the government's proactive efforts to build a strong framework that supports innovation while protecting user privacy and combating disinformation. Recognising the changing nature of the digital world, the government is actively participating in continuing discussions with stakeholders such as industry professionals, academia, and civil society. These conversations promote a collaborative approach to policy creation, ensuring that legislation is adaptable to the changing nature of cyber risks and technology breakthroughs. Such inclusive talks demonstrate the government's dedication to transparent and participatory governance, in which many viewpoints contribute to the creation of effective and nuanced policy. These advances reflect an important milestone in India's digital journey, as the country prepares to set a good example by creating responsible and safe digital ecosystems for its residents.

Reference : 

• https://economictimes.indiatimes.com/tech/technology/govt-may-release-personal-data-bill-rules-in-a-fortnight/articleshow/106162669.cms?from=mdr
• https://www.business-standard.com/india-news/dpdp-rules-expected-to-be-released-by-end-of-the-month-mos-chandrasekhar-124011600679_1.html 

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